Datadog, Inc. (DDOG) Earnings Call Transcript & Summary

Good afternoon everyone. My name is Yuka Broderick, and I lead Investor Relations here at Datadog. Welcome to all of you here in the theater and everyone joining us online for our 2026 Investor Day. Before we dive in, just a few reminders. During this presentation, we will make forward-looking statements, including statements related to our strategy, product development, market opportunity and financial goals. These statements reflect our views today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially. For a discussion of material risks, please refer to our Form 10-Q for the quarter ended September 30, 2025, and other filings that we may make with the SEC, including our Form 10-K for fiscal 2025. We will also discuss non-GAAP financial measures, which are reconciled to their most directly comparable GAAP financial measures in the appendix to this presentation, which is available at investors.datadoghq.com. All right. Let me briefly run through our agenda for today. In the first half, we will focus on our strategy, platform and product innovation. You will hear from our Co-Founder, CEO, Olivier Pomel; and CTO, Alexis Le-Quoc, followed by product leaders, Yrieix Garnier, Tim Knutson, Michael Whetten; and our Chief Product Officer, Yanbing Li. We will follow that with the Q&A with that group. In the second half, we will discuss our go-to-market, how we deliver value to customers and our financial performance. Our presenters will be CRO, Sean Walters; COO, Adam Blitzer; and CFO, David Obstler. That group will be joined by Olivier. With that, let me turn it over to Olivier to kick things off.

Thank you, Yuka. All right. Hi, everyone. My name is Olivier Pomel. I'm the co-founder and CEO here at Datadog and welcome to the Investor Day. So you're going to hear from several of our leaders today, and I know they are quite excited to present. And so as we discuss what we're doing here at Datadog, our goal is to show you why even after 16 years of building the company, we believe that we're still only just getting started. We're barely scratching the surface of the opportunity. So I'll kick us off with a quick recap of the problem we solve today, how our platform has expanded over time, what we're up to with AI, and I'll give you a sense of the broad direction we're taking over the long term. All right. All right. So let's start with cloud migration and digital transformation. So you have here the usual beautiful chart [indiscernible], which shows a sustained rate of migration over the past few years that is expected to continue for the foreseeable future. And it is worth noting that Gartner expects spend on public cloud to exceed $1 trillion by 2027. And even with that, it would still add up to only 16% of global tech spend. So why is this happening? Because as a company, any kind of company, you absolutely have to, you have to interact with your customers online. You have to differentiate from the competition through innovation. You have to run into the cloud to get agility, short time to value and efficiency. And to be honest, you also have to lean into the best tech so you can hire and retain the great engineers. And in the end, this modernization leads to better business outcomes. All of this was true over the past decade, and we expect it to be even more pronounced in the age of AI. As you cannot adopt AI, if you are not digital and in the cloud. So our customers want to build their applications in the cloud as fast as possible using the latest technology. And this is a slide I've been using for a while now, and that is still true. And it illustrates the explosion of complexity. We've all witnessed [indiscernible] innovation over the past 20 years. So I won't spend too much time on it because we've gone [indiscernible] before. But you can note that we added a few things in there such as on the chart on the top right, which is the scale and compute units. You see that now we've added the GPU fleets with many [indiscernible] being deployed all the time. And the way you read those charts is you multiply each of them. And so what you end up with is truly an explosion of complexity. Now AI is a large, rapidly growing and exciting [indiscernible] spending. And I think we all noticed. But to put things in perspective, we have a few Gartner numbers. So the market opportunity really is very large. And for us, it is the next big thing that's going to bring both better business value to customers and additional leverage internally in our business. But it's also going to compound the complexity our customers face to take advantage of it. And so we added a couple more charts in there on the right side. You'll see one on the top right that illustrates the number and also read the scale of models that are available in [indiscernible] phase. And on the bottom right, we have a chart that shows the increase of developer productivity. And we've all seen the shocking ascent of coning agents over the past few months. But really, this is only the continuation of a much broader trend that has been going on for decades. When we started the -- way back when you had to write all the code yourself in low-level languages. And then you could use higher-level languages. So you became 10x as productive. Then the Internet came along and you could learn about everything so much faster, so you get another order of magnitude and productivity. Then we saw the assent of the open source software in the cloud. So you could use [indiscernible] components, so a full end-to-end services and then you get another order of magnitude of productivity. And today, with the rise of coding agents, I think we're starting at the next 1 or 2 other productivity right now. So this is the prime result. To put it simply, Datadog exists to solve this enormous problem of complexity for our customers. We connect to all of their software components. We scale with all the infrastructure, compute units they deploy. We also scale with the services they create and deliver. We understand the way infrastructure and applications are changing, and we connect separate teams to each other across different functions. Now what's interesting is that none of this problem are going away in the age of AI. In fact, the complexity is even greater because AI, all those things to be built faster. There's a lot more of everything. And the stakes are much higher as agents start to act on their own [indiscernible] humans. Our response to win this race against complexity is to invest, to invest in innovation and to invest on behalf of our customers. Over the years, we've invested about 30% of revenues into R&D. In 2025, we invested over $1 billion in R&D and ending the year with about 4,000 engineers. We believe that we are investing several times more than our largest peers. We are also adopting AI in R&D and [indiscernible] products, which gives us unparalleled leverage and velocity in our space. That investment has led to an expansion of our platform over time and our successful entry into new categories to solve more problems for our customers. We delivered [indiscernible] on the unified platform. that break down silos among what used to be disconnected teams and data sets. Now all those categories you saw on the previous slide, are part of the critical user flows that make up the activities of our customers' businesses. And what we see here on this slide is one way you can model on the customer side, the continuum of problem areas that take customers from tech innovation all the way to realizing business value. So you have writing code all the way to the left, and understanding where the value is and running the business all the way on the right. So we started right in the middle in observability. Of course, we didn't have all the functionality listed here from day one. We added a lot of it over time as we kept going deeper and kept covering all the new technologies our customers were adopting. And this includes new innovations to help our customers observe their AI stack as you can see on the highlights there. More recently, we've added capabilities around the data layer bringing observability across the entire data life cycle from injection to transformation and all the way to downstream using reports, applications and AI models. And the market for data observability is picking up quite meaningfully as data is critical to developing and adopting AI. Data quality monitoring, in particular, resonates with customers. Looking to the right, we have been building a great business in digital experience. We are now expanding further into user and report analytics, where we are seeing rapid customer adoption. And we are improving value for customers with our [ Synthetics and RUM ] products that can scale very cost-effectively to extremely large consumer user bases. And what we found when getting all of this into the head of customers, is that by broadening our scope and removing painful integration points, we see the value of our platform go up dramatically in surrounding areas. Now looking left on the developer side. The developer landscape is evolving very quickly with [indiscernible] tools and rapid deployments. And so the market for tools that have developers build, deploy, debug and iterate, is expanding very meaningfully. So we've been bringing more value to developers with capabilities such as feature flags, the Datadog MCP server and the [indiscernible] agent. And we have much more to do in the software delivery space. You will hear later about this topic from Michael. Security is a concern that spans from end-to-end across a spectrum of development, production and user interaction. And our suite of product delivers against that need. This is includes helping find venerabilities in development, identifying and eliminating threats in production, and securing sensitive data around live user activity. And we are building AI capabilities to move faster and [indiscernible] security problems. You will hear more from Tim on this area. We've been building our cloud service management products as well. What this involves is going beyond helping our customers understand and secure their systems and into helping them coordinate people and teams to manage, communicate, organize, take action and more and more automate response. So we are building on our momentum in this space, including the successful launches in 2025 of our own core product and [indiscernible] SRE agent. And Yanbing will share more about that. So if we look at the platform altogether, we are delivering an end-to-end suite of capabilities that help our customers build faster, deploy confidently, fix problem rapidly and deliver better business outcomes. And we are breaking down silos across operations and DevOps teams, data engineers, product designers, developers, security teams, incident responders, FinOps teams and business users. We have much more to do in each of these areas, but we've made meaningful progress over the years and are seeing broad customer adoption, which shows that we are delivering value. As proof of the value, we're delivering across multiple categories, as I discussed on the earnings call a couple of days ago, we now have $1.6 billion of ARR in infrastructure monitoring another $1 billion of ARR each in both log management and the end-to-end APM and [indiscernible] suite. The fact that we have real balance across the 3 pillars of observability as well as meaningful scale in each one shows that Datadog is unique within the industry in establishing true platform value for customers. Even though you've heard a lot of competitors say they have 3 pillars capabilities, their business typically remains driven by just one of these pillars, which dramatically reduces the value, they're able to deliver against customers explosion of complexity. All right. So I want to talk a little bit now about our AI build-out and break it down into 2 buckets. First, we're building AI for Datadog. We're embedding AI across Datado who -- every type of engineer can move faster. Our AI agents now surface context, identify problems and recommend fixes faster than any human can. They can be proactive and get in front of issues and users can interact with them in plain English. The second category is to cover the AI applications or agents our customers are building or running themselves. And we call [indiscernible] Datadog for AI. If you're putting LLMs or agents into production applications, those systems do need observability. They need to be monitored like any other critical app. In fact, as I mentioned earlier, this takes [indiscernible] hire as AI agents can now take action. So we're building a full stack of products, so users can understand and improve their AI solutions. So here's that AI for Datadog bucket, but laid out across our platform. So as you can see, there's capabilities across every single layer of our platform now, and we're building more. And here are the capabilities in the Datadog for AI bucket. Same thing here. AI-specific instrumentation [indiscernible] every single layer of our platform, and we believe we are uniquely positioned to win market share in each of these areas. The last time we had an Investor Day, 2 years ago, we told you this. We wanted to make our customers [indiscernible] more productive for every single step going from code to business value from end-to-end. We call this closing the loop. We are well on our way to deliver this vision on behalf of our customers. We do this across 30,000 customers from the most tech-forward native companies to the largest Fortune 500 companies in every industry, in every geography around the world. Here's the loop that we've been helping our customers close. For the production systems, it's an end left cycle of making or incurring changes, figuring out how to understand the affected systems and fixing problems as they arise. It's complicated, it's frustrating and it's expensive. And today, we save our customers time and money by breaking down silos across teams and data sets, and we close these loops tighter, faster and cheaper. But there is a second key workflow that is becoming increasingly relevant, thanks to the rise of AI coding. And that's the loop that moves software from development to production. The time spent and value created have to this point, been heavily concentrated on the build part. But with the rise of AI coding, software engineers will work much faster. Already today, we have at Datadog experienced engineers who are building features 10x or 100x faster than they could before. And I think I've seen some of them in the room today. So please don't hire them away anywhere else. So the value is instead shifting quite rapidly from coding to being able to evaluate changes and deliver business value. This is the space where code meets production environments, and that is exactly what bringing 100x more code to production will not be easy. The tough part is making it work in the real world. It will come into contact with all the other coding components in the environment. It's going to need to be reliable and scalable while being cost efficient. It will need to maintain the security of the business and user data. And in the end, it will have to deliver great business outcomes. As you hear today, we think our investment in innovation, deep domain expertise, large and diverse customer base and massive [indiscernible] of data are all factors that will help us be a critically important part of the solution to this problem. Our place in the world is right where code meets production environments, other applications, other agents, end users, and broadly speaking, the real world. And that is what we think is the most impactful place in AI development Datadog. At Datadog, this is what we've been building towards for the past 16 years. We've been around long enough to be part of the transition from monitoring to observability and to drive the adoption of DevOps at the time when cloud was just emerging. With code development accelerating potentially by orders of magnitude, the problem we solve is expanding to be an even more important pervasive and valuable one going forward. And solving this problem will take us from observability to the edge of autonomy. Enabling autonomy will mean that we validate our customer systems, apps and agents. And that we do so as they are increasingly AI coded or rely on probabilistic AI models that are, by definition, harder to test or predict. It will mean that we help maintain their security and safety that we keep our customers' agents aligned with their intent and constraints. That we give our customers the right control mechanism and automate their feedback loops, and that we verify that every change generates the expected business outcomes. So that's where we're headed. We think we have a unique opportunity to enable autonomy for our customers across development, operations and security to support our customers in their goal to rapidly deliver business outcomes accelerated by AI, to bring everything together end-to-end and to give our customers the ability to harness complex new technologies and deploy with confidence. This is the latest and by far, the biggest area of opportunity for us. As you'll hear later, our market just observability is very large and growing quickly. And even though we've successfully grown our business over time, our market share is still only in the mid-teens. And as I showed you, we have made significant headway in building capabilities in other areas, and those do significantly expand our addressable market. Furthermore, we are building both AI for Datadog and Datadog for AI. And we're embedding both broadly and deeply into the Datadog platform across every single product or use of AI. And we're already delivering AI capabilities that our customers [indiscernible]. Meanwhile, our investments will deliver a quickly accelerating pace of innovation as our engineers themselves built with AI and [indiscernible] technology. And we are very focused on deploying those investments to achieve this vision of enabling autonomy for our customers. If we can get this right, the sky's the limit. And this is why we feel that we are still just getting started and barely scratching the surface. Now we'll turn it over to Alexis Le, who will talk about our data-driven advantage and how we apply that to NextGen AI.

