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

Okay. Good afternoon, everyone. I am Mark Murphy, Head of Software Research with JPMorgan. Great pleasure to be here today with Olivier Pomel, CEO and Co-Founder of Datadog. So Olivier, I just want to say thank you for taking the time to be with us again, and it's great to have you here at the conference.

Thank you. Great to be here in Boston.

So it's interesting when you run through and look at the stats, Datadog is 1 of only 4 enterprise software companies with at least $3 billion in revenue and growing mid-20s plus. The other 3 are Palantir, CrowdStrike, we were just talking about and Snowflake. So clearly, something very rare is happening at Datadog. Can you explain in layman's terms, what is the problem that you're out there solving for customers? And what do you think is the phenomenon that's fueling that level of growth and scale?

Yes. So as a reminder, we do observability and security for cloud environments, which means we sell to engineering teams primarily. And we help them understand whether their applications, their systems, their AI, all of that is working properly, it's performant, it's doing what it's supposed to do for the business and is secure. So the trends, the mega trends that make us successful are digital transformation, cloud migration and now AI transformation. This is the specific motion or specific trends. But if you zoom out, really, what -- the problem we solve and how we keep delivering more value to our customers is that we help them make sense of all of the complexity of their systems and their applications. If you replay what happened over the past 50, 60 years, in technology or in software, in particular, there's been a dramatic increase of productivity. It's been easier and easier to write applications. There's been more and more components of the shelves that you could combine, whether that's software libraries, cloud components, SaaS, advanced languages and now AI models. What you see happening pretty much everywhere is an escalation of complexity, a complete explosion of complexity, and the humans are having trouble keeping up with that. And that's what we help them with.

So Olivier, you have over 30,000 customers. They're using Datadog to observe what's happening all across their own hyperscaler environments and basically to keep it running. So it gives you quite the vantage point to comment on and really try to understand the trends. And we look back on it and interestingly, in Q4, while the stock market was rising, it was a very disappointing quarter across all 4 of the top hyperscalers. All 4 of them either missed or guided below in Q4. And then after that, you had the threat of a trade war coming in. The market was sliding. And now in Q1, the hyperscaler results actually felt a little more stable. When you look specifically at Azure, it accelerated, it accelerated and surprised quite positively. How are you reading the tea leaves on cloud activity for 2025 amidst potentially a trade war? Certainly, the headlines are changing every day.

Yes. We try not to look too closely at what happens quarter-to-quarter to the major cloud providers. And one reason for that, at least in relation to what we do at Datadog is that a lot of those gyrations and changes in revenue are tied to the supply of GPUs, for example, and things that are, I would say, somewhat decorrelated from market demand and growth trends. So you'll see in the longer term, all of that even out, but quarter-to-quarter, it's quite hard to make sense of it. From our vantage point, though, what we see is that cloud migration is alive and well. It can accelerate a little bit at time, slow down a little bit at a time, but it remains around a certain trend line, and we expect it to continue for the very long term. We also expect it to accelerate and have a longer runway, thanks to the AI transition that is also happening now. And of course, digital transformation and cloud migrations are prerequisites to AI transformation. And I think all of our customers, and we can talk more about that, but all of our customers, whether they're AI natives or traditional enterprises, all of them realize that.

Okay. So core trends, pretty healthy and resilient. And you've been consistent on that, on stage here, I think, over the course of several years. Thinking back to March and April, we did hear some feedback that companies in the EU and actually Canada becoming a little hesitant, right, to put more data into a U.S. cloud provider, Amazon, Azure, Google, et cetera. And there was this comment that they were realizing that they need an Airbus of the cloud, right, to maintain independence, a local cloud provider. Do you see anything tangible happening there? Do you think the bark is louder than the bite?

