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

All right. Good morning. I'm Sanjit Singh, I run the infrastructure software practice at Morgan Stanley. We're super excited to have the management team at Datadog. Datadog has joined the conference every year since they become public. I think even like 1 year when you're private. So thank you once again. We have Olivier Pomel, CEO and Co-Founder and Chief Financial Officer, David Obstler. Oli and David, thank you again for joining us at the TMT conference.

Thank you.

Thank you.

Awesome. So let's get through some disclosures and then we'll dive into the story. For important disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures. If you have any questions, please reach out to your Morgan Stanley sales representative.

So let's start sort of level set where we are in the business. Datadog had another solid year, growing the business 26%. You're at $2.7 billion in revenue, 25% operating margins, a great financial profile and you have 30,000 customers. Oli, I think what stood out to me about this year was this wasn't a year of easy comps per se, and yet the stability of growth really came through. What were the factors that enabled that stability of growth. Where do you think the team executed well? And where do you think there's areas of improvement?

Yes. Thank you. So look, in general, it was a year where most of what made us successful today remain true. So in particular, cloud migration is still happening. It's still happening at a good clip. It never really stopped. There were some ebb and flows during COVID, after COVID, et cetera, et cetera, but it's still there, and it's still very, very early. We're still getting an outsized share of existing and new workloads. We're getting share pretty much in everybody else in there. We're the leaders in observability, and yet, we only have about 10% of that market. So there's still quite a bit of it to be had, which should allow for very healthy growth rates in the future. Another thing we did well, I think, is we kept executing well on the multiproduct part of our business and consolidation. So many customers have been consolidating on us. They've been buying more products from us. We've been building more products successfully and shipping them. So we mentioned in the last earnings call, we have about $2 billion in ARR, of that $1.25 billion is in infrastructure, $0.75 billion on logs, $0.75 billion on APM and $200 million in some of our other products that are also growing very, very fast. We're very happy with that motion. Now in terms of what could have done better, I think if I look back at the whole year, I think we did a pretty good job at growing the sales and the engineering teams in the second half of the year, but we got started a little bit late at the beginning of the year to scale that up. And so when I look at where we are today, I wish we had more sales capacity online, and I wish we also had more engineers to build all the things we need to build.

That's great. And that kind of dovetails into my question for David. When we think about the guidance, you guided Q1 to about 21% full year 18% to 19%. Margins come down a little bit versus last year. Can you give us sort of the underlying guidance assumptions? I mean beyond the -- is there any sort of factors that stand out beyond sort of you discounting the sort of near consumption trends on the revenue guide. And then on the investment side of the house, what parts of the business is poised to get incremental investment in 2025?

Yes. On the revenue side, it's going to be a bit of a boring answer. We did what we've done every year since we've been public to start the year. We've taken the drivers, which are net retention and new logo accumulation and discounted them. And we took the same approach this year as we did in previous. One thing that we've been asked is we've pointed out that we have a small, but rapidly growing AI tools customer base. And we've talked a little bit about that. And we essentially didn't assume that was going to continue to grow at the same rate like the other sectors, we took that down. We don't know what's going to happen, but we put some risk management on that. And in terms of the investment side, I think this has been going on, as Oli mentioned, for some time. We said sort of towards the end of 2023 that given the long-term opportunity and the fact we had pulled back on the back end of COVID and some of the optimization that we were going to invest more both in go-to-market and in R&D, and we started successfully doing that in the second half of 2024 and are going to continue that this year. In the R&D, as I'm sure Oli will talk more about, this is about capacity to invest in the platform and the new product areas. And in go to market, it is really about coverage. There's a lot of international places we don't have optimal coverage as well as investment in channels, in some specific areas like Fed security as well as in marketing in order to cover that around the world.

