Datadog, Inc. ($DDOG)

All right, everybody, let's get this started. My name is Koji Ikeda. I'm one of the software analysts here at Bank of America. Absolutely thrilled, and thank you for joining us for the day 2 keynote lunchtime keynote. To have Datadog CFO, Dave Obstler with us. Thank you so much for doing this.

Thanks for having us. We appreciate it. Thank you.

Thank you. So we have a lot of people in this room. Thanks again for joining us. And I guess there are some people that know Datadog very, very well, but there's also many in this room that might not. And so let's start very, very high level. What is Datadog. What is the core problem Datadog is trying to solve today?

Yes. So Datadog has an observability and security platform that allows the deployment of modern software applications mainly in cloud environments in a safe and effective way -- most of our customers are those that have mission-critical software that's customer-facing. . So think about video or credit card companies or banks or airlines or hotels, all of them have digital applications that interact with their customer and the Datadog platform is used to monitor and secure the effectiveness and operations of those platforms. Most of this is in real time and Datadog over the years has had an expanding platform to cover more of the surface area in examining what's happening in those applications and allowing them to operate in a good way for the customers.

So your results and fundamentals are accelerating. Clearly, good things are happening. And so why is observability and security becoming more mission-critical software complexity and AI adoption acceleration .

Yes, definitely. AI adoption is a part of it, but essentially, we are observing the development and the production of modern applications, mainly in the cloud. And as they get more and more complex, and as more and more of those applications are moving from legacy technologies to the cloud and be modernized, that is what Datadog does. So what is the effect of AI. One of the things is anytime there's been new technologies of which certainly large language models are one, there has been more of an impetus to modernize the tech stack and therefore, create more applications that are in the cloud and that creates business drivers and has historically created business drivers that have enhanced the data business. This applies both to non-AI-native companies who are modernizing their tech stack in order to have large language models in their applications as well as a set of infrastructure companies who were called AI natives who are experiencing a demand cycle and have rapid product releases, they're cloud native. Their whole stack is modernized. And they're using in a very significant way Datadog products to help observe the delivery of those products to their customers.

So 1Q results A little while ago, but still yes, --

Fond memories.

It has been a couple of weeks. But it's a -- I think our note started with low, right? A fantastic results all around accelerating growth. winning new customers, you never thought you would have before. And so let's tackle the first part about just accelerating growth. Looking over the past 6 months, what sort of inflections were you seeing from core observability using out there?

Yes. So this has been going on for -- I think we've been accelerating for 3 or 4 quarters. So we've been communicating this message that -- we have seen a good buying environment that the investments we've made in our platform, we can talk about the number of different products, we're resonating. So we're getting more platform growth -- and we also are having a significant demand cycle in AI native companies. And this has been building on itself. We've also been investing substantially in our go-to-market over the last year in order to deliver this to our customers. So this has been building on itself. And essentially, in the first quarter, it continued that trend of acceleration in many areas, the non-AI natives, the AI natives, different geographies, enterprise down to SMB and then all those were all contributing factors in producing the first quarter, but the seeds of that started 3 to 4 quarters ago and have been building on itself.

Let's talk a little bit about the multiproducts. You guys give a lot of metrics 2-plus products, I think 4, 6, 8, 10. I think I got that right. So yes. So what are the products that are driving the most multiproduct sign out of those metrics, the 2, 4, 6, 8, 10, which is the one we should be focusing on .