We do hear that feedback when I -- so the last few times when I've been in Europe, I've heard definitely the intent of owning more of the data, hosting more of the data locally, giving more business to local players as opposed to relying too much on global players and in the U.S. in particular. Now that being said, we don't actually see that much of an impact at this point because there are no viable options outside of the major cloud providers. The -- if you go the next step down in technology is quite a bit less performant and quite a bit more expensive. And I don't think it makes sense even if you have the intent of giving more business locally to -- for alternates. Now if you look at the way the world can evolve over the next few years, I do think there's going to be more of a motion to at least host more of the data locally and have more local governance. And as far as we're concerned, I mean, look, we -- first of all, we'll go where our customers go. Like if they want to run data a certain way, like we will be there for them. But I also think it creates an opportunity for a company like ours. Hosting data in many different geographies, jurisdictions and having a lot of different residency laws to comply with is a huge headache, it's a big problem. And I think most companies will need help and they'll need our help to manage that. So I think long term, it's an opportunity.

So it's more talk than action. You do think there could be a trend line there multiyear, but net-net opportunity for Datadog?

Yes. And I think the real battlefield in the, so to speak, in the short term is going to be AI and the ownership and the control over the AI models. And I think that's going to drive the decisions that are being made for the rest of the data centers. To put it another way, if what you really want is to be able to build and host an AI model and if for doing that, the only available option you have is the existing hyperscalers, you can go with the existing hyperscalers. You will not wait 10 years to do it. So you can build your own local equivalent before you can train AI models.

Okay. Very helpful perspective. So I want to think back for a moment, Olivier, and the mood out there was very different 3 or 4 weeks ago. Coming into this earnings season, we were saying that investors were too pessimistic, and we were emphasizing that really, the hard data wasn't changing as much as the soft data, right? So we were saying the mood has changed, but the activity hasn't really changed. For Datadog, the Q1 results, though, were very solid. And what stood out was you had -- sort of the bookings health, you had backlog growth or CRPO had actually accelerated noticeably, it accelerated to 30%. And then 8-figure deals, you had done 1 a year ago in Q1, you did 11 this time. So what do you think drove strength during Q1? And why did you have so many customers booking that much business given the kind of environment we've been in?

Yes. I mean, look, I think there's really 2 reasons. The first one is again, we're still early in cloud migration, and cloud migration is going well. If you -- I think one of the stats we disclosed was that we have only 45% of the Fortune 500 that are customers today. And the median ARR with those Fortune 500 is less than $0.5 million a year, which tells you that there's tons of growth to be had with all those customers. They are still early in their migration in general. And for them as companies, their cloud spend is a relatively small part not only of their IT budget but also of their top line, meaning that that's the part that they're going to invest in, that's the part that's transformative, that's not the part that is at risk if they're going to face issues in the short term. So that's number one, a very healthy market. The second reason we did well is that we've invested over the past year, especially in the second half of last year in building up our sales capacity. And that sales capacity is coming online, and we see great ROI and great productivity there. And even though we're a leader in observability, I think there were updated Gartner numbers this week on the market share. We're also taking share. We're growing faster than the rest of the industry.

Okay. So cloud migration is doing well, the sales capacity kicked in, you're structurally gaining share. We had showed data, again, coming into this earnings season, Olivier, that the current environment was actually looking -- it's less severe than what we saw during the COVID lockdowns, and it is actually less severe than the onset of, we call it the software recession, which was the second half of 2022. We thought investors were expecting this environment to be worse than those prior cycles. Can you compare and contrast what you're seeing now? Because you see the day-to-day consumption, but you also see the pipeline looking forward.