Thank you. Thanks, Olivier. Hi, everyone. My name is Alexis Le-Quoc. I'm the CTO and one of co-founders.. So I think Olivier gave you a clear picture of the [indiscernible] complexity that our customers operate at and presented very clearly the opportunity that's ahead of us. Given the platform we have and the platform we've been building for 16 years almost. What I want to do now is explain further why we at Datadog are uniquely positioned to use AI to deliver on this vision. So data is unique in how much data we get, how much data we have, but also how much we know about infrastructure applications and systems running out there. We ingest data at a significant scale, trillions of data points, billions of traces, exabytes of logs. We also have a diversity of data that users are selling to us about their system. This could be besides metrics, traces, logs, user sessions, data jobs, lineage, LLM and agent traces, team structure, service names and many, many other pieces of information. And they come from our SDKs, from our agents and from our crawlers, from integrations. That data is, of course, what powers the Datadog of today. That gives the current observability to our customers. But it is also the foundation for the AI needed to deliver fully autonomous operations. A few years ago, we started an AI research lab because we were convinced that given the amount of data we have and given our R&D capabilities, we could be leaders in building AI specifically for observability and security. How did we start? So we wanted to prove that having lots of data, lots of domain-specific data give us an edge and thus was born our first foundational time series model in Toto. Now if you look for the largest public data set of time series data, you get to about 300 billion data points. And it covers domains that you'd expect; finance, health care, energy, transportation, some web traffic and so on. But we train Toto on 3x that amount. And the vast majority of the data we have, we use for training, for pre-training actually, is completely unique to Datadog. And it's all related to applications, infrastructure, software systems. So as a result, when we compare Toto to other time series model and its ability to forecast, it performed far better than other models out there. So we reach state-of-the-art. And from there, what we did is, we released it as an open [indiscernible] model and hugging phase that -- I think that was last May. And we've seen a significant uptake since about 9 million downloads. Now you may wonder why release it as open [indiscernible]? What's the point? Well, for 3 reasons. One, and maybe importantly, we want to contribute to the field. This is a nascent field that I think can use all the help it can get. Number two, it was important for us to establish our credibility as an AI lab. And number three, because it's actually a way to understand how important these models are by just seeing how many downloads they get. But I'd say the most important difference that we notice is the difference of cost compared to AI models you use every day. We spent about [ $750,000 ] to train this model in 2025, which was, at the time, 3 to 4 orders of magnitude cheaper than a frontier model of the same vintage. Now sure, frontier model can do a lot more than what Toto can do, right? It can speak hundreds of human languages. It can review and amend legal contracts or analyze medical imagery. But none of that matters, right, in the context of observability. And with Toto, we can show that -- we get -- we showed that we can get good results with lots of proprietary data and small models. But I'll get back to that. Let me talk a little bit about training. So really, when you have a -- when you're creating a model from scratch, you spend a lot of time and money on the pre-training stage, if you will, training is an important piece. And it's both pre-training and training are essential to produce models and agents that are useful in real-life situations. So let us see how we're training the Bits AI [ SRE ] agent. And if you're not familiar with Bits AI, sorry, it's what's in the name, it's effectively a site reliability engineer. It is tasked with finding and building a plausible causal chain starting from a symptom on a software system somewhere and sort of building the chain from there. So when an alert signals an issue in an application, for instance, Bits AI SRE, develop hypotheses about weather problem, where the problem is coming from and [indiscernible] is all the data available. And the goal is to identify the root cause that can then be addressed. And so that the symptom goes away. And as you can imagine, and you'll hear more about Bits AI SRE, it's very popular because there are always problems out there on software stacks around the world. Now in order to train an agent to correctly identify issues in production, we need a solid baseline of past incidents and correct root causes. And here's how we've done it. Like any complex system, our own platform is constantly evolving and constantly being maintained. Think of it like the [indiscernible] around us. The streets need to be plowed, [indiscernible] needs constant attention and effort to fight entropy, so that life can keep going. So in the course of any day, our engineers investigate in fixed issues. And what they do to is the record their finding as well as the entire set of ability that was needed to reach a conclusion. And they turn that package into an evaluation or an eval. Because that eval comes from human analysis of an expert, and it was used successfully to troubleshoot and fix an issue, we know we can trust it. And every time we make a change to a model or a model instruction we run through the growing body of evals and see if the model gets better or worse. So this is a chart behind me showing a number of evals that our engineers have recorded over time. This is not something that can outsource to nonexperts out there. And it is important that you cover as many use cases as possible. And for that, you need a large infrastructure to do it, and we have the large infrastructure. And again, going back to the city analogy, to keep New York City up and running, it takes a lot more variety and scale of effort than if you only have to keep up a small village, for instance. And so as we recorded more evals, we've seen the accuracy of Bits AI SRE it has gone up, not as a straight trajectory, it's up and down, but we can see generally the trend. And so that's how we've improved the quality of Bits AI SRE. And normally, we're going beyond this sort of human curated set of evals in building. So [indiscernible] is to generate synthetic data, and that's really to reach an even larger scale. And we think it's necessary. And as our customers have begun to use Bits AI SRE, they've actually -- the -- sellers feedback, right? This is useful, not useful. This has worked, not worked, and here's why it was useful and and why it was not useful. And we, of course, use that as evals. And that's great because it continues to enrich the diversity of environments and problems that our agent faces. The reality is there's no shortcut in training -- pre-training and training to get this kind of high-quality result. You just need to have lots of data and expertise. And that we feel is a strong differentiator for us. So let me maybe sum up our advantage. One, we have access -- continuous access to lots of clean and rich data that we can use for pre-training, for training and -- of financial models and agents. Number two, we are building our own models. Number three, it's not only the shared amount of data matters, it's also the diversity. It has to come from a broad context. That's very helpful. And before, we bring to bear our domain expertise to improve our model and agent performance. So we think that we're unique in our ability to deliver the best in observability powered by AI [indiscernible]. Now you may wonder, okay, that's great. Why not just take the data and throw away out of the frontier model and see how it does. And thus, you would replace data [indiscernible] with just a bunch of data in frontier model. And maybe you have the data to do that. But first of all, is it enough? And what you'll get is you'll get -- so we've done that internally because our customers probably do that. What you can do is you can -- the Frontier model is going to be great at summarizing data. You have to give it a bunch of context. You're going to get some good results. The main problem is extremely expensive. So here's the idea. Here, I'm plotting costs and accuracy, if you will. And read -- first of all, training a frontier model, it's starts at $1 billion. And it's unclear how high or how the accuracy slope trends as you go orders of magnitude past $1 billion. Whereas the research is telling us a different approach works and this we think is better. We've built much smaller models on the exabyte of data we have. And the small models, what we've proven is that they have orders of magnitude better accuracy per dollar because simply, we're not paying for things that frontier models have, but we know won't be useful in the context of observability and security. And even I'd say if you hire lots of engineers to fine-tune and do RHLF and so on, it's not going to be able to match the accuracy at the same cost. Because really, when you use the frontier model, observability is you're paying for the amortized cost, the pretraining, training and on top of that, all the -- so the vast quantitative hardware that's needed to run in front of the Frontier model. So in summary, one, we believe that the autonomous operation needs very good models at a low cost. And number 2 is the approach with small dedicated models, lots of [indiscernible] training data and ongoing evals, we think, is the way to go. So I've shown you the reason why we build our models and also how we can improve them based on real life observation. Now you may wonder how do we tie all this together to get to fully autonomous operations because autonomy requires validation, safety, security, alignment and control. In other words, we have to go beyond only observing a system. We have to understand its behavior and maintain control through verified changes. So in our lab, we've built, as I mentioned, a bunch of models, but we've basically understood that what we need really for this domain is a world model. So how are we going to do that? We take all the data we have, and it needs to encode and represent the state of distributed system. Now we are already in a position to observe these systems, right, because we plugged in everywhere. We get that -- these exabytes, trillions and so on of data every single day. So what we're working on right now is an optimal representation of code, system structure, system behavior. And we think it is essential to predict the future behavior of software with high enough accuracy. For that, you need to understand past behavior and have knowledge on how such [indiscernible]. Without it, it's not going to work. Without -- and finally, without high accuracy, they won't be autonomy. So we have to get to a high accuracy. And when we think about steps towards autonomy, we think in terms of stages, here represented on the right-hand side. Started with customized and adaptive observability than proactive alerting, automated remediation. And then you get to the predictive and proactive and preemptive sorry, -- and finally, you get to autonomous operation. And with bit SRE, we've tackled the first stage, automating away sort of the more manual steps of observability. So the need, for instance, to build dashboards by hand and so on is gradually going away. And we've built proactive learning and automated remediation with the goal to take care of an increasing number of cases when currently people are seeing the loop to fix things. Now there, we think our trajectory is going to be like that of coding agents, limited in scope in the early days, more narrow, but increasing as time passes as we refine our models. And as that happens, as accuracy and coverage continue to increase, we'll be able to further predict and then preempt and prevent issues before they occur. When we reach that, we have a self-healing, self-managing, self-optimizing system, able to operate customers' infrastructure and application with fairly limited human intervention. And that is our goal, as Olivier said. Now there's obviously a healthy amount of work to get there. But we think we're uniquely positioned to make it happen. That is where we're going because that is the only worthwhile goal. But for now, let us here about what we're delivering for customers today. And for that, I'll hand it over to Yrieix.

Thanks, Alexis, and hi, everyone. So my name is Yrieix Garnier. I'm super excited to be here today for my second Investor Day. So Olivier and Alexis talked about how we're moving forward towards autonomous operations. In these sections, I want to take it back to its foundation, the Datadog platform. Platform is what Datadog started. And over the years, it really helped us break down silos amongst teams and data. So to do that, we've built a robust set of elements, thousand of integrations, common UI, data services. And we have about half of the 4,000 engineers working on the platform. And the investment allows us to be evolved very quickly and to seamlessly integrate AI to better serve our customers. Bits AI, this is how we're calling our AI capabilities is really present throughout the platform. Let me give you a few examples. So it can analyze and correlate all the different data types that we're getting from our customers, to detect, investigate and remediate code fixes, or interact with Datadog through natural language. And we're just getting started with more AI capabilities to come. But the platform is also critical for a rapid pace of innovations. By leveraging the platform and all these building blocks or engineers, team can really stay very lean but also move really quick to deliver either new products or enhance existing features. This is really the flywheel between platform and product. For instance, it helped a handful of engineers to deliver companies autoscaling end-to-end solutions. Or after the Metaplane acquisitions, we were able to take to market data observability in a record time. So today, we have dozens of products, and we continue to advance our platform to accelerate this flywheel effect and build even more product faster. But let's look at the data injection side. We've also added a number of data sources including like valet, LLM inferences, and we can ingest extremely large amounts of data to provide analysis and correlations in the context of our products. But our end goal is really to deliver value to our diverse users, but also to the uprising number of agents. They all need the data in one place. We need to provide the best outcome. As Olivier said, our customers are facing a rising level of complexity. And our job is to stay ahead of that. And if we do all that right, we provide a single set of truth to break down silos across all users and agents. So the platform actually come a pretty long way over the years. Let me reflect on that. In 2015, we started with few integrations. [indiscernible] millions of events per hour and only had one product in from monitoring. But our customer needs and demand grew really exponentially. And today, we have over 30,000 customers, including some very, very large ones. We have 25 products and we can handle trillions of events per hour. As you can see, our platform reached unprecedented scale. And it's one of the main reason our customers keep choosing Datadog as we can store, process and move data with always improved performance or cost efficiency. But let's look at this from a customer angle. This chart is a very fast-growing AI company. The customer demand grew very quickly. But where the end user were experimenting bottlenecks, they needed a unified [indiscernible] solutions to get to the root cause as fast as they could. And to do that in about one year, they adopted 16 Datadog to products, and that was really key to their business success. So that's a platform. Now let me speak of the way we're scaling and our customers just retain and analyze the growing data sets. So to expand scale, let me take the example of the log management. We launched it back in 2018, and it had multiple scaling phases. The first phase was called logging without limit. And it's all about ingesting all your logs without limit and only processing the relevant ones with correlations with infra monitoring and APM. So bringing it logs in a valuable but still economical way is really the foundation that helped grow that business 7x to $1 billion ARR. But really, it's the foundations of our customers value. And it worked great for observability logs. We also knew that we are only capturing a fraction of our customer logs. Of the use cases like transaction logs or audit logs typically involve log volume that were other magnitude larger and which needed to store for a much longer period of time. So as the second stage of growth, we've extended the capabilities to support those use cases with Flex Logs, Frozen and [indiscernible] Search. By doing so, we've unlocked new market opportunities and delivered more and better outcomes to our customers. And it actually worked. We saw very strong adoption of Flex Logs from the start. Now our customers are storing tens of trillions of events and Flex Logs is approaching $100 million ARR and growing very rapidly. But to keep pushing on this log journey, we've leveraged the platform to build a new product, Cloud SIEM. Cloud SIEM is actually a natural extension for text logs and it requires logs to investigate security issues over a long period of time. And Flex Logs really unlocked the security revenue. With 18x growth in 5 years, we are still seeing an acceleration in Cloud SIEM adoption. And if we look at this, we are still actually at the beginning of that opportunity phase here. Sorry. So as you can see on the chart here, Flex Logs really unlocked that security revenue. It grew like 18x in 5 years. And we do see that acceleration. Yes, I just already said that. So Tim is going to talk more about this as we actually do see that the beginning of that opportunity. So let's now look at the impact of the platform data scale and actually a whole customer journey. These e-commerce customers started using infra. Then in 2022, they adopted APM, Synthetics and Logs. And in Q4 2023, they become a Flex Logs and SIEM customer. As you can see over time, they consolidate more tools within Datadog, which enabled them to address an increasing number of use cases, cover more environment, larger data sets, and most importantly, enhance their customer experience. And that's how Datadog and customers really scale together. So to finish, I want to talk about enterprise coverage. This is where the platform effort help our largest, our most sophisticated customers to drive tool consolidation. Beyond monitoring everything in the cloud, a number of our customers asking us to cover more of the environment by combining cloud and on-prem. With our extensive integrations, this is something which is out of the box for Datadog. We're already supporting on-prem servers and network. And now we also monitor wireless access point, end user devices, like laptops and desktops or edge devices. So with that, customers can really see the entire physical footprint in one place and also combined with the cloud environment. And for us, it means a larger footprint and a bigger market opportunity. We also continue to expand our coverage across our customer tech stack. Historically [Audio Gap] for cloud security, we provide posture scanning, run-time vulnerability detection, attack analysis and combine that with observability enriched prioritization to tell you what cloud risk matter the most. With AI and data security, Datadog automatically secures sensitive data and offers comprehensive run time protections for AI agents to enable safe AI transformation. And finally, for developers and DevOps teams, we have code security, helps to identify vulnerabilities and first-party code, open source libraries, infrastructure code, all before they move to production and therefore, dramatically reducing alert fatigue. And with Bits AI, code security generates bulk remediations, so developers can still deliver secure code while getting to spend more time on building and innovating. Now plus our security products, because they're built on the Datadog platform, they can all take advantage of Datadog's shared services to accelerate remediation. For example, integrated case creation and incident response to security, SRE and DevOps can rapidly collaborate with one shared view, or even one step further, agent builder can be used to build custom automated remediation workflows. So we have growing proof that the data log advantage that I just outlined of unifying security and observability is delivering value to customers. Today, Daydog has over 8,500 customers using our security products. This includes one in 4 of the Fortune 500, and we've now surpassed $100 million in ARR. And you know who else is using our security products, we are. The Datadog security team secures the Datadog platform for our 30,000-plus customers using Datadog. We put our reputation and our business on the line using our own products as an indication of our confidence as a security vendor. So we're off to a good start. If you wanted to pause [indiscernible] okay. So we're off to a good start. We believe we have a big opportunity ahead of us. Today, 70% of our $1 million customers use one or more Datadog security products. But the spend security represents, it's together is only 2% of their Datadog spend. So as a result, we see potential for much more wallet share as we deliver more security products and capitalize on that Datadog advantage. Let me give you an example. Here is a long-time Datadog customer in the media market, who over time has adopted a number of security products from Datadog. They wanted a unified observability and security platform, and they chose to consolidate on Datadog over market alternatives like Palo Alto, CrowdStrike, Wiz, Google, Microsoft, just to name a few. As a result, today, 20% of their Datadog spend is on our security products. And that could just be the start. For another one of our $1 million customers, 38% of their business with us is security. So we see a clear opportunity with all of our current and future customers to go deeper and broader with security. And how we get there is the value of the Datadog advantage. It combines and correlates security and observability in a truly unified platform. So that's it for me. Let me hand it over to Michael to talk about what we're doing for developers.