Yes, I mean you should essentially -- the benefit of Datadog is that you can do all of your observation actions in a single platform. So in some ways, it's a single product, the platform. At the epicenter and this has been going on for some time, you have the -- what we used to call the 3 pillars, which are infrastructure or metrics, application monitoring or traces and then logs. And we've had a very substantial demand cycle from what we call digital experience. And this is taking how an application interacts with customers from the back end all the way out to the mobile device, et cetera. That's called -- that's RAM and that synthetics, et cetera. And that -- those are bigger products for us. Those have been growing very rapidly, and there has been a consolidation away from both point solutions and do-it-yourself towards our platform. That has been enhanced by some other products that we've put on our platform, including our cloud security products our service management, which is basically interacting with the users to be able to manage cases, et cetera, what we call products that allow you to A/B test on different applications and determine what's most effective. So we've been adding on additional products on top of that. And then we started to add on what we call AI for data dog and Datadog for AI. So Datadog for AI is most of our business is created because there are many things that impact an application. What I just mentioned plus databases plus network and now you have LLMs and other things. So we've been working to monitor those, and they are being adopted as well as AI for Datadog, which is how can the platform gets smarter, and service the customers, and these are things like our bits SRE. And all of those have been developed and are starting to get traction, which is enhancing what had happened over the last 3 or 4 years in the core pillars.

So infrastructure monitoring, APM logs are all big ARR business?

They're all over $1 billion share .

Remind me out of the other products, what sort of metrics you gave on scale. I think there's a bunch that are $10 million plus -- and -- is there the potential for some of those to beach $100 million $250 .

Yes. We've been doing this. We've been giving a lot of metrics. Our Roman synthetics have passed through that, and we gave metrics on that. we gave metrics, I think that security passed the $100 million. What we've been doing is, over time, as we reach these metrics generally around 50 and 100, we have been giving those metrics. So we had the passing of 100, which was the ones I mentioned, security, then we had a number of other products that were reaching the other -- the 50, which were things like database and network. I'm not sure I have all of this exactly right. And then we gave metrics. So we have a lot of other products that are passing through 10 and multiples. And so yes, we have a lot of products that have been scaling this. And what we said was there are lots of opportunities. And what we're going to do is we're going to give -- as we reach these milestones, we're going to give those metrics so that everyone can follow along. And the things that show a lot of promise are Bits SRE. These things like the product analytics we talked about, which is really about how an application is constructed and interacts with clients. These are all smaller products, but the TAM there and other point solutions are much larger than we're at today. And so we're optimistic we can scale those different milestones as well.

Yes. In the quarter 1Q results you guys won a lot of big -- I mean, you guys have many big deals for a while now. So what's going on there with the big enterprises? Is it the go-to-market motion getting better in the enterprise? Is it the enterprises need something more like Datadog? Maybe it's a confluence of both? I mean, help me understand the big deal activity.

Yes, it's definitely a confluence of both. So you have in a customer base that has been around for a long time. So therefore, they're not cloud native. They didn't just get birth. So they have legacy technologies. And they've they have a long way to go. There's somewhat 25%, 30% of workloads in the cloud right now and modernize. So they are continuing to find use cases and modernizing Datadog's platform is getting bigger and better. So we're consolidating market share onto our platform. And we're getting better about delivering the enterprise service model whether that be channel partners, customer service, technical help the whole ecosystem to be able to deliver. So all of that has been what we've been investing behind for some time that is bearing fruits. And that's resulted in some large lands. Some we still are largely a land and expand, some very substantial expansions. And when we've given our customer examples, if anybody is curious to go back and look at the scripts, what you'll see is a great combination of what the economy is, you'll see insurance companies, financial service companies, tractor companies, all sorts of different car companies, airlines. So you'll see how this is evolving in the spread of the business towards cloud native and certainly AI natives, which we'll talk about, but also the cloud nativity within very large traditional enterprises.

Is there any limitation to the type of company or size of company that might not look at Datadog anymore? Or is Datadog available to all types of companies?

Well, so there are some companies that have in their past wanting to want to do this themselves. There are very few. I mean maybe I think Google is a good example of trying to do things themselves. So it's not -- that's not really core to our business, although -- and we'll talk about it. There's examples where we've gotten those types of businesses, and we'll talk about that. I think the other thing is if we generally are delivering our product through the cloud, so if you require an on-premise solution for regulatory or other reasons that has, by choice, not been where we've concentrated our R&D. We are developing more of those products. So you can see companies which because of either their practices, or the regulation cannot have data leave their premise. That would be a company that is not core to Datadogs end market.