Yes. That makes total sense. Sticking with David, I actually would love to get both of your perspectives sort of taking a step back, you've been sort of 3 years into a higher rate, more cost-conscious budget environment. What's been the storyline in terms of how is the competitive environment sort of changed? And as the sort of dust has settled in this sort of a tighter budget environment, what would you point to for investors that Datadog is coming out of this tight cycle in a stronger competitive position?

You go first.

I mean to me, the competitive environment is not actually very different from what it's always been. We've always had some incumbents on the higher end on the enterprise end that tended to be more on-prem than we were. And a number of, I would call it, a rotating cast of new companies on the low end in between the do-it-yourself and the very low end of the market. And that's not different today. I think we are generally the leader, still in some areas behind some of the incumbents. So for example, we're still smaller than Splunk on the log side, which I think is a big opportunity for us. But, overall, I would call the competitive environment very similar to what it has been in the past. I mentioned earlier, we have about 10% of the market. We're the leader, plenty of opportunity as the market is growing by 20% year-over-year. So we can see ourselves keep taking share over everybody else and have a very substantial growth for the foreseeable future just in our core market.

Awesome. I think just to add, we said all along the platform sale, the consolidation opportunity, the gaining of market share, it's only come into more of bright light as you have the natural utility and productivity of having the platform with also the opportunity as you consolidate and about half of our deals on the larger side our consolidation you can save money and cost manage at the same time, you're getting a better product.

As we think about where we are in the state of the business, $3 billion in scale. For investors who haven't or not as familiar with the Datadog story, maybe they've heard of Datadog, looking at as an investment opportunity maybe for the first time or revisiting the story, Oli what gets you excited? You're at $3 billion in scale. What are the opportunities that you're really excited about that can essentially move the needle in terms of growth at this stage of the company?

Right. So well, if you haven't invested yet, now is the right time to invest. So there's a number of things to be excited about. I would classify them in 2 halves, there's AI and there's the rest. The rest is probably what's going to move the needle first, which is everything we're doing on top of our existing observability business and on the edges of it. We have so much to do like when we look at what we're pushing for this year, we have a lot of opportunities with our logging product. I mentioned earlier, we're not the incumbent there, where there's plenty of markets to be had. We have a product called Flex Logs that is doing extremely well in the market and that we expect to scale quite a bit. On top of this product called Flex Logs, we are starting to see an inflection from our SIEM product, our Cloud SIEM product that we expect also can make a significant end this year, and we think it's a great product that we're putting a lot of effort behind. We have a ton of opportunities in APM, and there's a long fuse in instrumenting and observing every single application out there, and we've seen steady growth there over time, and we keep investing to deliver that. There's a real opportunity with new products in service management, and in particular, with our OnCall product, for which we had extremely strong reception even before it was released in GA and so we also expect that product to get strong uptake fairly quickly from our customers. And we see great expansion possible in analytics and going beyond core observability on top of our release on monitoring product, which is growing very fast off of a large base of more than $100 million in ARR. And again, we think that's a product we can really lean into. So a lot of directions to go into on our existing products and obviously in OnCall. And now if you switch to AI, I would break down AI into, like I say, also 2 halves. The first half is helping monitor applications build around AI. And most of that is core observability. Most of that is logs, APM, metrics, infrastructure, real user monitoring, that sort of stuff. I would say the other half is interesting. There's opportunities in both helping companies understand how the models are performing. So we have a product for that called LLM observability, which helps customers understand whether the LLMs are working properly and what they're delivering for the business, and we're starting to see real traction for it, which is exciting. And the other part of it, the last part is how we can automate the whole process for our customers and be in the business of not just observing and giving tooling for engineers, but also in giving them agents that can help them perform some of the tasks automatically. So the dream there is instead of putting you up at night, telling you to fix something or telling you to investigate to fix something, you get a text the following morning that said there was an issue, it was fixed, you should look into it. That's sort of more attractive. We're not there yet, but we can clearly see the path there from the technology and that's also an exciting development for the future. And from our positioning from the vast amount of data we have, the fact that we see right into the production infrastructure and that we are in the right workflows for investigating, detecting and solving issues, we think this opportunity is ours to lose. So a few ways to get excited. At least, that's what excites me.