Yes. I mean if you look at what happened, at least seen from our business, so during COVID actually was fine because that was the explosion of online, et cetera. What was trickier for us was the end of COVID and the flattening of the demand from all of the cloud native companies, basically, which were the ones that were spending big, they were done with the cloud migration, they were fully at scale in the cloud and they tried to save as much as they could in as short of time as possible. So that was fairly painful, and you saw that in all of the numbers we released at the time. Today, though, if you look at the current situation, the companies that are growing faster, like those that replaced the cloud natives are the AI natives, and they're accelerating, and they are still fairly early in their runway. And the bulk of our business, the bulk of the demand we see is these larger enterprises that are still fairly early and that, as I said before, only spend a small fraction of their OpEx and an even smaller fraction of their top line on the cloud, and that's really what they're investing in. So from where we stand, we clearly don't see the same kind of pressure. Now obviously, if things take a turn for the much worse, like everybody is going to try and save money, it's going to be more difficult for everyone at every single level. But today, in the numbers we have and what we see in consumption and what we see in the booking side or the willingness of customers to do deals, we don't see any impact.

Have you tried to look at that specifically in the industries that are most heavily tariff impacted? We think of -- because we -- to be fair, we had a wave of pre-announcements from airlines. There were a bunch of retailers that had problems. There were automakers having problems. Is any of that looking like the canary in the coal mine right now in the data?

We don't. But again, the spend of the economy is fairly small at this point. So we had actually a great quarter in Q1 for traditional companies. Q4 also was great for these. We mentioned in the earnings call like one of our best new logo deals was a car manufacturer. We actually signed 2 car manufacturers that same day, I remember. We signed a few airlines over the last few quarters, and those are growing nicely. But again, the budget we see, the spend on us, that spend that is growing is part of the transformational investment, that's not part of the much larger carrying costs they have for their supply chain, their factories, their operations, their aircraft that I think is what trends...

Okay. So it's more of an insulated pocket for you. I'm going to talk for a moment about the AI native trend, Olivier. It's -- Datadog has just clearly stood out for developing one of the strongest AI tailwinds really across the entire software landscape. The AI natives reached 8.5% of your ARR in Q1. So it's really -- it's actually Microsoft and Datadog are the 2 companies that are quantifying a really substantial tailwind at this point. Can you help us understand who are those companies that are driving AI for you? And then why do you think Datadog is so linked to it?

Yes. So I mean it's pretty much the newer incarnation of the cloud natives. I think if you started a company in the past 2, 3 years, you're very likely an AI native. If AI is not part of your pitch, you probably cannot raise money is my guess. So that's mostly newer companies. We do have revenue concentration in that cohort of customers. We have one customer that's now our largest customer that is meaningfully larger than the others in that cohort. But we also see diversity of emerging winners in that cohort. So we now have more than 10 companies that are AI native, that are over $1 million in ARR for us. And those are growing both as businesses and also in terms of their consumption and their consumption of cloud in general. And when you look at the makeup of those companies and what it is they do, like they cover the gamut of what you need to build in AI. So there are infrastructure companies for AI, there are model builders, there are agent companies, whether they are coding agents or legal agents or other kinds of business agents. And then there are also various applications that don't necessarily build models themselves, but that are built on top of these models and that generate value based on that for consumers, for example.

So -- and if we drill down into that and look at how they're using it, I think Datadog isn't really getting involved so much in the training side, right, where they're building the models. You are involved in the inferencing stage. So in other words, product reaches commercialization, and then you're getting involved. Inferencing feels like it's a lot earlier stage to us and that it's probably going to have more durable, more explicit growth because basically, you just look around and say, "Well, there's so many models that are still being built." Would you agree with that?

Yes. I mean -- and to level set, I think a lot of the training today is still very -- like it's more of a research activity, and it's largely one-off, homegrown. And I would say, a smaller fraction of companies are doing that at scale. Inferencing is where the action is. That's where you actually have to serve customers and you scale with the demand and you provide value. And typically, when you -- even when you're a model builder and you ship a model and you have customers connect to it, like that model just doesn't live on its own, like there are other layers on top of it, databases behind authentication systems, firewalls, everything that you would find in a traditional application that needs to be monitored with it. Also, as these models and AI companies are becoming more and more sophisticated, the models don't operate in a vacuum anymore. Like many of these models get smarter and smarter by becoming agents and by using various tools. Those tools need to be run as well. So if I take the example of what we build at Datadog, we have -- we're building agents to automate a lot of the work that SREs and engineers are doing. As part of that, there are models but then those models run queries, they ask for data, they run automation scripts. And all of these different things are applications that need to run. And so what we see is as more and more AI gets adopted and these applications grow, there's a bigger diversity of components that need to be monitored, and that's exactly what we do.