Last quarter, you guys talked about winning some AI labs within some large tech companies. Let's talk about that. So what happened there? Why did they come to you? What were they looking for? And how are you guys hoping to solve that?

Yes. So as background, so I think we -- there's a lot of information we've given a lot of information about how pervasive the AI business has been. And this includes some of the foundation model companies, companies and database companies that are vertical. So already Datadog has been used pretty pervasively in the monitoring side. So production work environments, inference production. And I think we said over 650, and we gave a lot of statistics on spending over $10 million and 10-plus products. And what we added to that is that 2 larger companies, a hyperscaler and another very large tech company that have model creation within their businesses, foundational models had used Datadog for training as well. Most of the time, our end market has been for production workloads. But as a couple of things have happened as there is a boundary between training research or training and when it has to go into production that we've now been able to have some customers buy from us. And in this case, there were some large customers hyperscaler who traditionally does more things on their own, but use Datadog. And what we found is this is an example of the fact that they may not be using Datadog pervasively, but there are use cases where there going to be using Datadog, and that's a great voice of confidence that these companies whose whole businesses this are using Datadog, so it's sort of like a seal of approval.

I mean you mentioned earlier in our conversation that Datadog does well with Inference. And now you're saying training too.

Maybe we're saying we have, but we're not -- we don't believe -- we haven't said the -- we generally don't overpromise. So when we have like a certain amount of training that is spread out we'll tell you, right now, it's more of a sort of centralized. It's good, but we have said most of our revenues we expect to be from production and inference. But we'll see what happens. We'll bring everyone along.

As we think about AI in the future and more enterprises organizations, everybody building their own things, large small language autos for I mean -- how does Datadog think about that opportunity and maybe even going after it a little bit .

We'll prepare itself for that opportunity. I think that for the most part, when you think about production, so we're always going to -- I think we're always -- even if we're in training will always be somewhat proximate to production. So there could be market extensions. But essentially, you use Datadog when it can't go down. . So if you're training, yes, I mean, if you're training and by definition in the sandbox, you're not putting in production. So you have sort of different impetus. Maybe that will happen, and we certainly are preparing our products. They're the same products. We certainly will have the products. And we certainly are in touch with customers -- and we certainly will push that if it makes sense for customers, but we just don't know the answer whether that's going to be a core market or a specialist market for us.

So when I think about observability in the most simplistic way. It's data ingestion and analysis. But I know it's much more complicated than that and the architecture is very important in the way to address that. And so maybe help explain to those in the room that are again less familiar with Datadog what is it specifically about the technology architecture that takes this what could be a simple complement concept into a very complicated, something that you have to invest in and difficult to replace.

So 1 of the things before we get to the architecture, 1 of the things is data, Datadog, okay. But it isn't true. There's not one data. We have 1,000-plus integration. When you think about the operation of a cloud application in real time, CPUs, GPUs, databases, lines of code, network, all sorts of things. . So one of the things that -- and this gets to the architecture, that Datadog did very early as they developed a common architecture to take all that data from all the different parts that could affect and organize it and put it in one place and make it transparent. So you can see that is very hard to do. And that's the reason why point solutions don't really make sense because none of the customers are saying, I want to see what happened to that line of code. They say, I want to see what happened to the functioning of the application. So you have to see all of it. So that's 1 thing. So then the architecture is that data dog organized that data, knitted it together provided user interfaces and other ways to see it in real time. correlated. Now this is being enhanced by AI now, which is using large language models to do diagnosis and maybe even 1 day sulfur mediate and produce it all knitted together. And that's hard to do because there are a lot of factors. And many other companies have tried to do this piece mill, and it's all gone back to that integrated data model putting the analytics on top of it, having a simple but not simplistic, meaning everybody can use it. We don't charge by sea. We benefit when everybody goes into this utility and uses it, and that helps us. And so all of that architecture has been an architecture, which is -- provides the most value to our customer base in analyzing. And it all has to be real time because this isn't like, okay, I produce a marketing collateral, there's an error in it, I can fix said, this is your whole business on the front end. So that's what's created it. And then all the different pieces, adding on all these different pieces and knitting together have been complex. Right now, we have a competitive advantage and that we have a very large platform. It's scale. It can handle anybody's scale. It basically organizes all this for everybody and it allows you to have a significant platform investment to amortize application investment on top of it, which is a big competitive advantage.