I loved how you framed it between like the core opportunity, which still has a lot of runway as well as how AI can drive growth. Some of my industry conversation from the last couple of months is, there's a lot of framing of sort of observably 1.0 versus 2.0. It's framed in different ways. But if you sort of just take 1.0 as the application that we've been monitoring for the last several years, applications running in the cloud or applications built on cloud-native infrastructures. What does the next era of observability look like from the Datadog perspective?

Yes. So the first thing I'll say is observability 1.0 is already monitoring 3.0. So it's been there for a while. And when we started Datadog, we were monitoring 2.0. So we've already got one major version upgrade from when we started. When I look at where the market is going and where it is today, it's interesting because it went in many of the directions we thought it would go, and we shaped the company to support. The first thing that happened is that what used to be many different categories have really consolidated into one larger category, which is observability. And we've built towards that, and we think that's great. The second thing that happened is that we've seen a lot of democratization of observability. It used to be something that a few system admins would do in a corner on their own, and now observability is a core part of every developer today. And there's broad use within the companies pretty much across workloads and across teams and across individuals, which is very exciting. There's the third trend we've seen with observability that was always present, but is getting even more important which is that the value is being shifted from writing the code into understanding it, running it, operating it, securing it and understanding whether or not it's doing the right thing for the business. It used to be the case, like that trend [ has ] been there in the past because it was getting easier and easier to write code. You didn't understand, you had more libraries, you could use open source, SaaS, cloud, the Internet with stack overflow and you name it. So all of that was there and that helped developers gain a lot more productivity over time. Today, we see that happen with the coding models. You can actually write code very quickly today using AI systems. It used to be that if you wanted to write a new application using a specific API, you spend a day reading about the API, you watch a few YouTube videos to learn about it, then you do some trial and error and you'd get it to work in the end. Now instead of doing that, you can ask the model to do it in 5 minutes, you'll have a version of it that works. It's great to get to production a lot faster. On the other hand, that information doesn't go through your brain. And so you just have no idea of what's going on. And so the value, again, has changed from writing the code to testing it, understanding it, figuring out whether it works as you thought it would for the business, and doing everything that happens next, which is what we do.

Yes. Is there a theme there in terms of shifting left in some sense as the value sort of moved away from code generation to operations? And any sort of thoughts there on how -- or maybe give us an example of how Datadog is making that shift left?

Yes. I think, it's not so much about us shifting less, it's more about the value -- more of the value being on the right side than being on the left side. So it doesn't mean we have to write the code. We still think that writing the code and with developers on a day-to-day basis, it's still going to be a fairly fragmented ecosystem. There's going to be lots of tooling, lots of personal preferences that come into play, whereas the systems that for observability and management in general tend to be more platform in that company, maybe developers will use 15 different IDs, but there will be one platform for observability. I think that's where we live. We think more value is going to be delivered that way as opposed to what happens on the coding side.

Yes. One of the interesting side I find covering this space is just all of the different sort of technology debates and because it's a category that's kind of defined by a fast pace of innovation. And so when we think about some of the emerging technology trends, GraphQL, for example, the query data where advances in sort of database technology to store and process data, AI/ML approaches to analyze data. I guess the question is always, how is Datadog ensuring it remains at the forefront of the technology curve as you guys continue to move more and more upmarket?