Thank you, Tim. All right. I love seeing everybody taking a ton of note. Anybody using AI right now in this very moment? I knew it. There are some people out there -- advantage, right? So I'm Michael Whetten. So we live in this era of speed right now, right? I don't know if you all feel it, but my customers are under intense pressure to compete and innovate and build and ship product that works to customers and try to scale out as fast as possible. And it's been like that I've been in Datadog almost 10 years now. It's been like that since the beginning with the cloud, this competitive advantage that technology can bring but it feels like it's accelerating, right? But as Olivier said, the complexity is a drag on their ability to innovate, right? The big companies have a lot of complexity, and they're feeling the drag in different ways than small companies who are having trouble getting to scale, right? And they have drag in different ways and different types of complexity. And if there's one thing that you take away from today, it's that -- I know some of you have traditionally written about Datadog as kind of an insurance company, utility company or some -- must have for companies that scale. But more and more, my customers are telling me or they're adopting even the fastest-growing companies in the world right now who are in the most hypercompetitive landscapes, they're buying Datadog because they need to move fast and Datadog enables them to do so. So let me walk you through a few examples of how I see this working. So one type of complexity that we see a lot is fragmented visibility, observability or monitoring or product analytics or whichever point solution they have here. So here's a simplified diagram of an application, a single application at a company. The fragmentation, each of these tends to represent a different team at a company, right? And even though it's a single application, and this user is interacting with your application, making requests, getting response traversing through whatever your application does, the fragmented nature of the organizations that are required to serve this user, they don't always provide the best user experience so that when an issue happens, right, if I go to a lot of my customers right now and I say -- or potential customers, usually, I say what's -- my favorite thing is kind of a [indiscernible] question. But my favorite thing to ask them is where -- how do you know that things are broken now? What triggers an incident? And most of the time, those cheapestly look at the floor and say, well, customers, like a support team where somebody tweets something is down and then we call an incident and immediately start reacting. And some of you are nodding because you must hear this from people as well, right? And the problem is, is that all of these different teams are using different tools. So we have a bit of a tower battle problem, right? And who knows that these signals that are going off even point to the root cause of the problem. So to use a real-world example, there's a major global bank in which they have 5,000 engineers. So that was one application. Now at this bank, there's hundreds of business units, and each one has many applications. So 1,500 applications spread across the organization, right? And the problem that they have is these -- the way that the organization is set up doesn't match the user experience, somebody might be trying to make a withdrawal or deposit at an ATM or on their phone. They don't know how the company is set up and whose problem is who. So they just know they can't do something. But when something goes wrong, these are all interdependent technologies, and troubleshooting this is a nightmare, right? So they consolidate everything on to Datadog. They went to all those point solutions and brought it all in. And the beauty is that, not only is it just one single pane of glass, the real advantage is that from the time of collection till the time it lands in Datadog, we're automatically summarizing and correlating all that data. We're making sense of that data. So that when it lands here, most of the work is done. And when a signal goes off, it's typically in the right place, notifying the right team and they can [indiscernible] responding much faster so that you have global impact within the organization just from consolidating onto one platform, right? So millions of dollars a day in avoidance, better customer sentiment. But the real advantage, the real value isn't actually displayed here. The real value is all those people that were responding to those incidents that were taking hours and now take minutes. Those are sometimes the highest paid or smartest engineers at your company. And rather than building value for the company and living the bank forward into what is the next generation of bank software, they're spending time and incidence. And here now, they can spend time adding value for the customers. So ripping and replacing load-bearing technologies that have been there for 10 years across 5,000 engineers, it's not super easy for a lot of organizations, right? Either it's a mandate as it was at that bank or a trend that we see happening now to address some of this complexity and scale is a new movement that we're seeing where people are calling it user-first monitoring. So much complexity on the back end, so many teams to coordinate, so much politics to try to coordinate. Can we just start with the user experience and start making our way backwards, right? What are the most load-bearing critical user journeys that we need into in visibility and start traversing the organization that way? And so the crux of your business really is your application front-end stack. That is what your developers are typically making, right? And then they scale it out with infrastructure network. So we have a suite of products for this solution and the space is called Digital Experience Monitoring. But really, it is what is the user experience, how are they impacted by the changes my engineers are introducing into production? Are they making things better or worse? And so in some ways, this is accountability software. Are my engineers who I'm paying who are there to bring value to the customers, are they making things better or worse for the end user? So we see a big push towards enabling engineers to have direct visibility into the business impact of their changes. And so this is one of our fastest-growing areas. And we see that -- when the APM, which is the back-end instrumentation and the front-end user experience are stitched together, it's a story that works and it spreads through the company much faster and it spreads through organizations much faster, and we see that when these things are together, we have -- we provide a lot more value, which turns in more revenue for the business. And this isn't unique to digital experience and APM bundling, as customers grow with us, they do find more value. The land and expand does work and more products equals more value, spreads in more parts of the company until they have that consolidated approach. Why? Because this is better for the human responders, right, bringing more people into the conversation to troubleshoot faster together is meaningful and does increase the value. But an advantage of this single platform and that automatically correlated and summarized data that we do is it's better for our AI agents, too. We found that when the AI -- when we have the full context of the entire stack and is already correlated for humans, AI also operates much better on that. It can act faster, right? So what might take a team of people before many hours to solve, but then they adopt Datadog and they can do it in maybe one hour. The AI can come to the same conclusion in minutes, and we'll hear more about that soon. But it can traverse. This is, again, one application. It can reverse all the applications. It's working 24/7, right? And so the advantage here is when it has -- you can't separate the AI from the underlying data formats and context. There's a lot of work you can do to make the AI more efficient, faster and more important, more accurate. So Datadog does bring equilibrium into that DevOps life cycle. So the developers can develop as fast as they can. What are they doing? They're introducing change into production. That's their job is to introduce new things, but production can adapt to that quickly and make sure that it's done safely and securely. At Datadog, we benefit from this. The reason that we're able to ship so fast is because we use Datadog. And so we are the embodiment of that DevOps life cycle, and we do have that equilibrium. This last year, we released -- we announced and released hundreds of features into production, right? These aren't ideas, they are actually out there in customers' hands, iterating with customers, proving value. Now as has been said already, there's a sea change, right? When I walk around Datadog office, I see a lot more engineers coding on their phones, having multiple windows open, directing lots of agents and writing code 24/7, they say it's addictive, right? And I'd say it's only accelerating. We've got one guy who's coding with his glasses, right? He's sending prompts and he's saying, looks good, now change this, right? And so this is a real movement in the industry right now. But what does it do? We don't hire developers just to write code. So 100x more code might not actually translate to 100x more value. Are you really going to release 100x more features? Are you going to release one feature that's super sophisticated and can do a lot more? How do we verify that this 100x code is good that it works and that is a good product? There's a difference between code and product. So this is -- the bottleneck is no longer coding as Olivier was talking about, but bringing value to customers maybe at 100x. And this is where Datadog has a unique opportunity to help the AI movement forward. So developers love to make changes and bring things into production, but they break things. The number one cause of incidents is faulty code, right? And that's when humans are reviewing it and taking painstakingly efforts to make sure it's good. But Datadog has automated integrated code testing. We also have the best understanding of how production works, how that code is deployed, how it's connected to other parts of the application, what data is flowing through that code at all times, that we can actually inform the entire life cycle here, whether it's humans or agents with AI coding, we can tell the code that you're writing right now, hey, this code that you're -- this idea that you have that you're going to propose into production, it's going to increase latency. It's going to make things slower because look at all these requests coming in right now and feed that into the agent. So the agent can make better decisions or so that the person whose hand coding can make better decisions and write better, more efficient, more secure code that we already know is going to work in production. Now as you're sending it out, right, there's a risk mitigation strategy that already exists. You write your code, you write unit test, you run those tests locally. There's Git hooks in there that's going to run all your test locally, then you push your change in some centralized pool of [indiscernible] called CICD and it runs a bunch of tests and those tests, sometimes they pass, sometimes they fail and you have to kick it and it goes again. Then it will land in a staging environment where people kind of vibe check it, type of few things, looks okay, let's push it into production. If you're more sophisticated, you got feature flags, which means that you're going to slowly roll it out region by region, 5% hit it, 10% hit it, 15% hit it, what are you doing? You've got Datadog at least you've got monitors there to tell you if things are going off. If you don't -- usually, you're just praying and waiting to see if requests are going to come in, we can actually do much better than that. We have automated testing and we just launched our feature flags product, which is unique in the domain in which we can actually contain or sandbox that change. When it goes to production, we can attach metrics to it automatically. And as it starts to roll out, we can statistically tell you this is going to be better. This is going to be worse. Roll it back or go straight to 100%, right? And so that's feature flags and experiments. So but the gist here, and I can talk about any of these things, but the gist is we can automate much of this, just as AI is pushing more code or AI and the manual tooling that we're enabling for customers as well, which the AI will use, can accelerate this path into production. So rather than becoming a bottleneck or a super high risk and just breaking things all the time, we can reestablish that equilibrium even at the pace of AI. So that's -- my throat is getting dry, but I am almost through it. So the AI -- or sorry, the code gen that most people are doing right now is to just build existing applications. And there's some exciting stuff. Technology does empower the creative individual, right? So I had -- somebody last night told me that from the time they got a white paper for a new database -- this person works for a database company. So research paper, they went [indiscernible] the idea into a working prototype and they thought they were so smart and novel and they were the third one on GitHub to have put it up. And this is like in hours after the white paper was published, right? And so this is what I mean by speed. Well, everybody is trying to take advantage of -- they're trying to build more intelligent applications and they're trying to also see if they're going to be safe in the environment. So it means that us as Datadog, we have to continue to build new types of products, new types of observability, new types of security to be ahead of the curve when people need to start bringing these things into production. So one of those things -- when I do talk to CEOs, CTOs, CIOs, one of the big concerns right now is the proliferation of agents in their company. What are these agents, right? Who made them? Why are they there? How much do they cost? How are they permissioned? What are they supposed to be doing? Are they doing a good job at that, right? Am I getting value? I see 5 of these agents that are supposed to be doing the same thing, should we choose one of them, right? So the AI agent [indiscernible] gives leadership a way to actually track and teams who are building these things a way to track all of these questions about the agents that they're bringing into their market. Now those who are building these agents are trying to enable their product with more intelligence, they're also stuck unless you're at a leading research lab or Frontier model lab, you probably don't know where to get started. And so there's a lot of information out there. The nice thing about AI observability product is that it comes with out-of-the-box framework. So just by using the product, you start to understand more about if you're already an expert and you'll be familiar about these tools. If not, it's almost an on-ramp in how to think about nondeterministic applications. I need to evaluate these things for basic competencies before I send them out into the world. Once they're in the world, I can experiment to see what is good behavior, what is bad behavior or trending which direction, I can try different models, et cetera. I can sandbox and I have playgrounds, all the stuff that these researchers and AI engineers need. So the momentum on this is growing. There's been a lot of investment in learning over the last 24 months or so, but we're starting to see these things try to get into production and who helps people get things into production is Datadog. So that's why these products are growing with us now. And the last thing I'll say here is we're not only getting started, right? This movement and helping this movement to figure out what it is and what it will become. Datadog is perfectly situated to enable that and to talk more about the exciting things we're doing with AI is our Chief Product Officer, Yanbing Li.

Thank you, Michael. How's everyone doing? This -- you are getting to the last stretch of our first half. So my name is Yanbing Li. I joined Datadog as the Chief Product Officer about 18 months ago. Before Datadog, I spent time at Google, responsible for the observability function powering Google's [indiscernible], infrastructure and services. So when I went to speak to a senior SRE leader to get some candidate feedback, this is what they tell me. They simply said, go look at Datadog. And this was back in 2019 before Datadog was even a public company. After Google, I got to lead engineering and product at autonomous trucking company or innovation, actually, the very first company to operate commercial driverless truck on the U.S. public roads. At Aurora, get to learn firsthand what it meant to ship safety-critical autonomy product into production and at scale. So this is why I'm excited to be here at Datadog to help our customers ship faster without breaking things and operate reliably and safely, all while navigating the increased complexity of AI. So let me circle back to this DevOps loop that Oli showed earlier. This is the reality of what our customers DevOps team leaves through every day. They need to detect issues as they emerge they try to investigate and find a root cause and the next step of action, and they take action to remediate back to health. And because systems are always changing, with new code, new traffic, new dependencies, this loop just doesn't stop. So what happens when a major accident -- when a major incident happens to a production system? Our largest customers often tell us, they need to mobilize tens or even hundreds of engineers because they bring different knowledge, different data, different tools and also different system boundaries. And also, most of those teams, not only they have a partial view, they're motivated by proving it's not their problem rather than finding the problem. So the area under this curve represents the time and times resources that's part of this operating expense. And certainly, incidents, we all know is very expensive to business outcomes with lost revenue, lost customer trust and reputation of risk. So this is a structural efficiency we are trying to solve at Datadog. So Datadog is in the business of keeping this DevOp loop healthy and running for our customers. So when there is production stress, we detect issues, we help coordinate the customer's team, get the right team involved to investigate and take action to remediate the system back to health. And the previous speakers have already talked about Datadog's unified end-to-end observability platform can shorten the incident response with fewer people, less time and closing this loop faster. So the result can look something like this with faster detection with the right team involved with the right information, they're solving the incidents much faster. And we all know when you have an incident, time is money. Let me take you through this with a concrete customer example. So this is a major U.S. insurance company who's been a customer for 5 years. Before Datadog, they experienced thousands of severe incidents every year. And with Datadog, after they standardize on our core pillars of product, they begin detecting and fixing those issues proactively, preemptively before they became real production escalations. And as they have seen a 10x reduction in their severe incident count. And certainly, when they have fewer incidents, when they solve them faster, there is a significant boost to their engineering productivity, and they're saving about equivalent to 70 employee years and translate to $11 million every year. So what does this mean from their business point of view? Again, with fewer incidents with faster resolution they are actually seeing a whopping 20x reduction in customer impact that's caused by this incident. So this is the kind of value we've been providing to our customer with our unified platform. Now by applying AI, we're taking that to the next level. We've launched a fleet of Bits AI agent. Yes, in Bits with those futuristic sunglasses. And so -- so we're helping our customers autonomously detect, decide and taking action so that we're closing this loop even faster. So let me give you a few examples of the Bits AI agents and starting with the SRE agent. So you've heard about this several times throughout this presentation. So why SRE? By now, the world has recognized that the future of coding is going to be AI coding. But still, a lot of our customers are struggling to really measure and establish the real ROI. And we pick SRE as our first agentic effort because not only our primary user personas are SREs, but also when an incident happens, it's often acute, high stake. And better yet, the verifiable results of what Bits AI SRE can do is very obvious to our customers. Actually, many of our customers to test Bits AI ASR, they simply play back all of their previous serious incidents and see if Bits AI can get it right. Obviously, the business outcome is also very tangible when you can reduce incidents. So because of the verifiable nature, our customers are really excited about what Bits AI SRE can do for them. So how does it work? Don't worry, this is not an eye test. Let's focus on the left-hand side. When alert triggers, Bits AI autonomously investigate the issue. It first gather all the necessary data and relevant context, it then can reason like a group of engineers in different parts of the system, try to establish multiple hypotheses of what happened and then investigate all these hypothesis in parallel. The right-hand side is intended to show you how that parallel works and is very visually explained to our users. It then identify the root cause and even can propose the next step of action based [indiscernible] and better yet, Bits AI can learn and it's getting better with every investigation. So the superpower of Bits AI SRE also comes from this holistic understanding of our customers' entire environment, spending systems and applications and users and teams and even business processes. So it doesn't just leverage the rich real-time observability contacts and telemetry inside the Datadog platform is broadly integrated with third-party knowledge stores and also third-party telemetry. So our customers can really get that full picture of what's happening in their system. Even though we're still in the early days with Bits AI SRE, we are already getting a lot of positive feedback from our customers. Here, you see 2 examples that customers are telling us how Bits AI SRE can accelerate their incident resolution and how it's acting like experienced engineers to help them understand their complex system. And when they matter the most, our customers are also telling us Bits AI SRE get the job done. So if you remember, the major AWS outage last October, we've got many customers reach out to us saying, when that outage happened, Bits AI SRE was able to autonomously root cause to the outage before being notified by AWS themselves. So even though Bits AI is AI agent, it actually gets a lot of love notes from our customers. As you can see on the screen here, and not just because this is a Valentine's week, we hear this from customers all the time how pleasantly surprised at how smart Bits AI is, how it gets to the root cause, how it's saving them time, how it's boosting their productivity. And the best indication of that is the actual usage. For new product, we look at the usage metric very, very closely. So since the Bits AI SRE launch, we've had our customers run well over for 100,000 investigations and since our GA last December, this rate is increasing and accelerating. And in January alone, we have more than 2,000 customers run investigation with Bits AI. Okay. So let me switch gear to talk about how we're using AI for some of our other use cases and products and starting with this security example. We have our Bits AI security analysts in preview. And so this agent can autonomously investigate Datadog's Cloud SIEM signals and conduct in-depth investigation for potential threats and also enable users to remediate those threats all in the Datadog user interface. I think a better way to explain this is a real example. So a major financial services company was testing Bits AI security analysts for the first time. And actually correctly identified a live, serious security threat. So the situation is a compromised automation system in their environment change their cloud firewall setting such that it's open to the entire Internet. Some of the sensitive management ports are open and exposed. Does this sounds quite serious? Yes. So without Bits AI, so the investigation would happen that they may receive some security signals and that goes into a queue and human investigate them one by one, and it could take hours for them to come to this realization versus was this security analyst agent could do investigating in parallel. And within minutes, we were able to service this severe threat to the customer. Obviously, the customer did become true believers of this technology afterwards. And this is just an example how Bits AI security analysts can truly transform how security teams can investigate and resolve security incidents. Let me give another example because Bits AI SRE and security analysts, they're doing investigation or trying to understand what's happening in the environment. What about autonomous remediation. So this is why we introduced the Dev agent. So the Dev agent can automatically analyze the telemetry and code when there's an error happened in our customers' system. It can explain the root cause in plain human language and even map it directly to the relevant code, files and function. And then proceed to generate a context of where fixed and [indiscernible] is important because this fix would be generated based on the real production contact that Datadog uniquely brings to this problem. We can then proceed to test the fix in isolated sandbox so that you have high confidence that this fix is ready to push to production. And all of this can happen without a developer even logs in. And of course, the tool can interact with the developer. When they do log in, they can review the code, they can ask questions and they can help also merge the PR. So you may be wondering, is this yet another AI coding agent? Now there are already so many on the market. The answer is no. Because Bits AI Dev agent is deeply integrated within our DevOps loop to truly [indiscernible] bringing that full production context, so to help create a better PR. It's also very proactive. A lot of our customers are really pleasantly surprised that they received a Slack message from Bits informing them, there is a high severity error, and Bits already fixed it for them with a PR that's ready to be merged. So I shared 3 Bits AI agent example. And the important thing is with AI, how our users and engineers are interacting with their tool is also radically changing. So the good news is our customers can use Bits AI from anywhere that's fitting into their workflow, whether it's in their Datadog UI or through collaboration tools or from their favorite IDE or getting involved by another AI agents. And all of these interfaces are also enabled by the Datadog MCP server. So the Datadog MCP server enables our customers and the AI agent to access Datadog's docs AI-driven observability [indiscernible] from -- directly from their existing workflow. And as you can see on this chart, since the launch of our MCP, we've seen also exponential adoption and growth. And many of the customers are integrating this to their existing workflow. They're also building custom AI agents so that they can build those agentic workflow to help them with incident investigation performance optimization and many other use cases. So with Bits AI and MCP, now incident resolution can look like this. We can help our customers narrow to the root cause, take action within minutes and with very few people involved as opposed to the tens and hundreds of engineers working tirelessly over hours and days. And better yet, the Bits AI agent can easily work alongside human SREs and human security analysts and developers to make them far more productive. So we're closing the loop even faster. That's the value we're bringing to the customer. So I just spent the past 10 minutes also taking you through how Datadog is solving the structural inefficiency in the DevOps loop by providing an end-to-end unified observability platform and now turbocharged with AI that we are helping our customers closing this loop very, very rapidly. And as Michael and Olivier alluded to earlier on, we've also been shifting to this loop on the left-hand side to the preproduction environment to help our customer ship better software into production. So our long-term vision, as Olivier outlined, is to achieve autonomy across Dev, Ops and Security. So that will require us to help our customers validate their system, their application, their AI agents, and in addition to helping them shipping production ready [indiscernible] hold faster and preventing incidents from occurring at all, we have to help them maintain their safety and security to achieve true alignment of their AI application toward their intent and business outcomes. And in the meantime, give them control and feedback to help them improve. So I am personally very excited about this vision. It is special having built autonomy for trucks now building autonomy for development, ops and security. So at Datadog, we are very excited of this vision that our customers need us to bring this together now more than ever. With that, thank you, everyone. And I will hand it over to Yuka for Q&A. Thank you.