I wanted to dig in on the Datadog AI strategy, but also the data for our AI strategy. So let's get the data first. Right. What is so special about the data that Datadog has. And how long do you think it would be even if it's feasible for a competitor to amass that type of data to be competitive .

Yes, I wouldn't be feasible at a price point that would be competitive. In other words, large language models are by definition large. And what they're doing is they're looking at lots of data. So our strategy in the model side is to offer that, but also then have a set of models that are very specific to observability and security use cases and then to make them because of all the data we have, and then both have a higher functioning model and a cheaper model. . Because if you're not -- if it's not a generalist model, you don't have to pay the cost of all that stuff that doesn't apply to your use case. So that's what we're doing in the model side. Then there's a whole bunch of different things. So I'm speaking now about AI for data. We'll get to data for AI, which is all the DNA that goes in. Then there's also the ability to connect to all the software creation on the agent side, whether it be an agent or Marsh or a person, we don't care, whatever is creating and getting towards an application to see what's going on there and then correlate that with everything else that's affecting the application. So all of those are things that we're putting in our platform we're having our user conference in a week, the next week. And there'll be a lot of product releases and our Investor Day also had a very strong articulation of this. And what that will enable us to do is and is already is basically making the platform smarter being able to diagnose very quickly what's going on, being able to make recommendations on what to do about it. And in some cases, to actually implement those recommendations. And that's the vision. That's what the whole service management vision is in the model. So that's like the investment in AI for data to make the platform enabled to see all those things and use large language models, whether they be the third party or our own to be smart. .

Let's talk about your AI products, the bits AI products Actually, I don't even know how many you said. What do you have? And how should we think about that.

Okay. So heading first into monitoring data flows. So we have LLM observability. So that is the functioning of an LA model Think of it as you have databases, you have code lines of code. So that is where you have LLM in a production model application. And you're using signals to understand is that affecting the performance of the application. And I think we've said that, that is germane to having LLMs in production. And we have we've had significant growth there, still early days. So we're getting revenues from that. Then we have GPU, which is essentially like an infrastructure product, but instead of CPU, GPU, where you're seeing how the application might or the model may interact with GPUs in delivering. And that's like similar to our other products where you have to see how the servers or the GPUs are doing -- and then you have that is sort of an example of data dog, monitoring things that affect. So that's data door A. Then you have AI for Datadog, and those products are the bets products, bits is sort of a general name. This is our mascot, our dog. So that's why we are using bits. It's very cute. And so for different end markets, so SRE would be the systems reliability engineers, et cetera. So that product is out there. There are 2,000 customers using that. I think we have 100,000 or more investigations. And then we also have products in for development and for security that we're rolling out in the pit suite. We also have, as I mentioned, the ability to connect to the cogeneration through our MCP server. When that will enable that information to get into Datadog and monitor that. And we also have a bunch of other products, agent [indiscernible] et cetera. That is being able to monitor on sort of the cost and management side, how much you're spending on tokens, who's using it. We have cloud cost monitoring and now we're extending that into agentic monitoring. So that's -- that was a long time. I apologize for that. There's a lot of names, but those are some of the product lines that are being put out to market in those areas.

I want to talk about platform consolidation. Good driver of growth for you guys. What is driving platform consolidation today with the large enterprises. And does that theme of what they're doing today for consolidation just continue into the future? Or does consolidation maybe change?