Yes. So it's a great question. I think there's different parts to that. First part is just cold math, which is we reinvest about 30% of our top line in R&D. And we can do that because we built a super-efficient business in a very efficient go-to-market, which means we have high gross margins. We have room for healthy margin that we can return to investors, and then we can pay for go-to-market and we still reinvest 30% in R&D, which is significantly more than all of our peers. And I think, we invest like 2 or 3x more than our 2 publicly traded competitors combined in R&D. So that's one part. The second part is, and that's structural to the business, we have a very, very broad customer base. We sell 30,000 customers, as you said, and we also have many, many free customers for users on top of that. And about the bottom half of that 30,000 customers only represents about 1% of our ARR. So we don't have those customers because we make a lot of money off of them, we have those customers because they are small companies, new companies, and they take us into the future in terms of the technologies they use and how they build and what they to do with it. We've mentioned a few times over the past few quarters, our cohort of AI native customers. And I know there was quite a bit of focus recently, maybe on 1 or 2 of those customers that have grown faster than the others. But even if you look at the rest of that cohort, like these customers are the who's who of the companies building their future of AI applications. And they are building companies in a different way, and they're building software in a different way. They use different components. And they pull us into that direction with them as they build. So that's a key part of our business.

Fantastic sort of feedback loop into the product engineering organization. One more question for you on the sort of technology side of the house. This relates to OpenTelemetry. And for those in the audience who don't know, OpenTelemetry is an open source standard to collecting telemetry data. It seems OpenTelemetry gets more and more popular. You guys have supported OpenTelemetry quite extensively. You're a top 5 contributor to the project. But in terms of -- as OpenTelemetry gets more capable, is there a concern that it will make it easier for customers to in-source their observability capabilities? And to what degree is OpenTelemetry becoming a threat to the growth equation?

Yes. So we've always been big believers in the data collection to the open source. So from day 1, our agent and everything that lives on the customer infrastructure has been open sourced with a very copy-less license that lets them basically do whatever they want with it. And so if there was ever a threat of customers stating that [indiscernible] themselves instead, that's always been there. We're super happy with the evolution of OpenTelemetry because it works really well. Customers are opting it. It's making it a lot easier to instrument workload. And in the end, the end goal is to have more instrumentation in more places, more penetration of APM and all those things, I think it helps towards that goal. I think where we had quite a bit of work to do was to make sure that the path was as easy and as straightforward for all customers whether they were using core Datadog instrumentation from day 1 or they started with OpenTelemetry or they mixed the two of them. So we spend quite a bit of time working on that. But I think we've got to a very good place there where we can offer everybody the best experience, whether they are 100% on our own instrumentation, 100% on OpenTelemetry or half and half.

You mentioned in one of your previous answers in terms of framing up the opportunities sort of being underpenetrated in the log opportunity. And our recent conversation, there seems to be a sort of reemphasis on getting logs right. Can you talk about Datadog's evolution in terms of its log management product and your recent acquisition of Quickwit, to what extent do you think buying criteria is shifting more and more towards how to effectively manage, process and analyze logs?

Yes. So we see in logs, in particular, we see 2 drivers for customers right now to upgrade and maybe move from an incumbent to or a homegrown solution to a platform like ours. These 2 drivers are being cloudy and modern and well integrated with everything else they do. And the second one is being cost efficient just because the fundamental CRM of observability in general and logs in particular, is that for every amount of data, the limit you set, any application can generate more data than that, and there's nothing you can do about it. So it's very important to have the right cost effectiveness of those product. With our Flex Logs products, we're hitting those 2 things. So obviously, we are very cloudy and very modern and our platform in general is very well integrated and customers feel good about that, but also with Flex Logs, we give them economics that help them reduce their spend from whatever they were using before, but also scale effectively into the future, and that's something that is working very well. We've also, as you mentioned, we've made an acquisition more recently of a company called Quickwit. And the goal there is a little bit different. So the goal is that company was focusing on a -- have built a data store and search engine that can be deployed on-premise for extremely efficient and simple access of log data. And our focus there is to try and bring that into the Datadog platform so we can serve customers with specific needs in regulated industries or in countries where data just can't get out and things like that, so that they can also use our platform. It hasn't been a need that has been very present, I would say, over the past 5 years. But looking at the way the world is evolving from a geopolitical perspective and also in the way more and more primary data might end up in [indiscernible], say, for example, if you interact a lot with an LLM and you want to log all the interactions. Like those interactions will actually contain some personal information, some highly sensitive information, we think it's likely that some of the data might be held on-premise, even though the heart of an application and even a lot of the compute might be held on the cloud.