Okay. Thank you, Yanbing Li. We are going to start a Q&A session. Now joining me on stage are all the print centers you just heard from. Their names are up on the screen. We are -- we're going to be taking questions from the in-person audience. I have 2 of my colleagues, Megan, on your right; Eric, on your left with mics. And we'll alternate between them. So please, yes, raise your hand and wait for them to get to you so we can all hear your question. We're going to start on Megan's side.

It's Kirk Materne with Evercore ISI. Tim, I was wondering if you could talk a little bit about the silo tax that you brought up. I mean one of the reasons there's always been silos is buying silos, meaning you've had SecOps, DevOps, IT apps have different budgets. I was curious as we head into a world where you need more talent [indiscernible] across all those areas. Are you seeing those budgets collapse into one? And if not, how do you make sure that you're talking to the right person to get more sort of visibility on the security [indiscernible].

Great question. So I think we're seeing 2 things right now. Number one is the recognition that we can make security easier, better with the unification and consolidating on a single platform. And it's more just the [indiscernible] or no longer the need to have those separate budgets for those items because they can get it all through the single platform. So that's one item. The second point is, I think there's also a broader recognition that, in fact, we use this ourselves at Datadog that having organizationally security and SRE, the same word has a lot of benefits, particularly when it comes across the board for both reliability as well as security. The team is working together. Of course, in our case, using our own platform and tool sets it will work much more effectively. So I think that's something that will also emerge over time is probably a growing trend across organizations as they see the need to be more effective.

Just to add up to that, it's not completely about the [indiscernible] between buyers. But if you think of the impact of coding agents, there's going to be much less separation between the roles like I think as we build a lot more, a lot faster roles that used to be [indiscernible] as how you're building it separate from whether you're building the right thing, which is separate from whether you or not [indiscernible]. [indiscernible], which is separate from who you operate it. I think all of that gets merged together quite a bit as the coding itself and how you build it is left to the agent.

Great. Thank you. Eric's side.

Alex Zukin from Wolfe Research. Thank you so much for the presentation today. I particularly wanted to ask about Bits SRE agent. And given kind of the increasingly heterogeneous environment that is increasing the magnitude, scale and complexity. When you think about the ability to read from other kind of data sources outside of the Datadog perimeter and compiling kind of complete tasks across that information. Can you talk a little bit about kind of where your tool, how you differentiate in that context versus other folks building there? And then maybe some pricing and even a customer example kind of who's at the frontier showcasing.

Great question. So when we started Bits AI SRE, we actually focus on the data and telemetry that's within Datadog because the most important thing for AI agent is to show that they can get it right. It shows value to the customer. And when we are within the platform, we have real-time rich data that allow us to really showcase that value to our customers. Obviously, a lot of our customers do have those heterogeneous environment. So now we're expanding our telemetry to cover those outside data sources. So yes, so -- and we also see there's a lot of other start-ups and company out there that they probably take an approach that's looking from the outside. And so the way we are uniquely able to bring the power for Datadog but also integrate with those external data sources have shown that we can simply generate better outcome and results.

We'll be more right with the data we have and we -- for which we have more -- or I would say more higher resolution, more reach, et cetera, et cetera. If we took out the whole stack, we can be more right, which is also why that's where we started. But I will say, when you look at where the market is today, the market is very active today. The market right now is you have an issue and you [indiscernible] and to be honest, you can get a pretty good result if you ask [indiscernible] to do that. It can ask a number of different systems. So for that particular thing, I think it's okay. For where the market is going, which is you want to be [indiscernible] proactive and you want to prevent issues that just doesn't work at all because the data doesn't flow through [indiscernible] different systems. [indiscernible], back to autonomy in self-driving. So after [indiscernible] as today, you can send the pictures to ChatGPT and it will help you tell you who was right or wrong, but the crash happened. But ChatGPT is not going to drive the car. You have a separate [indiscernible], you have a separate everything. And I think the same happens is going to happen to us with observability. As we get all of the data as we control the data plan for that as we can run -- develop and run models live on all that data, we'll be in a position to get in front of the issues and prevent them.

Yes. And in terms of the customer adoption, as I mentioned, we have 2,000 customers. So the product is still fairly new on general availability. So we're in the process of getting -- make sure customers can let us use their names. But what I can share that our 2,000 customers is widely represented in all kinds of [indiscernible] and verticals and geo locations from the largest Fortune 100 companies to the most innovative AI start-up. So there is a fairly broad non-discriminative adoption of this technology. And this is why I'm personally excited about SRE is really a very strong well-fitted use case for AI because of the result is in [indiscernible] and verifiable. So this is why we're seeing such a rapid increase in adoption.

Then finally I just mentioned that our pricing is completely transparent. You can go to our corporate website, and I believe that Bits AI SRE $500 per 20 investigation, right, Kai? So but you can check out all of that stuff by yourself whenever you want. All right. Megan? Megan's side

It's Keith Bachman from BMO. Tim, I also wanted to direct this to you is how do you think about the boundaries within security about where you want to be in terms of portfolio expansion and talk about some areas of interest or where you don't want to be? And the reason I bring it up is to your slide deck, you're still a relatively small part of generating ARR for Datadog. And some of your competitors have a much broader portfolio [indiscernible] a consolidation game. So in some measure of success begets success. And so I'm just wondering about how you think about portfolio expansion to try to get deeper penetration within your existing customers?

Yes, something I think about daily, hourly, minute by minute. One of the things that leads our thinking obviously is where there's existing mature spend. And we believe, as you saw in my overview of the portfolio that we are well positioned to go after areas of established budget, established spend. They also have a lot of established competitors. But then again, as you probably heard me say during my -- repeatedly in my piece, I think we do have an advantage because of the fact we have this unification in the platform, which really pays off when it comes to incident response or is being more proactive with security overall. So right now, in the markets we're in right now, we see a lot of runway to go after that. We see the advantage that we can bring for differentiation. And we also see a strong ability to pivot off of our existing relationships to get into those security conversations. Now even within those areas, obviously, with AI and agentic, there's going to be new areas, new services that we'll be looking at because it's a new set of problems for security teams and for the enterprise as a whole. Outside of that, obviously, we'll look at where it makes sense to expand where we bring that advantage we have with our platform.

Great. Eric side?

Ittai Kidron from Oppenheimer. And my question is to you, Alexis. Your presentation was quite interesting, and thanks for making the case that frontier models. I guess, can't scale or do what you do without going outrageously cost or ineffective giving the results that deliver accuracy. I guess when I look at your business, you talked about the future massive feature set that you have, the domain expertise and your ability to take a small model, right, and make a much better use of that in your business. I guess if we try to flip it on the upside down a little bit, the question is if you take $750,000 to build a small model that delivers much better accuracy, how do you think about the barrier of entry from third parties into your business. How do you think about the risks? I mean, the opportunities are clear. The revenue is clear. What, in your view, are the risks of AI to your business?

I think the -- this is where the data advantage, I think, is clear. And it's -- I think on the -- so in the case of our [indiscernible] model, it's just -- we just have a volume of data like legitimate real data that's just not publicly available. So that's one edge. I think in the case of the sort of the training of the agents, it is the -- both the volume and the quality of evals we can build, and we have that I think differentiates us. So I think there are obviously other companies trying to build small models. They don't -- there's no -- I don't think there's a clear financial barrier of entry, but the quality of the data you get, I think that's the real moat. The -- one of the issues, and we see is for instance, generating synthetic data in our domain, it's not terribly easy. It's not like you can basically sample what's out there, text to image and then you remix and create something plausible. It is much more if you will, the relationship between the way software is built and the observed behavior, it's much more intricate. And so you can't just go to one of the providers and say, okay, I want synthetic data. I want tons of it, so I can train my sort of small models. So that's where we retain advantage. I think the other piece is what Olivier alluded to, which is, look, we just sit that data, we get it for free as anywhere for training purposes, right? Because it's used for by our customers in the day-to-day. And that's where someone else just isn't sitting in that flow. They have to require that flow somehow. They don't have the scale to do that. So it really -- I see it as a positive flywheel that we get data, we get more and more data. We can get the SRE agent to generate more and more evaluations, which makes, hopefully, Datadog more and more valuable. So it's really accretive in that sense.

And remember, we -- so we spent $1 billion in R&D last year, maybe between $700 million and $800 million the year before and then another $500 million the year before that. So spending all this money is why it costs us $750,000 to train the model.

Great. Megan side.

Sanjit Singh from Morgan Stanley. I wanted to get the team's view on how fast are we racing towards this vision of autonomous operations. Like what does this look like a year from now? What does this look like 3 years from now? And in terms of executing is that vision, are there other pieces of the stack that Datadog needs to own? Do you need to own the software delivery pipeline to really execute on this and that gets into a build versus buy question. So just on the -- in terms of the race to autonomous operations, what does that look like 12 months from now and over the next couple of years?

Yes. I think it's really hard to tell. If there's one thing we could tell with AI is that the rate of progress is very surprising. So you get these big jumps of capability. And then looks like things are sort of stagnating a little bit. And that when you're ready to write it off and it starts jumping again. We've had a jump recently just 2 months ago in the quality of coding agents, for example, that made a very notable difference, at least in our usage internally and what we can see from our customers. So I think it's smart to tell whether we get there in a year or in 3 years. But what's pretty clear is that we're going to get there. Like the problems are getting solved one by one, the technical approaches, look like they're working. So we're going to get there. In terms of the moving pieces, we had identified a few areas that -- where we thought we needed to move faster. So one of them is future flagging and experimentation. We were not all that interested in future flagging and experimentation a few years ago because we thought future flagging was a bit of a commodity itself, and we thought that experimentation was more on the surface, more something that related to AB testing, button colors and things like that, which is -- it's interesting, but that was not our core business. I think we've changed our minds quite a bit on the topic as we understood that this would be a key part of automatically shipping and iterating on software. So that it could really make use of the productivity gains you get from the -- on the coding side from the AI agent. Another example is data observability. We thought also this was an interesting market, but this was a little ancillary to what we're doing. Now that this becomes one of the key limiting factors or the data quality and timeliness is one of the key limiting factors for building and deploying AI models is also something that is -- that has boiled up to the top of the list for us. There's a few other areas we're thinking about, but I'm not going to tell you about it.

Can I add some more comments that -- what I've learned about autonomy for Dev, Ops and Security is not a 0 to 1 game. This is very different from putting trucks on the highway without the driver is absolutely 0 to 1. There is nothing in between. What we're seeing is the adoption of this closing this autonomous DevOps loop is the evolutions of technology and human behavior. So we start partly with the investigation piece. Initially, a lot of the customers don't trust the result of the investigation, and they verify that. But now as they use it more, they start to gain trust. Same thing was pushing a code fix. Most of the people still are very comfortable absolutely requiring people in the loop but as more confidence get built and the technology to get more mature, we would expect customers to get more comfortable. And really, the holy grail is proactive, preemptive, predictive detection because this can truly move the needle toward autonomous operation because we detect and fix issues before they even occur. So what we're trying to do is to demonstrate tangible progress along this circle to increase customer trust, improving the coverage and rest of the technology, I don't think this autonomy is going to happen in a 0 to 1 fashion. It's going to happen in this partnership between technology evolution and our customers comfort and trust and culture evolution.