Yes. So you might wonder why this didn't all this happen already? Why isn't -- why didn't everyone just buy everything in Datadog to begin with, and it's already preconsolidated. The reason is, one, Datadog didn't have these products in 10 years ago. . So there are other products out there and there are contracts that customers have with those products. So we're in an environment that it's happening over time. And why is that happening at all? It has to do with the -- that if you are a practitioner and you need to operate in real time. You do not want to be context shifting. You don't want to be going into a lot of different data sources. It's slower. It's actually costly, more costly. So as we've developed these products and clients have looked at the utility and they've gotten off of contracts, this has been going on for some time. And I think as the product suite has been getting better and better and better, the components all getting to product parity and beyond. It's been accelerating. And yes, I think we're very early on in this. There's proliferation, there's a lot of observability point solutions out there. Like I said, all of the decision-making is leading towards consolidation in the single platform. So we think we're pretty early on in that trend.

Do your buyers still think about -- and we'll talk about it in the 3 core applications Infra, APM, log analytics. Do they still think about that as 3 separate products or the customers beginning to come to you and saying, please solve this.

No, they've -- for a long time. We've been selling as credit, so you buy $2 billion of Datadog. You go in and you use all this. No, they've never thought as different products. We're doing that in order to provide transparency to you all. It's one product. They don't care what they're called. They only care about doing their job, systems reliability engineering. So this happened a long time ago. That's what made Datadog and they think of it as the platform.

I want to touch a little bit about security. 10 million-plus business -- it's been out there for 3, 4 years. A couple of years now. So maybe for those that are a little bit unfamiliar with here, you tell us about your security journey, what you're doing on the product side? And maybe more importantly, what are you doing on the go-to-market side? That's very a very good thing.

So first of all, you have to say what security, okay? So -- these are the things we're not in endpoint security. There's some really good companies. We're not in network security. We're not in e-mail security. What we're in is cloud security. Cloud Security has 3 components. It has how the cloud is working, it's called posture management and set up to secure digital applications. Two is cloud SIM. How are you using logs and other information to investigate what's happening? And 3 is code security. How are you engineering security into the code. And so for -- so not on-premise, not legacy, but for cloud applications, we've built this suite of the 3 products. I would say the 1 that we probably made the most progress on is cloud SIM. Some of the reasons are it's off of our logs business, and we have an over $1 billion log business. We have a great largest observability logs. And the end market has dynamics where we've been an innovator in it. So one of our -- so we've had 2 major strategies. One is attach the whole suite to cloud-native emerging companies and 2 is attach cloud SIM to more traditional companies where we have a strong observability logs. So that's been the strategy and it's working. In terms of -- that's the product strategy. In terms of go-to-market, particularly for enterprises, it's a different go-to-market in that one, you have the influence of a buyer that is not a traditional buyer, the CS and even though they're getting closer together, that is a different buyer than DevOps. And for some reason, I don't know, I'm too young. But for some reason, there was a distribution -- bottoms-up distribution that facilitated direct in DevOps. But security is a sort of highly central -- has been a highly centralized and if CISO has the [indiscernible] So it's a different buyer who buys through channels. So what we've been doing is we've been working on selling our direct to our champions, but also investing in product experts or specialty salespeople who are going to try to penetrate CSOs and others and channels where we have a channel program like the other security companies. It's still early. It wasn't our DNA, but we're investing behind it as part of the overall security investment.

One thing that we've heard over and over from customers and partners is I'm the observability buyer that guy is a security buyer. Right. Is that how it's always going to be in your view? Or do you think one day that might convert?;

We think the whole strategy is that it makes no sense -- there's certainly territorialism. There's certainly -- but essentially, it's better -- vol up here, he would say it's idiotic not to design security into applications and to have all this come through. So dev tech ops, we have seen signs and we believe it will come closer to closer together. A thing that we think will accelerate that is Agentic coding because that's going to speed up everything, and then it's going to require everyone to work together. So even though you're right, there are these separate buying groups. We're seeing signs of them coming together. And we believe in the future, since we believe that businesses are in the and logical that they will come together, and if we can use the gains in technology from agentic content of detection of penetration, remediation, that will most likely help us in our attempt to scale security.

So in that vein, when you say genetic coating, it's getting pushed out from getting from the software development side and security has to deal with it -- does that feel like the you're beginning to pull in some of the security budget because of where that velocity of...