Yes. Let's talk about prosecuting the enterprise opportunity. The enterprise business has been one of the leading growth segment in the business for the past 1.5 years. In terms of the enterprise go-to-market sales motion, Oli, when you think about 2025, what's the sort of magnitude of change? What do you have plans for the enterprise go-to-market motion? How does that compare to last year? And to what extent are partners becoming a more important piece of the puzzle?

So we're investing, and David, you can speak to that, if you want. Like I know you love the topic of investment, so...

I mean I think we essentially made it clear that enterprises are underpenetrated. They're earlier in their cloud journey. They expanded less than cloud natives in the COVID period. And when you look at our penetration, yes, we have a lot of customers, but we were not really fully penetrated. So what we're doing is, one, we're trying to expand our quota capacity in enterprise selling, and that includes all geographies, but there are many areas of the world in APJ, Latin America. A good example is we had nobody 2 years ago on the ground in India, we were covering it from Singapore. And now we have 40, 50 people in all the functions in India. So there's a lot of coverage to be had. And then you're right, in the channel side in some of these markets, some of the first motion, go-to-market motions is our channel-led in Japan, in some of the areas where currency is an issue in Brazil. And we didn't have as much coverage in the channel. And I think in terms of both sales engineering and support and marketing, we know that we need the whole ecosystem. So starting at the beginning of last year, but really taking effect in the very end of last year, we began to accumulate quota capacity and are going to continue ramping that. Now it takes a year or 2 for that to become productive. But this is a sign of optimism that we think there's a big opportunity, and we're investing behind it.

Yes. To pick up on that question, one of the questions that we're getting is that as Datadog moves upmarket, prosecutes that enterprise opportunity. Does the unit economics, means this great, highly efficient business model that you've built over the last decade, does that sustain going forward as you move further and further up market? And do we see that same level of efficiency, granted that you're making some investments near term, but does the model sort of prove out when you move further...

We were most likely too efficient, right? So in other words, when you look at the return from enterprise investment, given our retention rates, we had really, really high returns. So we think we can grow this and have a payback in roughly the 1.5 years or so period. And because of the land and expand, it's a great return. So I think we expect that the sales and marketing as a percentage of revenues will go up purposefully, but the return profile will be retained because it's so high return.

And we feel good about the investment in the ARI. I mean, we're investing and we're driving up the investment because we know some past results that we will do well with that. And I will say we're not afraid of a degradation of economics. I would even say that if you look at the past year or 2 years, we've had better sales economics on the enterprise side and on the SMB side. Now granted that might be relative to the SMB being more affected by the macro and some of the, I would say, free growth not being as free anymore. But still, we feel very good about the level of economics.

But we're pretty disciplined people. You know that, and we always watch this. So we are going to layer in enterprise investments as long as we think they're higher return than they are.

And that's where cloud is most or at least underpenetrated, that's where the growth dollars are, which makes a ton of sense. Let's spend the last 5 or so minutes Talking about the AI opportunity, some of the debates on. One of the debates that we hear on Datadog is that as we go into a new compute cycle, and this category has been highly sensitive to changes in compute cycle, would there need to be a new sort of AI native approach to observability in the way that sort of applications are built. And is that in any way disruptive to how Datadog is built today? How did you sort of respond to that?