Thank you. Eric side?

Eric Heath here at KeyBanc. I wanted to come back to maybe bring your own cloud and understand that opportunity a little bit more. Can you just talk a little bit about who the addressable customer bases for this, maybe the timing of -- when do you make this product more broadly available and the go-to-market strategy around it?

Sure, I can start with that. So maybe to your question about timing, this is something we're already previewing. So we have customers actually leveraging those -- that solution bringing your own cloud, we have [indiscernible]. And the idea is to go and go to those markets where historically they potentially could not leverage Datadog. I was talking about like data residency, like some markets require the data to stay in that country. And that's like for those that would be interesting to have the technology inside their infrastructure and then be able to leverage it from there. So this will be some of those opportunities we extend, I think would be different geos that can look at it, but also industry where you need to have like more compliance and you need to keep some of the data inside your environment. So that's fully wide from like geos to different industry and and we already have been customers, and we're building more and more like from logs to other type of cemeteries that we're actually building on the [indiscernible].

There's another type of customer we're targeting with that, which is the [indiscernible] customer. When certain customers want to make use of infrastructure they already have or they want a licensing model that is more favorable to them than the SaaS model, and that is something that we are addressing with [indiscernible] so we see some of these customers coming to us.

Everybody wants that SaaS solution and feeling for their users, right? But making it affordable when you have 30-plus petabytes a day is tough over the wire. So they're already finding you.

Megan side?

Fatima Boolani from Citi. My question is just around the Bits AI suites. I can appreciate that is the gateway drug, so to speak, to the autonomous vision. But just taking a step back and maybe asking a more pointed question. You all are very excited about the code security that you can absolutely get confused right out of the gate. But [ OPUS 4.6 ] and the [ Codex 5.3 ] iteration, I mean they are absolutely coming up with relentless capabilities around code security inherently. So I'm wondering how you create a protection barrier to the value you're providing to customers and where that competitive edge is vis-a-vis the type of code security, hygiene and rigor you're providing vis-a-vis the context from your platform versus the general purpose LLM who could maybe have a broader coverage around code security because to your observations, the coding assistance are only going parabolic.

I think it's an advantage myself, like I don't think it's us versus them. Any way like having the LLMs be really good at creative thinking and ideating around these things is fantastic. But one advantage of our code security is that we can see how that code is deployed in production. So for [indiscernible], for example, it might find that there's malicious packages or vulnerabilities and packages but you don't know if that package is actually deployed in production. So you might pull a fire alarm and have everybody waking up and then it's actually not even deployed in production at that version, right? It's not a real vulnerability. So I think these things can work in conjunction as we say. We're using all these technologies in the appropriate ways to inform, but I think there's still something unique to bring to the table, there is my opinion.

Yes, I don't think we're going to see need of defense in depth. But clearly, we should think about and also understand how far in the left now with coding agents we can solve for a lot of the security problems. But to Michael's point, there's a lot involved, there's a lot of complexity in these production runtime environments. And that's what you're going to -- that's not going to go away. And there's always going to be this need to understand for something that has been found to degree, for example, a vulnerability, is it actually something that's not only being loaded but is it actually being executed. And that's the area that we'll continue to focus on, even with the [indiscernible] adoption of coding agent.

It's not [indiscernible]. And again, there are a few examples of ways in which this breaks down. It's also because the code at the time you wrote it because it was secure at a time, doesn't means it's still secure 2 weeks from now. So there is something that needs to be reevaluated [indiscernible] permanently. There are some things that Claude might think is secure, but you, as a company decide is not. And so you might have your own rule, you manage your own tech and everything. So all that to say that there's going to be a lot of room for a lot of specialized tooling that is going to complement the general purpose coding agents. And -- but definitely one, that tooling might use some of the same [indiscernible] and two, the agents are here to stay and they're going to do more and more. So it's going to be the question of working with them and complementing them, not trying to replace them.

And can we produce a good product [indiscernible] told you why would you build a monitoring company for cloud software when the clouds will probably do it, right? Here we are. [indiscernible]

All right. Ryan?

Ryan MacWilliams, Wells Fargo. It might be early, but I love to hear about the differences in monitoring an AI agent workflow versus monitoring a normal SaaS application? Do AI agent workflows acquire more data intensity and more logs that are required to monitor? And maybe more observability across the wider surface area, love to hear what you're seeing so far?

There's a lot of recursion and uncertainty in one, what the agents are doing. It's changing a lot. Even internally, we're always experimenting with different [indiscernible]. So it's a very volatile area. And so it does require some specialized tooling. Also the fundamental testing dimensions of quality assurance, verification that it's good and validation that it's working appropriately and a good product, right, are a little tougher when you don't know what it is doing, and then you put it out there and see how people are engaging with it, and what it's doing. It requires a different feedback than when you can write deterministic software and test it in pretty predictable ways. So I think it does require some new things. That's why we have playgrounds and sandboxes and experimentation and why experimentation really became important for the major research labs and foundation model providers, all them are big experiment [indiscernible] foundation users because they don't know exactly what it's going to do in production.

But it's super early. So I'd expect there will be a lot -- we'll have a lot more clarity about this space in a year, in 2 years, 3 years. It's so early that right now, the companies that are building agents are on the leading edge. And so we're all learning together.

Megan side.

This is Arti Vula from JPMorgan here for Mark Murphy. Olivier, anyone who wants to chime in. A couple of days ago, you guys talked about one of the largest AI foundational model companies adopting Datadog, consolidating open source in-house hyperscaler solutions. We spoke with another AI company that said that your platform was critical and they couldn't replicate it if they wanted to. So can you just help us understand that journey that some of these really highly innovative companies are taking where they come to the realization that they can't do it themselves. They don't want to do it themselves. Is it the breadth of capabilities, the fact that it takes more resources even with developers than they think it does? Is there like an aha moment for them?

I mean it's been the story of the company, I mean, since day 1. And these AI companies are not any different from the cloud native we're serving initially or from the larger enterprises, we started selling after that. They all have some mix of homegrown and some various tools they bought in the past. It's always -- it never works quite well enough. It's always a time sock. It always becomes a big issue at some point because keeping your systems up and right and safe and keeping shipping software is an absolutely business-critical need and you absolutely have it -- have to have it nailed down and it breaks and then it causes some questioning of what you're using. folks realize that they have other problems to solve to be competitive than to reinvent something that they can buy and they just typically buy what we do. So the question is not, like, look, if the biggest companies in the world try to do that as their sole focus, could they do it? Maybe maybe not. But the point is they're doing other things. They have to do other things, and there's no point in them building their own monitoring, their own observability and their own autonomy.

Great. All right. Eric side.

Kingsley Crane at Canaccord. So you've used Datadog to help observe and build Datadog in the past. How do you think about observing your own agents? And in solving that recursive challenge, does it help you build a better agents console and be the best product on the market to help customers observe forms of agents?

So yes, but we have to be careful because when the field is brand new, we are not every other company. So the one mistake you can make is to mistake yourself from the customer. And the way we learn is by speaking to as many customers as we can. Then we do through the product, we need to make sure it needs to make sense to us, too. It needs to be amazing for use cases. But just because it works well internally does not mean it's going to be the right product for customers.

Great. Okay. This will be our last question on Megan side.

Howard Ma with Guggenheim Securities. I wanted to ask about the perceived threat of open telemetry and other forms of open source observability tools and -- or I guess, open telemetry being more of a standardized protocol. And what is Datadog's competitive moat while embracing these open source standards? And specifically on the back end, I'm curious how defensible are having 1,000-plus integrations and the ability to correlate lots of different data sources in the way that Datadog does it, that's different from others. And from a coverage standpoint, you had a slide that shows monitoring virtualized environments on one end. So going more on-prem in one direction and then GPU monitoring in another direction. I mean it really -- is the right way to understand it that you want to address highly customized enterprise needs out of the box, and then that is really the true moat.

The collection has never been the moat, right? We -- when we started the company with Galaxy, we thought we decided everything that's on the server side, the SaaS is going to be the smart. And then the -- what leads on the customer environment, like the agents and everything else and the collections and the integration is going to be open source. And our agent and everything that came with it is open source. It's actually very premise license is Apache. Is it still Apache...

I think so.

Yes. license. But we didn't change it at the very least. And by the way, early on, our competitors were using our agent, and they were using our integration and everything else. Today, we're very happy to see OpenTelemetry come up. This is -- we are open telemetry native. It's great. It's a great way to get more data into the system, make it work faster, reduce friction. I think it makes everybody happy. It's never been the differentiation. When you talk about having a tight integration, the question is not, can you plug into the system and get that out. The question is, how do you -- how well do you understand that? How well can you use it? How does it come together with the rest of what you have? And whether or not you're using OpenTelemetry or some of its predecessors because they used a few different standards before that, that part is what is fairly unique to us, we're doing much better than anybody else.

And maybe just to add on the OpenTelemetry side, it's not really competition in some way because we are like a big contributors to open telemetry. If you look at Datadog, we're like the top contributors of hotel, and we have like now we fully support open telemetry, and we're like supporting like any type of data will come the same way being OpenTelemetry or own agent. So that really for us to Olivier's point, how the data is coming is what we do with the data internally, which is more important.

Great. Okay. Great. Well, this ends the first half of our session. So we are going to take a 20-minute break. That means you'll be back here at 3:30 to start the second half. Thank you. [Break]

All right. Welcome back, everyone. Let's kick off the second half of our Investor Day. I'm pleased to welcome Sean Walters, our Chief Revenue Officer, on to the stage.

All right. Good afternoon. My name is Sean Walters, and I've been with Datadog for 7 years. I lead our global sales team. I'd like to start by describing our go-to-market motion, how we're organized and how we've expanded our capabilities over time. Our go-to-market motion has 3 main parts. The highest volume part of our go-to-market in terms of net new logos is our self-serve market. A lot of other software vendors only focus on enterprise use cases. So for them, there's only a finite number of accounts to go after. Whereas for us, even though we have well over 30,000 customers today, we believe that there are a lot more new logos to go get. We will see self-service customers who start trials with us, visit one of our demo booths at a conference or otherwise indicate their interest in Datadog. And if they've contacted us in any of these ways, it's a fairly warm lead. So we're going to check in with them quickly with our commercial team. The commercial team is a heavy outbound motion. They work on those warm leads from self-serve customers, but they're also doing a lot of work on prospects, proactively reaching out to see if Datadog can provide value. And commercial is a velocity logo engine and often lands logos with a small amount of dollar value, but that's just fine because we are a land-and-expand model, and we have the opportunity to grow with these customers over time. While the focus for commercial is landing new logos that are smaller, some of these companies achieve great business success and become very large customers with big cloud footprints and a lot of Datadog usage. In fact, 24% of our top 25 customers are commercial customers. 50% of our $1 million-plus customers are commercial and 72% of our $100,000-plus customers came from the commercial business. So that's really the power of the land and expand motion at work. The third leg of our go-to-market strategy is the enterprise team. Enterprise account executives will stay with the customer throughout the relationship, working with all the teams across Datadog to support and achieve success with that customer. We deal with enterprise customers with increasing sophistication, and we've built our capabilities over the years. So I'd like to dig into some of that. First of all, there's our strategic enterprise team. This is our typical enterprise sales motion. And it's a full life cycle motion. We're doing outbound pipeline generation. There's lead gen through corporate marketing as well as account-based marketing, events, referrals and many other efforts. If we get our foot in the door, we apply a custom selling methodology that guide sellers through qualification, technical selling, trial and evaluation, champion building and all the things we need to do to build the confidence and the value that Datadog brings. Once we land that customer, the enterprise rep will continue to develop that relationship alongside other teams, including customer success, post-sales support and service. Then there's our majors team. These are our largest existing customers. Our reps here are focused on increasing usage for our current use cases, but also getting more strategic with the customer by going wider across departments, personas and use cases and solving multiple technical and business problems with our platform. Finally, there's our key accounts team. This is a more recent bet that we've been making to enlarge new customers that we haven't yet engaged with. These customers may have a longer sales cycle, and it may be more of a top-down motion than we traditionally go after. So we're focusing these reps on a longer journey. In the first year, these reps may have objectives like number of meetings that they schedule. But in the second year, their goals are shifted much more to revenue and closing deals. Our continued innovation is delivering capabilities that these sophisticated customers demand like enterprise class governance, access control and data security. And since they operate at very large scale with very diversified environments, our recent innovations like Flex logs, frozen logs and bring your own cloud play a very important role. This team made significant strides in 2025, including some faster-than-expected wins that are already expanding very rapidly. Our enterprise sellers go after the largest customers in the world, including companies in the Fortune 500. We are making great progress in penetrating this group, but we still have more than half the Fortune 500 to go, and we're working to engage them as they move to the cloud. And our median spend for these customers is still modest at less than $0.5 million per customer. So we think we have much more opportunity here. Compared to commercial, there's not as many new logos to land in enterprise, but the size of each customer can be orders of magnitude larger. As we have more products to sell and as we've grown in our ability to sell our full platform, we've seen the average size of our enterprise lands increase and particularly so in this last year. And if we do a good job developing the relationship and delivering more value over time, these customers ultimately expand from their land size. Here, I'm showing the average enterprise revenue per customer against that average land size. Finally, I'd like to talk about our technical services -- our technical support and services team. As a mission-critical partner, it's up to us to provide not only a platform that solves business needs, but also to help our customers establish best practices and execute on their learning curve with Datadog. We've developed a variety of services to support our customers in their use of us, including implementation services to get started with Datadog best practices and a customized plan; technical enablement services to offer training and knowledge building of the Datadog platform; premier support, an extra layer of support that is more individualized to the customers' needs; and technical account managers who provide guidance to enable and accelerate Datadog adoption and support customers in their journey with Datadog. Okay. So that's what our go-to-market looks like. But we've been investing in going bigger and deeper over time. So let's talk about some of that. First of all, we've been bulking up our capabilities in channel and alliances. First, the hyperscalers. These are very important partners with us. We're seeing more and more synergies over time. We're always working to improve our relationship, our co-selling motions and our technical partnerships with them. Then there are the resellers and other partners. In areas like Latin America, Korea, Japan and many other regions, we're working with resellers and others is critical to our success in those regions. They have customer relationships, technical capabilities, service offerings that make working together a really productive thing to do. So we're seeing a lot of success with these folks. And then finally, the system integrators. We're not a service-rich product, which is good because customers don't want to spend a lot of money on ongoing professional services. So with SIs for us, it's more about aligning to strategic initiatives. We've signed some really exciting partnerships with SIs, and we're building out those programs. With hard work over the year, we've seen meaningful growth in channel alliances in Fluence business, but we have a lot more opportunity here. Another area of growing investment is security. We started our security and the typical usual bottoms-up sales motion. This works well with our observability users who can be champions for our security products or if they're in the DevSecOps organization and part of the purchasing decision. We also started from the bottoms up because we were working on product readiness. The best salesperson for our products is the products themselves. That's why we invest so much in product. because when the product is ready and delivers value and makes the selling motion efficient and effective. And these days, the product is broadly ready across our security platform and particularly in areas like SIEM. A couple of years ago, we started adding to our security go-to-market motion, starting with a small number of sales engineers. These are our technically skilled staff who demo our products to customers. By specializing, they develop experience in showcasing our security products to security personas. About a year ago, we began hiring folks in channel and alliances to activate and build partnerships with our security channel. We're on our way there, and we're learning about how we go to create win-win partnerships with these partners. Recently, we decided to start a pilot of security-focused sales teams. We want to build on our security successes so far. With the product ready to go and competitors in this space consolidating, we are excited about our opportunities. Here's an example of an expansion with security. This is one of the biggest cruise lines in the world. Over the years, they have grown to be a multimillion-dollar customer with us. They chose Datadog as a strategic security vendor. As they believe -- they also believe that their unified observability and security approach is essential to maintaining operational efficiency and minimizing downtime. And over the years, they adopted nearly every product in the Datadog security stack. Recently, they told us that their on-premise SIEM environment was causing problems. It was hard to manage and scale and security investigations were taking way too long. Last summer, they chose us to replace this on-premise SIEM with our cloud SIEM to get faster detection and investigation at a lower cost. Today, about 1/4 of our business with this customer comes from security, and we have more security opportunity with them. So this is a great example of our potential with security. All right. So that's security. I also want to talk about investments we're making to expand our presence geographically. A few years ago, our sales teams were relatively concentrated in places like U.S., Ireland, Singapore and Japan. But as we've grown, it's become important for us to place sales teams in more places so we can literally meet with the customers where they are. So in places like Brazil, Mexico, India, Australia, Middle East and many other countries, we're putting people, channel and alliances partnerships, local language marketing and our investments in place so that we can capitalize on these opportunities. I'd like to give you an example of how these investments are delivering for customers regionally. Our Latin America go-to-market teams are pursuing opportunities with grit and a team mentality. They are persistent and patient, and they are delivering. We are winning some of the largest companies in that region from banking to e-commerce to retail to telecoms. Today, our business in this region is 5% of our revenue, but they're growing far faster than our overall revenue and the pipeline of opportunities in this region is great. And finally, of course, AI. As you heard in the first half, our product teams are delivering AI innovations across our platform. And our customers are focused on their AI efforts. It literally comes up in every single meeting we have. As you heard from Yanbing, FIT AI SRE is ready and is delivering tangible value for customers now. We just had our sales kickoff, and we are going all in on AI. Every seller has received training and is ready to have that conversation. And as we move forward in time, the preview products will go GA, and our sellers will have an even broader suite of AI capabilities to sell. So those are just a few of our investment areas. We keep experimenting and expanding our go-to-market capabilities to match the innovation of our product teams. I'm extremely proud of the sales team we've built over the years. And here's the evidence that our hard work is yielding results. This shows our bookings by year, including a stellar 2025. I've never been more excited about our opportunities and our potential to execute, and we are just getting started. Thank you for your time. I'll hand it off to Adam Blitzer now to talk about how we deliver customer value.