Progressive companies. We have been for some time, meaning that we have clients where it's all -- it's like I said, it's all knitted together. But you have a whole big world out there. And so it's a process. I don't know if I can say that in the last 6 months, we would see a sea change. We think it's going to -- it's an evolution, and we're -- and we believe it's going to happen. It's going to happen over time.

You guys are highly successful with cloud-native companies and cloud native strategies -- at your Investor Day, you talked about Cloud Prem product that you have -- so can you tell us a little bit about Cloud Prem, what's the strategy there? And how are you leaning into go-to-market with that product?

Well, essentially, we're leaning into it and go to market in the same way, meaning that if a client would find utility or a cost benefit from having data kept in their -- on their own servers. We want to have a product. And so far, it's been that we have the analytics or we have the log and you keep the data. So we've also have products that allow the data to get in an efficient way of everybody pipelines -- so -- but -- so that's what we're doing. Now we're investing behind it. We have customers that want it, and we've been successful -- it still hasn't been the vast majority of the way our customers want to buy. But what we're working towards is in difference, meaning that if you want to keep the data, great. If you want to give us the data, great. If you want to have all the functionality over to your side. And so that's taking time to get complete product parity where we have exact functionality. That's taking time, and that's what Cloud Prem means. We're working on that. That's mainly for the first use case lows.

I got you. I wanted to ask you a question on durability of growth. You guys have great growth trends. And so how do we think about the drivers of the durability of the growth, whether that's cloud migration, platform consolidation and AI and what are you guys seeing that's giving you the confidence that growth continues in the passion it has .

Well, the durability depends on what your time frame is. Okay. So we always have believed and we're seeing that this is a very long time investment cycle. -- because such a high percentage of applications and infrastructure are still on legacy technologies. So we've always said that if you want to look at this, this is a secular trend. And it can be cyclical, but it's also secular and it's got a lot. I mean you have 70% plus of workloads that aren't in the cloud. So we believe this has very long legs. In terms of is it going to be a straight line, meaning on every month, is it going to have the exact same thing. We don't know. It's likely if proven you're going to have periods of investment, you may have periods of optimization. It's cloud software, it's consumption cloud software. So we can't predict exactly what the line is going to be in every moment, but we have confidence that it's very durable and it's very long because we're attached to huge market drivers. Now we also -- if history repeats itself, we found that new technologies, of which AI is happening is a huge accelerant of this conversion. And so again, it's still too early. But if history repeats itself, that is likely to inflect the line upward faster to get to the same place 25, 50 years out.

Yes. Yes, that makes sense. I wanted to ask you a question on how Datadog thinks about product velocity and R&D investment and frankly, your guys' own use of AI within the organization. So you mentioned -- I think we talked about it yesterday, you got about 4,000 developers with an organization. So how do you think about leveraging AI and investment?

Yes. So step back, we've always been a product-led company we've been out investing we're bending over $1 billion in R&D. That's had tremendous benefit, meaning it's allowed us to invest in the platform at scale and the functionality. We also have our company that has no shortage of things of functionality that we want to roll out. So that's been like epicenter to Datadog. Out invest everyone in R&D. Look at the pipeline of what we want to put in the market at a profitable price and creating value for clients and just look at your R&D resources against that. That's been what Datadog has done. So what about coatings coding aid agent. So that is essentially another piece of raw material that we are using to try to accelerate the launch of features where -- we're not -- what do you call it, code math, it's agent Maxine, what's the total in [indiscernible] we're not talking [ maxes. ] We've never paid anybody rewarded anybody on the number of lines. We basically think about products. So it's all product, all those software developers, we call product people. And so essentially, to the extent we can become more efficient in speeding up feature release. It's good for us, and we're finding that. We're basically experimenting around the interplay within that envelope on R&D between tokens and people. And we have a lot of experiments. We have places where we have keep the people. Let's see how fast you are with less tokens. No. Constrain the people. Let's see how fast you get. And we're doing -- we're pretty nerdy engineering, wonky. So we have all this stuff going on, and we're trying to see what's going to happen. We believe that the componentry of R&D will shift somewhat. We don't know exactly to tokens from people, but we don't know the exact amount.