I think -- so again, the first half of it is very traditional observability in that you build an AI application, you're still going to have a database of web server. It's still going to run some infrastructure. And for that, you need observability to tell you how the whole application is working. Some part of it is going to be new in that a lot of the application is going to be nondeterministic and it's going to be powered by an LLM model. So if I were to give you 2 archetypes, like 2 extremes. One extreme is, the code is still all written by developers and engineers, but the machine writes it, and then it runs on a distributed system, et cetera, et cetera, and [indiscernible]. The second archetype is the code is written by non-developers. It might be -- and it's basically a model that is operated by the app itself, and it's more stand-alone. I would say on those 2 ends, we have a good idea of what the observative picture looks like. And we think that the whole market is going to be on a continuum between those 2 sides. On the end where the machine writes the code and the developers manage it, we think it looks a lot like traditional observability. And as I mentioned earlier, the value moves from writing the code to operating it and security it and running it. And on the end, where the application is largely nondeterministic and is a model and not necessarily written by developer, then the job becomes understanding how that model is behaving and whether or not it is performing for the business, and that's also what we do. We do that through our analytics and really the monitoring products, and we'll also do that with our LLM observability product. And by the way, we're starting to see some interesting uptake for some of those products. So I mentioned the [indiscernible] monitoring growing fast at more than $100 million in ARR and really a good leg of future growth for us. But we're also starting to see some real usage and real customers using our LLM observability product. To give you a sense of what that represents, we have customers now that are spending more than 6 figures on LLM observability every year, while these are customers that are non-AI native companies and that have a total observability deal that is in the mid- to high 6 figures. That gives you a sense of the importance it's taking and the place you can find already with customers.

I can't let you go without asking on the agentic architecture question. Whenever I talked about technology trends in the context of Datadog, you always sort of frame it in terms of the level of complexity. When we talk about agentic architectures and agents proliferating in customers' environments, what is the implication for Datadog's business as customers build out those architectures?

Well, we'll need to understand what the agents are doing, which is a form of observability. I would say it's still a little bit early to understand exactly what the plan is going to be for most companies because the agents are still mostly not there and they are little deep, but not quite, so I think we still have to see what's going to happen, but that's definitely a big opportunity. And by the way, as a CEO, if I don't hype the agents, I think I'm going to be fired next week. Obviously, we're big believers in agents. I just think the market is still a little bit early for the production observability of most of them. With LLM observability, we do see some customers starting to move from building and observing chatbots to moving more agency workflows. But that is still largely in test, more than in production. What we see more in production today is the chatbots deal.

Maybe, if we can wrap up the conversation just getting your perspective on where we are in the cycle in the framework of training versus inference. Are you seeing your customers get consequential application in production that's sort of meaningfully impacting the business sort of in a crawl, walk, run framework, whatever framework [indiscernible] look at, where do you think we are in sort of the AI application build-out cycle?

I think we're still early. So I mentioned we have some customers that are making a real usage of our production-minded products and spending real money on those. So that is happening. A year ago, it was not happening, now it is happening. So it's good. We see that. But it's still a relatively small fraction of those customers. And I think the bulk of the adoption is still ahead of us for that. In terms of training versus inference, I think what's interesting now is that there's less of a -- in terms of where the world is going, there's less of a gap between training and inference. In fact, it looks like a lot of the training might happen continuously. It might happen -- many more companies might be doing it than just a handful of hyperscaled model trainers. And so -- and a great part after what we've seen from DeepSeek and the region of attention into reinforcement learning, so I think there's definitely a future where there's not as big of a difference and more of the workloads become production workloads, meaning ongoing concerns for the companies where we can add a lot of value.

And maybe just quickly to end on, it's like could you describe the world where Datadog becomes -- its business is starting to benefit from more -- the cycle getting more mature? Is that a proliferation of enterprise applications or just describe that world of where we are in that cycle where Datadog becomes more of a beneficiary?

Well, I mean, look, more applications, more agents, more value created through software. That's what creates value for us. So we're confident that is exactly where the world is going. And in many ways, in a shape that is similar to what we have today. But in some ways, the shape that's going to be different, and that's why we're working hard to build that out.

Awesome. With that, thank you so much, Dave and Oli, for joining us at the conference again this year.

Thank you. Awesome.

Thank you.