All right. Thanks a lot, Sean. Good afternoon, everybody. This is the part of the presentation where we have back-to-back pocket squares. So we hope you enjoy it. My name is Adam Blitzer, and I'm the COO here at Datadog. I've been with the company for just about 5 years now. I'm involved really in all aspects of our go-to-market. I get to work with some of our largest customers every day and also get to spend time on our largest, most strategic deals. I want to focus our time right now on why our customers choose Datadog, how we solve their problems and deliver value and why and how they continue to grow with us. So let's go ahead and jump in. This is the observability market. It is dynamic. It is large. It is fast growing. And there are many options in this market. It is a competitive market, as we talked about earlier. And vendors stake out different spots within the market in terms of their pricing. There are premium products, there are sort of middle-of-the-road products. There are commodity and siloed products, and there are even open source products. And as you know, Datadog is a premium product. So given that there's always been a rotating cast of low-cost and siloed commodity products, why do customers overwhelmingly continue to choose Datadog and continue to grow with us once they've made that decision? Why do we keep gaining market share? Well, we've always focused on delivering value to our customers as our North Star. And a key way that we do that is through our unified platform, a single pane of glass for observability and security. Now platform has always been our DNA. You saw this slide earlier, but I just want to highlight, we started with a rock-solid foundation, a unified platform. In fact, we started work on the integrated platform before we ever launched our first product, which happened to be infrastructure monitoring. We made an early bet on DevOps, and the idea was that breaking down silos between teams would empower them to solve problems more efficiently than ever before. From there, we really let our customers guide us and really have a customer-driven road map. And we look for places where we can break down silos between teams, between data sets and continue to deliver mission-critical observability and security applications for our customers. So let's take a look at how that plays out in action. This is an example of the single pane of glass. This is one of our customers who is one of the largest technology companies in the world, and they have tens of thousands of software engineers. Now prior to Datadog, they were using a whole host of point solutions for observability. Now you can see by standardizing on Datadog, it's allowed them to see all of their telemetry data in one place. So no more swiveling between screens and applications, no more painful correlations between sets of data. In a single year, they saved thousands of hours for their SRE teams or engineers that are responding to incidents. But what's even more interesting is they saved over 100,000 hours of time across all of their other software engineers who normally would have to have downtime during an incident. So instead of twiddling their thumbs, waiting for something to be resolved, they can focus on innovation and delivering new products. That's an incredible return on investment and really is the core value proposition of the unified platform. So while the original benefit of that platform was all about productivity and speed for our users and for our customers, one other really interesting trend emerged over the past few years. And that was that buyers really sought to consolidate on platforms. So instead of using a different tool for sort of each possible job, they saw that they could gain immense buying power by consolidating onto a best of suite onto a true platform. Here's an example of that playing out with a European home improvement retailer. They were struggling with incomplete visibility, alert fatigue, long incident resolution times and high operational costs. By standardizing on Datadog, this customer estimated that they saved over $10 million. Now some of that was indirect software costs. Some of that was in engineering time. Some of that was in customer experience. But we're seeing this trend more and more. Customers that consolidate on Datadog save in many different ways. So the unified platform has led to productivity gains. It's led to direct cost savings. But we think this becomes even more valuable in the age of AI. Customers who want to make use of agents for observability, for security, for software engineering, find it much easier to do so when they have their telemetry data and their security data in one place. And it doesn't matter if the agents that they're using are Datadog zone agents, as you saw earlier, their own custom-built agents that they want to run on top of observability or third-party agents from other companies. Now as our customers have grown with us over time, we have found many, many ways to deliver them more value and more purchasing power. So let's take a look at a few of those. This slide really shows how our economic model works. It's very, very similar to the cloud providers, which essentially all of our customers are familiar with. We have volume-based pricing. But as our customers grow with us, we bend the curve of cost for them. So they continue to get more and more value, but more and more leverage for that value as their volumes grow. We give them discounts for the amount of commitment that they make to us. We give them discounts for using multiple products, multiple combination of products. We give them discounts for term length. So the more a customer grows with us, the more we're going to bend that curve over time. We also work with them to optimize their usage, of course, of Datadog. We want them to use us in the best way possible. Since we're a usage-based product, right, we're only generating revenue when our customers use us, but we want that usage to be as valuable as possible. And if we find any optimizations to make with our customers, we'll work with them on that, and we find that they then invest that back into other areas of usage. But they can also use Datadog to optimize what they're spending with other software packages or with the cloud providers themselves, and we'll get into that in a bit. And then the last piece is we're constantly delivering innovations. So maybe the way we store data, different products that we launch that have novel pricing and packaging. You heard about some of that from Iris earlier today, and we'll get into a couple of examples. But the key thing to remember is that we are along for the ride with our customers' growth. So we scale with our customers. And as long as a customer is growing and their technology footprint is growing, their observability spend is also going to grow. Now we want to, again, bend that curve for them over time so they get more and more leverage and more and more buying power, but we're along for the ride with our customers' growth. And sometimes that growth can be quite exceptional. This is an example of one of our AI native customers. They have experienced tremendous growth in a very short amount of time. And you can see that their footprint with us has gone from using quite a few products to quite a few more products, but the really rapid ascent of the ARR that we're earning from this customer. And you see this pattern play out. As customers grow, their business grows, their revenue grows, their Datadog usage is also going to grow alongside it. Customers also scale as they adopt more products. This is an example of one of the largest online sports betting companies in the world. Their entire business is built on the cloud, and it depends on real-time low latency performance. So they find tremendous value in using our full platform. And you can see their adoption journey from product all the way to 21 products. And each time they adopt a new product from us, it isn't the same as using multiple products in isolation. It's really a force multiplier. So when they adopt a new product, it increases the value of all of the products they've already adopted prior to that. Not only are they saving money by consolidating spend with fewer vendors, but they're also unlocking additional capability and value. Now as I mentioned earlier, we also deliver cost savings and efficiencies to our customers as we innovate. [indiscernible] mentioned this one specifically earlier today. But this is an example from one of our tech customers. They've been a long-time logs user for us, and they adopted Flex Logs, which for many of their newer logging use cases. And for Flex Logs, in particular, they found that it really sharply drove down the unit cost of logs for them, and it allowed them to scale their log usage 75-fold. So we delivered more business value, but we did it in an economically efficient and business sensible way for the customer. We also launched new products that lead to very direct savings on both Datadog or on cloud costs or on other products that a customer may be using. So examples of these could be cloud cost management, Kubernetes auto scaling, et cetera. Now here's an example from a major software company that turned on our cloud cost management product and immediately found significant savings. So they were just really looking across 6 of their environments, of which they have many environments, immediately found $1 million in savings. And this is just an example of new products that we deliver to customers to help them with their spend in general. Now finally, I want to come back to the topic of us investing in innovation. We spoke about that a lot in the morning session, but we think of innovation as really the secret sauce of the company, and it's even more critical to us in the era of AI. Our space is incredibly dynamic and complex, and we have seen rapid technology shifts throughout the life cycle of Datadog. So first with the era of cloud and now in the era of AI. And our ability to out-innovate our market and guide our customers through change is a key differentiator for us. We take 30% of our revenues and invest it back into R&D. So that was more than $1 billion last year. We've always invested more on a percentage basis than anyone in our market. But not only that, you can see that this high percentage of investment, coupled with our rapid revenue growth means that, that compounds over time, and it leads to this very significant advantage in R&D spend. We're now at about 3x the R&D spend of our next closest peer. And we think that really makes us future-proof and that's what customers want to bet on in this fast-changing era of AI. You can see that our platform approach, coupled with a relentless focus on R&D investment has allowed us to deliver new products at a very fast pace. So if you look at this chart, it's interesting because our pace of innovation hasn't slowed down as we've grown. It's actually sped up. So how is that possible, right? Well, it comes back to everything being built on one platform. I have sold a lot of software in my day, and I've shown a lot of slides that said platform with arrows back and forth. But in Datadog's case, this is truly one platform that we're building on top of. And so it means every time we launch a new product, we're not starting from scratch, right? That new product is built on the foundation of all of the products that has come before it -- that have come before it. And it, in turn, adds back into the platform. So it creates this amazing virtuous cycle and delivers constant value to our customers. I want to talk a little bit about the cohort of AI customers that has really standardized on Datadog. So you can see 14 of the top 20 AI native companies are using Datadog. We have many of them that are spending more than $1 million a year with us, and the cohort itself is quite large with more than 650 of them using Datadog. And we've always been trusted by the market at large, but specifically by the most tech-forward companies, right? That's where we've really made our bones. As you know, cloud-native companies have overwhelmingly standardized on Datadog over the past decade. They pushed us from a technology perspective. They were using the newest tech. They had the newest types of architecture, and that all benefited our customer base at large. But what we're seeing now with the AI cohort is it's exactly the same thing, right? They trust us to meet them where they are and innovate rapidly as their space continues to evolve. And this innovation, again, will ultimately benefit all of our customers as they continue to meaningfully adopt AI themselves. All right. So that wraps it up for me. But with that, I'd love to turn things over to our CFO, David Obstler.