Have you been able to use AI internally with all that development that you have to help out with your own homegrown solutions in sales and marketing and G&A at all.

So we have AI -- there's 3 or 4 major use cases. One, the product itself. We talked about the models and the products. So those would be components of the product. Two is accelerate product release through software coating tools. 3 would be use AI for productivity of the employees and 4 would be what you're talking about. . Are we able to use a large language models or AI to improve the productivity and the effectiveness of the different functions. And yes, I mean, there's many, many examples. We're using AI to automate the creation of deals and the processing of deals. To enable our salespeople and our market people to understand propensity to buy and direct our salespeople in places that have a higher chance of success. So we have all of those -- I'm not going to list all the names, but there's lots of them like that. How do we enable salespeople in product training and things like that? How do we speed up all of the back office functions, whether it be legal, accounting, cash cycle, marketing collateral, all of that. Yes, we have experiments or projects going on in many areas to try to do that. And what we're really thinking about mainly is how can we convert repeated repetitive administrative tasks. Towards more insights so that we can act faster and inflect the business. So we have all that going on too. I mean we're not -- again, it's not token. We're not -- basically it's -- you've got to produce something from this, but we have a lot of opportunity. .

And remind us, what is the investment strategy in sales and marketing from -- how do we think about that? .

Yes. The investment strategy in sales and marketing is bottoms up. to be able to cover the high potential customers around the world. It has to do with investment in enterprise, all the way up government and enterprise biggest entities all the way down to start-ups on a lot of different to be able to cover them comprehensively and to be able to get lead gens from them, et cetera. then it has to do with geographic expansion like many U.S. companies, we were most developed and followed where the investment in cloud software was most intense was the U.S. And we're finding many areas with great payback Brazil, Korea, India, et cetera, where we did not have on-the-ground presence. And we're developing packages of salespeople. Sales engineers, marketing dollars, maybe even data centers to be able to do that. So that's sort of where it is. In addition, the way to reach customers direct and then also channel that we're expanding. So it's a bottoms-up business plan aimed at that micro level and how we can reach the broadest swath of customers around the world.

I got you. I know we're running up on time here. And so maybe M&A strategy. You guys are not afraid to go out there and acquire things and how are you thinking about your M&A strategy? Any changes in what you've done in the past -- and how do we think about that going forward?

Yes. I think we basically to date, not acquired streams of revenue, but acquiring product capabilities and the people. And we continue to do that. So when you think about building product -- we often build product and then we can enhance that through acquiring a product capability. And that's the epicenter of what we do. And if there's opportunities, we'll continue to do that. In addition, I think we're open to something that bigger where we do acquire some customers as well, but that will really be dependent on can we integrate it in? And can we accelerate? Does that help us? And so we're pretty principled on that. One of the things we really insist on is that the R&D team and the product teams stay at Datadog. So that means for certain companies sell move. We don't want that. We make it in the incentive structure. So we only acquire companies where they want to stay. So that's sort of what we've been doing. It's been very successful. We've created some very big businesses. through that with not that big acquisitions. And I think that's the core with the reservation that we could look bigger to the extent it fits in, in our disciplined way.

I got you. Yes. Last question for you, David, and thanks so much for doing. Yes, you got DASH next week and so -- we're trying to get a little bit out of you of what -- as investors in the room and myself as I look at the schedule, is there anything I should be focusing on just to make sure I don't miss anything big coming on.

Yes, was background, DASH is a place where we make a lot of product releases we do on product strategy, it's for users. And so I think you're going to get -- you get a pretty good road map. And then if you look at who's speaking and everything, I think you'll see is going to have no surprise, a lot of AI content. So some of the things that we will be discussing here the AI for Datadog, data the dog for AI, con, all that well, I think will be fleshed out a bit more at DASH next week.

Got it. Thank you. We're all out of time. Thank you so much, David.

Thank you. Good interview.