Thank you, Adam. For those of you who don't know me, I'm David Obstler, and I've had the great pleasure of being the Datadog CFO for the last 7 years. I'm looking out into this group, and I'm very gratified. I see so many familiar faces in the investment and analyst community. And I want to thank you for your support of Datadog since our IPO and your continued support. There's one other group I want to thank. You've seen Yuka on stage. Yuka is the tip of the spear on our Crack IR design and production group, and they work very hard, and I want to thank them without them, would not have had this day come off. Thank you, team. Thanks a lot. Thank, we only do this every 2 years. There's a lot of work. So let me start now. What you've heard today has been Datadog and its platform strategy in solving our clients' increasingly complex problems. And I hope you've come to understand that, that is only being enhanced by the advent of AI. You also heard from Sean and from Adam that we are broadening our go-to-market, making some investments there. all to create a broader and more uniform platform that solves our clients' problems and provides more value. And to use the word that we started on, all of this to try to increase the autonomy in the analysis detection and remediation of our clients' problems. And so sort of that's what you've heard. But I want to dive in a little deeper into the DNA of Datadog and what makes us grow, what are our opportunities that we see in front of us and how do we turn that into a profitable vital organization. So let me hit that. How do we grow? One of the most important slides that we've shown you from time to time is this slide. This shows our land-and-expand model at the center of Datadog. And as you can see, it goes back 10-plus years. And what it shows is not only do we continue to land new customers, but we grow with them over a long period of time. The first cohort here says 2015 and prior, and you see the growth of our relationship with those customers over time. And it's that which creates the engine that makes Datadog. Now within that, it is a combination of our business that we have from the previous year, plus the new clients we land, and I'll discuss that, and the growth of our existing customers, which makes up the majority of our ARR growth in any one annual period. Diving into that growth, about -- and we've discussed this before, about 1/4 of that ARR growth each year, plus or minus, it varies, comes from adding new customers, which is the engine for the future. And then the remaining approximately 3/4 comes from, one, our clients adopting more of the products that they've used in Datadog, the growth of the client's business and they're using more of the Datadog products and cross-sell, which is dependent upon the platform investment that you've heard in the previous presentations. And what that has produced is a rapid expansion of the customers we get, the land, 18% growth of customers over the years and the average revenue per customer growing 17%. And together, that produces a compound annual growth rate over this time period of 42%. And it's that land-and-expand model and investment in the platform that creates the sustainability of that that's been driving Datadog. Now what are the types of growth opportunities that make this flywheel continue to churn. So I want to go over some of those. You've seen in Ali's presentation at the very base, there's a very, very long secular tailwind from digital migration and digital transformation and cloud migration. And this is a slide that we've shown you, which is upward sloping. It's the investment in cloud spend. And that is -- and we've talked about this many times in our investment meeting. That's the bedrock in that there is a very long-term trend, and it's got a lot of legs to it. Then on top of that, we believe that there'll be an accelerant from the adoption, and we spent a lot of time in today's presentation of AI, both Datadog for AI and AI for Datadog, which is driving up the speed of the creation of software and the cloud transformation. And we're seeing this in a number of ways. You've seen this slide. This is a slide about the spending on AI, which everybody reads about all the time in the press. And how is that working for us? Well, we are having signs that, that's translating into greater and broader platform adoption. In this case, this is our observability of LLMs, which through our LM observability product is beginning to take off. We have many customers using it. And just in the last year, that has increased 10x. That's the span sent to us to our LM observability. We also are dramatically increasing our integrations and another piece of evidence that's starting to spread out throughout the customer base is the number of customers that are using those integrations. And we just talked about this in our public release. 5,500, and this has been upward sloping of our customers are using integrations. And guess who those are. Those are some of our larger and more sophisticated customers because that accounts for about 80%, just under 80% of our ARR. So our larger and more trenched, more progressive customers are using our AI integrations. In addition, we've been able to service many more clients. This is a statistic that we've given out and we showed earlier that we're supporting our customers, which are AI native and becoming the platform of choice for them. And that number has grown substantially and at the end of last year was about 11% of revenues. So a lot of trends in cloud migration enhanced by AI. Now when you get down to more specifically, how is Datadog growing against that very positive secular backdrop, I want to speak about our growing and retaining customers. Even though we are a leader, we have a lot of white space to go. What we've done here is curate down from the millions of customers that the cloud providers have. We then have taken other buying signals like how much they're spending, how many hands do they have on keyboards, what are their projects to curate down to just under 500,000 global customers that we believe are target customers for Datadog. And of course, we've been growing our customer base quite rapidly over 32,000, but that's a penetration in logos of only 7%. There's a long way for Datadog to go. Now what we've been doing is accumulating those customers and growing with those customers. These are metrics that we've been giving out since we've become public. And this looks at our total customers, then our customers that are over 100,000, and you see we have over 4,000. Once a year, we give out our $1 million customers, which this year grew to over 600 because of the growth you've heard of from our other customers. But here's a new one for you. This is brand new, hot off the press, debuting our customers that spend over $10 million with us. And that here is the time series you see. It's been growing quite rapidly, over 60% growth, and that's now 34%. So this illustrates how we're landing and you've heard from a lot of the other speakers about how we're expanding our value with our customers. Now who are these customers? Now first of all, they are diversified -- in a geographical sense. Yes, we started in North America. But you've heard about from Sean, Adam and others how we've been making major investments around the world and enhancing our investments with complementary types of relationships and channel providers, et cetera. So even though North America is our largest portion, our international business is quite -- is growing quite rapidly. But I find the other slide here maybe even more interesting because when you think about observability of in cloud, we started out and you might think about software companies, technology companies, cloud natives. But we have a very diverse customer base here with major segments in those, but also in media and entertainment, financial service, travel, consumer, et cetera. And what -- this is indicative of what is happening in the digital economy. All companies are digital and becoming digital and cloud native. And we, as you've heard from a number of the other speakers, address that very, very big and broad and diversified market. Okay. Now within that, what's happening? We have very good customer references and proof points in a wide variety of industries. And some of those are what you might expect in the technology, the Internet, the software, but we also have 8 of the top industrial manufacturing 8 of the top 10 logistics companies, et cetera, 10 of the top entertainment companies, further proof points that our end market is very broad. And all of this together is coming together in value to maintain a very high gross retention percentage. It's 97% plus in the company. And not only that, it's 98% plus in enterprise, but in SMB and mid-market, it's 96% plus. And what does that mean? That's indicative of the high value that our customers place in Datadog and the stickiness of the product. Once you have your Datadog, it's tough to get rid of your Datadog and you don't want to get rid of your Datadog. So this has been fundamental to the economic model of Datadog and the value we bring to. Okay. Now moving on to some other things that are growth drivers is our expanding products and use cases, and you've heard a lot about that. This is at the core of what makes Datadog. And we were just looking -- when we first went public, we had this metric of 2-plus products, and that has settled in the 80s. And as the years have progressed, we've added more and more because of the adoption, more and more products here. And now you see we're out to 10-plus products because of this flywheel and adoption. We have some numbers, half of our customers have 4-plus products, and we have just 9% of 10-plus. And I would expect, as we continue to penetrate and consolidate, you'll see these continue to go up. And we have created a very, very strong business. We just announced this on our last earnings call of what the penetration of the 3 pillars or I think Yanbing referred to them as the 4 pillars because when you have digital experience get as large as it is, we are also calling that a fourth pillar within the application monitoring. And as you know, we've crossed $1.5 billion to $1.6 billion in infrastructure and $1 billion in log and APM. Now while that's really good, that shows a lot of platform momentum. I want to show you, just like I showed you in the penetration of the logos, how much opportunity there is for us in our core business. Only 53% of our -- sorry, only about half of our customers use all 3 pillars. So we still have a penetration that's roughly half of customers who don't use the 3 pillars. And what we found is that when a customer standardizes on Datadog and uses all 3 pillars, they spend a lot more with Datadog. In fact, they spend 15x more. So half of our customers are not using the 3 pillars. And what does that produce? You see over time, we have a broadening group of customers who buy more products. And the ARR, of course, is very consistent with how many products they use. And so as you go out, this has been the consolidation and the growth opportunity that we've been realizing over time with so much more to go. Another benefit from this is as customers standardize on Datadog and use more of our products, they churn less. So this is a very important driver of growth in the future. Now outside of observability, which I've been talking about, we are also addressing new markets and expanding beyond observability, which you've heard about a lot today. Within observability itself, it's a very large market, a $30 billion-plus market. And we are the leader. We have the highest market share. And we've been growing our market share over time. But at the same time, as we've been addressing more complex client problems, what we've been doing is we've been increasing our TAM. And this slide illustrates what we've been doing over time in going from the blue of observability to add security, software delivery, service management, product analytics to get -- to expand our market to well over $100 billion. And these are all markets where we have products, we've made investments, and we're seeing traction. Now one of these, just to show the slide that we showed already is the security opportunity. It's one of those expansion markets. And you can see here that we've grown quite rapidly. We have quite a number of customers using it, and we announced 1 quarter ago that we've passed the $100 million mark. But there are a number of different vectors for growth there. So that's sort of going over from a little more of a quantitative lens, what are our growth opportunities and why we expect them to continue to be going. But of course, it's about getting the top line growing, adding value and also creating a strong business model. And so I'm going to spend the next bit of time on how we turn that revenues into profits and how we've been doing that over the years. The first place is gross margins. This plots out our gross margins over the last 5 years, 5, 6 years by quarter. And we've given guidance that we're planning our gross margins plus or minus 80%, leaving the flexibility to invest in additional platforms, data centers and products. But we've been really good about working on our platform to provide extensibility, cost initiatives. We optimize our own platform very well. And you can see we've been successful over the long period of time, plus or minus of having this 80% gross margin. You've heard many times because at the very center of Datadog, that we have been investing for a long time and are the leader in R&D investment. I won't repeat what you see here, but it's quite extensive. And that means we have been able to invest enough in R&D to maintain R&D as a percentage of revenues, and this has been our target around 30% and that has some fluctuation, and there may be some things about the use of AI that might be levers here, but we've been really good at investing methodically in R&D. At the same time, one of the reasons we've been able to manage the company economically and still invest substantially in the platform is because we are very efficient in go-to-market because of the frictionless adoption we talked about. And even though we've been investing at a very high rate and expanding our go-to-market, as Sean and Adam talked about, we still are much more efficient in how we do it than our competitors. That has allowed us to maintain a high growth of sales and marketing, yet keep that sales and marketing as a percentage of revenues in the roughly the mid-20s range. On top of that, we've been really efficient already in our own optimization and automation in G&A. And we've been able to run our G&A while scaling the company. I see a number of my G&A friends sitting here. Thank you for all the hard work you do there at around 5%. And the combination of all of that has resulted in us having an operating margin where it does have some fluctuation because as we've talked to all of you about, we set an investment plan. We try to set an investment plan based on the opportunities. We have a consumption model, and that changes faster than we can hire people when that changes. And therefore, there are some fluctuations, but essentially, we are trying to invest behind the opportunity while being -- having good financial management, which has resulted in us having increased scalability and margins that have been in the last couple of years in sort of the low to mid-20s. Now our financial performance here is summarized on this slide. I've talked about revenues. But while we've been investing substantially in the opportunity, we've been able to grow our operating profit at a higher rate and be good cash flow margin managers. Our cash flow margin, as you see here, last year was 27%. Our operating margin -- by the way, this is all non-GAAP. I wouldn't want you could to say anything. So it's all non-GAAP, and it was 22%. So we've been able to be good cash flows. We're not very capital intensive. That allows us to be very efficient in the conversion of profits to cash flow. Okay. Now our financial goals. We've shown this slide before. This is the same slide as 2 years ago, updated for the years. And I think what that shows is the consistency of the management of Datadog despite the fact we've been making substantial R&D and sales and marketing investments and scaling the company. And so here is a time series you can all take away. And there is a reaffirmation of our long-term target of an operating margin at 25% plus. And that's what we've said last time as well. We think we've been able to prove, if you look at this time series that we've been able to invest comprehensively, yet balance that in being a strong profit deliverer as evidenced by this time series. Just sorry, just a few other things to say. In capital allocation, we get asked this a good deal. We are strongly cash flow generative. What are we doing with all this cash? Well, one, we want to manage the company in a prioritized efficient way to grow over the long term our free cash flow, and that starts with our revenues. If you compound the revenues and you do it in a prioritized way, you will compound the free cash flow. We want to make sure we have the flexibility on our balance sheet to invest, and that includes within our own company as well as potentially in the M&A market. And at the same time, we've shown and we'll continue to maintain a thoughtful and disciplined acquisition strategy. So far, that's been focused, no surprise on products and adding technical capabilities to the company. And we've been very effective. Some of the products that you've heard about, you heard most recently about Eppo and you heard about Metapplane, these are product areas we're working on but enhanced through strategic acquisitions. And finally, our target in net share dilution, which is 2.5% to 3%. Again, we are trying to balance stewardship with being able to attract and retain the intellectual capital to continue to grow the company. And with that, I think I want to thank all of you for coming. Hopefully, you learned a lot. And we're going to repeat the Q&A session by having Sean and Adam come back up with Yuka and Ali, and we'll open it back up like last time to Q&A. Please direct your questions to Megan and Eric, who are in the aisles. Thank you very much, everybody. Great seeing you.

All right. Thank you, David. So we'll start another half hour of Q&A. Same rules apply. We will start on Eric's side. Please go ahead.

All right. Great. Thanks for a great presentation. It's Gregg Moskowitz from Mizuho. A question for Sean or Adam. So 2025 saw a big increase in Datadog account execs covering major accounts and key accounts. And you mentioned that in year 2, their objective shift much more towards revenue. Well, we are now entering year 2 for many of these folks. So how are you thinking about the likelihood of them driving much bigger land and expand with very large organizations?

Yes. I think with the advent of key accounts, the hypothesis a couple of years ago is really putting a lot of focus in those must-win customers and new logos for us was the way to do it. And so scarcity drives opportunity within those accounts. We've seen -- the first year was a lot of the groundwork, getting meetings, getting contracts in place. And what we saw last year was kind of that all coming to life. And so we saw some really incredible lands and customers that we had been chasing for a long time. And we're seeing many of them as they get on to the platform, grow from a use case and a product perspective, and they're starting to take off. So we're going to continue with that in 2026, and we expect -- we see a very strong opportunity there.

And we're talking about it today, so we are optimistic about it.

Okay. Great. Megan side, the right side.

Andrew Sherman with TD Cowen. Sean, on the 53% of customers using all 3 pillars, that's gone up a bit versus 2 years ago, but obviously still a long way to go there. What is usually the product missing there? I think it's probably logs. It's a highly competitive area. I know your business is now $1 billion plus, which is great. But what are some reasons why customers would buy that or not? And over the next few years, where do you see that number going?

Yes. I mean I think every customer engagement, every opportunity that we enter is different. And what's important for us and our sales team is to listen to what the customer needs and kind of the pains that they're exhibiting at that moment. So it's not -- we're not going to try to force 3 pillars on everybody. Everybody who comes into Datadog as a salesperson gets trained, like 3 pillars at the core of what we're focusing on to get people trained up on. We get them focused, but we get them to go in there and ask good questions to understand the challenges that our customers are having and then get started with maybe the 3 pillars, maybe some other products. But staying engaged with those customers, typically, we'll see the growth into the 3 pillars, and we're just going to -- we're going to keep doing that, whether it's logs or APM or DEM. The more the customers spend time in the Datadog platform, the more value they see from the solutions and the easier it is for them to add on more of our products.

Andrew, I wouldn't think of it as one particular pillar that's missing, right? Like there's -- every combination is represented there in some scale and has a lot of opportunity for us to work on. All right. Eric side?

Gabriela Borges, Goldman Sachs. I think this one is for Sean and for David. Really appreciated all of the customer examples you shared us with the ARR over time and the products on the bottom. What's curious about the charts is that they all go up into the right, but the pattern within them tends to be a little bit lumpy and you have periods of time where ARR may go down before it goes up. So my question for you is, what can you do to get ahead of some of those conversations to maybe initiate cross-sell? And what is the underlying driver of those temporary dips in ARR?

We lived through some pretty volatile times in the last 5 years. We lived through a period of very rapid growth. And then after COVID, as we talked about an optimization trend, then a stabilization and then a reacceleration. So I think that essentially, we have learned a lot. And what we are doing is we broadened the platform. We've gotten stickier. We essentially are working with our clients through some of the things that Adam and Sean can talk about with our account managers, et cetera, to get clients to use the platform in an optimal way. and to try to sell through the platform value, which I think we've gotten a lot better. So some of that volatility has to do with the end market and some of it has to do with our growth in helping become partners with clients over a longer period.

Yes. I would also say for some customers, it's very intentional account management work with them. So the trend with the customer may be very up and to the right over many years. But in a given year, the customer may have sort of exceeded the usage that they had originally planned for. And so we work with them to say, hey, as I mentioned in sort of the value presentation, we'll give them better terms or more discounting by them sort of committing to more over a longer period of time. So they may be sort of past what they expected. We say, "Hey, let's look at how you're using Datadog today." Let's do a longer-term commitment with you, and it might drop you temporarily from where you were. And then over the long haul, you'll step back up to a higher place than where you started.

And remember, it's -- we are a usage-based model, right? So if you are looking at a seat-based company, you'd have a perfectly smooth line into Infinity. In our case, we -- customers use more or less of our products. Usually, they use more, as you can see, over time. And we didn't actually cherry people that much, like every single account looks the same way for all of our customers. They grow with a little bit of choppiness. And we do see optimization on a regular basis. Sometimes we actually tell customers to optimize, hey, it doesn't look healthy. If you do something about in addition, we can help. Sometimes they're about to renew and they want to optimize before they recommit, so they have a better idea of what it is they need. So we see some of these contractions. And then they start growing again because we deliver more value, they grow on their end, they buy more products, et cetera, et cetera. So that's the motion we're going through. That also explains some of the conservatism we put in guidance because we're a usage-based model. So we're very confident about where we'll be in the midterm. In the short term, we do not know exactly how the usage is going to trend in 1 month from now.

The final part is our customers have different times when they're busy, right? Some of them are e-commerce companies and holiday is a big season for them, and then it should ramp down, right, in usage. Some of them are big media companies, they have specific events, right? And so that's the benefit of the usage-based model for our customers as well. And the reason why you don't perceive it necessarily in our numbers, right, all of that volatility is because with a diverse base of 32,700 customers, right, they're all experiencing their own volatility, but they're all experiencing it and with that broad set of customers, that broad diversity of industries represented, right, it doesn't tend to show up in the quarterly aggregate numbers.

One of the things that cohort chart, why it's so important is when you look at that and pair that with gross retention numbers that are so high, the clients stay with us. And then when you look at the length of those cohorts, they might move. They may not be a straight line up, but over time, they compound up, which is a very powerful driver of our business model.

Great. Thank you. Megan?

Yes. This is Peter Weed from Bernstein here. This is maybe a little bit of a question for Olivier and a little bit of a question for Sean. I think you've been telling a really powerful story, not just this year, but over the years about the coverage that you're getting across all of the different personas, both in the kind of operations function and in the product function. But maybe there's a different way of looking at this, which is when you kind of step back and think about like how you're serving the leadership of those organizations, the Vice President of Operations, the Vice President of Product and how that influences both product and commercial conversations that you're having, what's unique about Datadog's positioning where you could step away from being kind of trapped at something that's kind of individual persona and really kind of helping be that line of business application that helps these senior leaders succeed kind of almost regardless of the kind of individual tooling that roles on their teams might be using?

Yes. I mean as a sales motion, we're trying to go -- we have a very broad landing space, having a very large product set. So we're definitely selling across lines of business, across functions, across personas all the time. And not every one of them is right at the right time. So as a salesperson, it's a great thing to have because we can solve problems across. And then as the best seller internally for us is we get one person to be a champion at an organization and the product team, and they do a lot of selling for us. And that's usually what you see like that's why the expand happens so rapidly because you just see success in -- maybe we found the right person at the right time and the success of that person kind of bleeds into the other functions and helps us grow broader and having a broad solution set helps it happen pretty quickly as well.

And one thing I've seen recently from talking to some of our largest customers is, of course, Datadog started with SRE teams and they think about SLOs and service level objectives and all the metrics on how their systems are running. And really at the executive level at these companies is they've turned that around into like business SLOs, right? Like how is the business running? And so I use the example from a financial services company that's processing an incredible amount of payments at any given time. The business doesn't care that there's some latency in some system somewhere or there's some problem introduced. They care about what is the throughput of payments on the platform. And so they're using Datadog, which was really built for Dev and ops teams, but they're using Datadog to run the business in real time. And that's happening more and more with our largest customers.

Yes. We're leaning more and more into that into the user analytics, business analytics and making sure that the -- we are used to -- I mean, look, the company is in digital company is the applications running the business. So if you instrument the application, you instrument the business. And that's something we saw happen organically originally with our cloud native customers where the CEOs at Datadog on their desk because that tell them exactly in real time what their business was doing. Now we are building products for the rest of the market that is not necessarily cloud native to go through the same thing. Another way in which we make leadership successful is that we help actually implement transition, change, migrations, like adopting the cloud, adopting all those things that are actually really hard from a process and a people perspective. They are more used to buying software that doesn't get deployed or people don't use, and we never have that problem. And that's one way in which we shine. We help people shine like say, hey, we did this. We adopted this tool last year. We actually mentioned one of those customers in our latest earnings call, one financial institution in Latin America that started adopting us last year. And they had a pretty large deployment initially, but we are still a small fraction of the organization. And then what they did is they ran surveys like from their teams, like what do you use? What do you want to use? What is making you more productive? And they got like overwhelmingly positive responses from Datadog, met the executives who made the choices look pretty good. They made the right choices. They got their product use. They had a business impact. They had an impact in transforming the organization. And as a result, we could scale quite a bit with them. And that's a motion that we're repeating in many different places.

Great. Okay. On the left side with Eric.

Koji Ikeda from Bank of America. Maybe to continue the conversation and the question from the previous one. When I looked at the slide on the median spend of the F500 customers being only $450,000, I was like, oh, there must be a lot of opportunity there. And so digging specifically on that market, what are you guys doing to target those customers to expand that medium spend much higher than the 4500? And maybe digging a little bit deeper on the TCV bookings, $4.5 billion plus, it looks like in 2025. Maybe bifurcate the growth there, the TCV bookings coming from the biggest customers like the F500 and more of the mid-market size that are spending a lot with you guys.

Yes. I mean I'd say in the -- so the Fortune 500 customers, obviously, they get a lot of attention, whether it's an existing customer in our major accounts group, maybe they're an existing customer in the traditional strategic enterprise or key accounts. Maybe they're like not a customer or -- and we want to focus on them and make them a customer. They're very complex organizations and sales cycles are very long. So it's, again, working with the customer to understand and many times, they're in competitive contracts that are 3, 5 -- they can be very long contracts that we're working on. So we spend lots of time working with those customers in key accounts or other getting net new logos and landing where we can land and then working on that. But we're seeing more and more over the last couple of years, tool consolidation. Once we're ingrained within those organizations, we expect to see those will continue to grow with us as well. So there's lots of focus on that.

But there's a lot of opportunity to be out there. And in a way, you can say that for a few years, we were a bit victims of our own success in that the product grows really well once it's deployed with customers. And as a result, it was, I would say, a little bit maybe too easy. I don't want to say easy, nothing. A bit too easy to grow an existing customer from, say, $10 million to $15 million in revenue by adding more products, driving more transformation, et cetera, et cetera. But it's a lot easier to do that than to get 10 customers from 0 to 0.5 million. And by getting 10 customers from 0 to 0.5 million, you create a lot more opportunity for the future. And so we've made a number of changes internally so that our organization got a lot better at pursuing these opportunities and not just the so-called easy growth with some of the larger customers.

Great. On the right, Megan side.

Howard Ma with Guggenheim Securities. I believe this one is mostly for Sean, maybe a little for Adam and part in the multiparter, I'll try to keep it concise. It's on geo expansion. So as you expand into more geographies, I guess, number one, are there notable differences in certain geographies because you're making a pretty big expansion. India, for instance, we're hearing more about the offshoring more to India. I feel like there's less so Eastern Europe, more India. A lot of developers like do you have an opportunity to shift left more there, industry regulations, archiving, storing log data, for instance, like is that something to look out for? And then I guess last part is FTEs. Is there an opportunity to deploy for deploy engineers to, for instance, help organizations understand how to use B AI that they didn't have before and could -- sorry, really a multiparter. Last part is new logo contribution could that actually, it's really hard for a company of your scale to increase the relative mix of new logo contribution. So how big can the geo expansion play a part into that?

All right. So you're like -- this is a 2-part question. It's actually more of a comment or an opinion. I'll speak a little bit to markets being different from one another. The good news is that there's opportunity everywhere. It's just the observability market is large and growing incredibly quickly. One sort of proxy, it's not an exact proxy, but one way to think about opportunity for Datadog is where is cloud adoption taking off or where has it taken off? How mature is it? And largely where clouds have been successful, we have followed that. Sometimes we help people transition to the cloud, but also we're a very good choice for people using modern architectures and people who have standardized on clouds. There are markets in particular that are also interesting because in some cases, they've sort of skipped the generation of computing, right? So there's a lot less legacy and they're sort of -- either went all in on mobile, went all in on cloud, and those lead to interesting opportunities for us. To your point on regulatory concerns, that varies wildly by market as well. And so you've seen us make investments in data centers, partnering with the cloud providers. And a lot of that is about giving choice to our customers who might be -- who might have regulatory requirements from a geographic standpoint. They might be in regulated industries, and we want to give them that choice. And then we're also doing that from a product angle. So you heard this morning about BYOC or bring your own cloud. That gives us, again, more deployment options for customers that might be facing regulatory hurdles in different ways. So that was, I think, 2 parts of your 5-parter.

You can talk about the FDE. So forward deployed engineers, we start -- we now have 4 deployed engineers on staff, and they are useful in a number of situations. And I should say it's a term that's a little bit elastic like it's been used in many different ways in the industry. But for us, they're useful in a few different situations. One is customers that need to adopt AI and need to transform and we need to understand how it's going to apply to them. The second one is the new type of AI live type of customers that have large needs that are in an emerging area where we probably are building products that don't exist yet, and we do that by helping those customers in real life.

So I remembered one more part. So like on the India piece specifically, you mentioned offshoring. We see a lot of different opportunities in that market. But one of the interesting opportunities is a little bit less kind of offshoring of non-Indian companies, but to some degree, like massive cloud-first, cloud-native tech-forward companies being created in India every day. And the interesting thing about a tech company in India, especially a B2C company is if you get any traction, you have instant scale, right? So if you think about food delivery or streaming, anything that we interact with as consumers, when you apply it to that population, you just have massive scale. And so the need for something like Datadog becomes pretty pronounced. So it's an exciting market in the vector that you mentioned, but also in the vector of just interesting companies being created every day at massive scale.

One difference that I think has been pretty important in development is the importance of channels in some of these international markets. So that is true for Brazil, Korea, Japan in some ways. There are a number of markets where it wasn't enough to have a direct sales team. We had to also develop the channel relationships in order to have that work. And so that's been very important in a number of the international markets.

Okay. Great. All right. I think we're going to Eric's side on the left.

Alex Zukin with Wolfe Research. I wanted to ask a question about the AI native, specifically the adoption cadence, both from the types of products that they start with, the size that they're starting at and maybe the pace of expansion within that cohort specifically and maybe what you're seeing develop that's either the same or different and kind of how we should think about that going forward?

It's very similar, like the types of products adopted are very similar to the other companies. It starts with 3 pillars and then you get more into user experience, sometimes you get more into developer experience. Like there's a mix basically of everything that is being used in these companies. The growth can be a lot faster than other companies just because their consumption of infrastructure is pretty massive. But again, that also depends on the customers. Some customers are growing more slowly. Some customers have 2 distinct sides that they have one side where they do research and they train models, another one where they run applications and they use us on one side, but not the other. They have some homegrown thing on the other maybe. But we've seen both. We've seen some of these labs approach us for the live side and some other labs now approach us for the training side. So we've seen a bit of everything there. So...

Yes, I think they -- we created this distinction, and we own it, but they are essentially cloud natives. Fast-growing cloud natives, meaning they don't have legacy infrastructure on-premise. Therefore, they are using -- it's very important that they use a modern observability platform like Datadog. There's great product fit. They are using it for production environments, principally, so the pillars plus. And then Ali made the point that they're fast-growing cloud natives because of their demand environment. And so I think the major difference has been maybe that some of them are growing very fast, but they're operating otherwise like cloud natives. Ali, anything else to you?

Yes. We only caveat that they're not always -- in some cases, they're not always very cloudy. Like maybe they're going to consume infrastructure from like a single large data center or a couple of single data center that are nominally provided by cloud providers that are really single tenant, very specialized infrastructure for that we cater to that very well as well.

Great. Right side of Megan.

Fatima Boolani from Citi. I wanted to direct this to Sean and Adam. I was hoping you could opine on the real-world implications of taking Oli's and the team's vision of an autonomous self-healing environment to commercializing that with customers. So I'd love to hear your perspectives on how customers are reacting to that. And specifically if that would involve over the course of the next couple of years, a fundamental rethinking around how you are pricing the platform? And then as a related question for David, the question -- or excuse me, the slide with the customer cohorts and the growth in those cohorts. I'm curious if you can give us a little bit more detail on if the incremental profitability or profitability profile or the unit economics of a $10 million spender is materially different from a $100,000 spending customer.

I'll start with easier. So yes, we give volume discounts. But given our broad set of customers, fundamentally, and one of the reasons we've been able to maintain our margins the same at the gross is that we have a whole net set of customers coming in that are not using $10 million. So our weighted average has stayed roughly the same. So when you look at those cohorts and you basically make them weighted average for their ARR, they've roughly stayed the same despite the fact we're giving volume discounts to the largest customers.

I noticed before you started answering that, you said , do you want to start with the easiest question? It's not a competition, David.

Yes. I mean I would just say to the vision around autonomous sort of self-healing systems, when I speak to our most sophisticated customers and they draw up where they want to be a few years from now, that's what they draw up. So they draw up that exact vision. And I think it's a difficult place to get to, right? We're hard at work on it, but I think it just makes so much sense for the future of observability.

And do they want you to tell them how they should pay for these outcomes? We're absolutely moving to an outcomes-based sort of pricing modality for almost all of software. I mean, if we're not going to get there in a year or 2 years, maybe it's faster than that. But yes, they kind of want you to tell them how it should be priced or what sort of, I guess, negotiating leverage do you have in those conversations and scenarios?

I'd say right now, we're probably too far away to be talking about pricing models for a future state with the customer. But again, I think more of the customers are thinking about how could I achieve this with Datadog in the future. And again, we show our product road map around it. But I wouldn't say we're at the point where customers are thinking, hey, 2 years from now, how am I going to pay for my observability and...

We're not in the same situation than most other software companies that we don't charge per seat. We charge per usage. And our usage is typically related to some other fundamental usage our customers have, such as their usage of infrastructure or network or storage or something else. So it's -- I would say that the question of how the pricing model can work, I think, is a lot easier to solve for us. It doesn't mean we have a pricing packaging in mind just yet. I think it's -- we still have to see exactly what the shape of the product is and what the market will bear. But it's not a big shift or a big turn for us to support any of that. I will say the vision of the autonomy is something that resonates with customers, like they do want to get rid of these pains. And there's a big difference from when we stood here 2 years ago. Like 2 years ago, this was pie in the sky. Today, with the recent advances of AI, the coding agents, et cetera, et cetera, it's a pie on the very, very high shelf. -- but we can see it. And our customers also expect to get it at some point. I think they can see it, too. And so we -- that's why we're pretty hard at work.

Yes. And everybody has that vision, but I think we still have a lot of problems to solve. Even customers have a lot of organizational problems to solve before they can even get there. So...

Okay. Great. Left side there.

Yun Kim, Loop Capital. If you can just talk about, Sean, maybe talk about the partnership opportunity with cloud service providers, especially around the fact that most of the AI workloads where there's a lot of growth there, obviously. And most of them -- I mean, all of them are really running on CSPs. And last time I checked, there's a lot of CapEx spending to support that in the future years. So obviously, there's a really huge growth opportunity to target these AI workloads. Is there like a joint partnership opportunity with CSPs that you're working on, for instance, targeting the deployments and the workloads specifically, whether the customers independently? And how much of your Datadog for AI is available on their App Store and marketplace?

I mean hyperscalers and the CSPs are the relationships that we've had the longest in our channel and alliances. We're a cloud-native company, and we started very early on working like even in the early stages of our channel alliances or that was the place that we spent a lot of time. We still spend a lot of time, and I think we're getting better and better at our co-sell motions, our technical collaborations and the things that we're doing with them. So I'd say, in general, our sales teams are always in the field thinking about how do we partner with our hyperscaler partners and whether it's just as simple as sharing notes on the accounts to actually planning and strategizing around accounts and how we win them together.

And to your question on products, they're all available on the marketplaces of the large cloud providers. And AWS, for example, at their most recent re:Invent conference, they announced their top partners in terms of sales through their marketplace, and we were one of them.

This will be the last question on the right with Megan.

Arti from JPMorgan here for Mark Murphy. I appreciate you give me the last question and great presentation. Said it only happens every couple of years. Any way to quantify or conceptualize the sheer volume of code that's being produced today, [indiscernible] cloud code, OpenAI, CdX.s it subtle or overwhelming amount of code being created? And is it moving into production and driving activity for you guys?

Well, we see the increase of code. We see the increase of the -- like all the signals we get in the reference series that are linked to us, also what we can see in the open source point to a lot more code being generated. So yes, it's there. If it actually ends up in the repository, it's going to end up being shipped. And so that we also see that coming. I will say most companies are still early in the transition there. I mean the AI labs are completely in on it. The brand-new start-ups are completely in on it. The rest of the companies, some of them see the light, some of them don't see it yet. And even when they see the light, it takes a while to get the engineers to all transform to get all the new processes to work that way, to identify what the new bottlenecks are. So I would say I would expect quite a bit of that to happen this year. And so we should see where we are at the end of the year.

Okay. All right. Well, with that, I am going to conclude our Investor Day. Thank you so much to our presenters for sharing the Datadog story. Thank you to all of you for spending 4 hours with us. If you want to watch it again, a replay will be available shortly on the website as well as the slides. So thank you very much. Have a good evening.