Okay. So it's been an interesting journey, Olivier. For the last year or two, when I will ask institutional investors, what do you think of various AI products or copilots, a lot of people shrug their shoulders and seem a little unimpressed at what had been out there. We started commenting recently that the killer AI product for the buy side has finally arrived. It is here. That product is called Deep Research. And the response that we get is very different when we ask about that. People say that it's amazing. It will take a project, it will divide it into sub tasks, it will write Python code for you. Basically, it saves people a ton of time. What do you think these -- what do you think the advent of these reasoning models is going to do for growth of inferencing?

I mean we think we'll see acceleration of the growth there. I mean you're right that the reasoning models and the improvements of models and the ability to use tools really helps deliver more value. We've seen that internally. We see that with our customers that are using those products, and we see that with our customers and are producing those products. And so we think there's going to be, as I said, many more diverse applications to monitor with these kinds of reasoning models.

So they have a different level of complexity, they have a different level of compute intensiveness. So the -- sometimes, there's 8 GPUs clocking at once. Does that make it more important or more of a challenge to try to observe and monitor those environments?

Well, I would say it depends. If you're a model builder, maybe you've built a lot of that technology already. Like you built them all, you understand exactly how it runs, maybe you run -- you built some of the technology to understand what happens within the model. But even if you're a model builder, if your product is, say, an agent that is going to crawl the web and that is going to use a browser to try and simulate your actions and book flights for you and things like that, which you've seen those products and those agents are there, and they're getting better and better every day. Like you will need to run infrastructure that is crawling the web. You will need to run infrastructure that is running these agents in sandboxes and all these browsers and sandboxes and recording them and storing the images. So you're going to run a very, very sophisticated and diverse software stack. And all of that, at the end of the day, you'll see maybe 5%, 10%, 20% of your compute is going to be in the model itself, but the rest is going to be the rest of the applications that are here to help the model to fit them all with information and to have them all do its job.

And so one of the big hurdles that these LLM providers will have is dealing -- they're trying to deal with the bias in the models, the hallucination in the models. They're trying to deal with the drift in the models. And it has come to our attention that Datadog can help them with that. Can you help us understand what are the mechanics there? What does that value proposition look like?

Yes. So we have a product that is LLM Observability. And the questions that, that product answers are around, is my model -- first of all, the basic stuff. Is my model up? Is it working? Is it fast? How much is it costing me? So that's the very basics. And then beyond that, you can ask, is my model correct? Is my model safe? Like is it leaking data? Is it saying what it's not supposed to say? Is it saying what I'm expecting it to say? And then the last step, which is even harder, and I think that part we're still building is, is my model doing what it's supposed to do for the business? In other words, when folks interact or if I have end users that interact with the bot, for example, or with a feature that involves some open-ended thinking, what are they doing after that? Are they buying more? Are they staying longer? Basically, are they doing what I want them to do with the system? So we're doing all that. I think -- and that's with the current iteration of those models, and we expect those models to evolve a lot. I mean we already see a move from our customers using these models in chatbots to customers using them inside agents that are always running and don't necessarily require an agent -- a human to prompt them. So we're seeing an evolution there already. But if you zoom out even further and if you look at what might happen in the future, if more and more of the application is not coded, but is emerging and is stochastic and is this model that is somewhat unpredictable in some ways, we see that there's a ton of value to be provided by observability, because the value goes from initially packaging it into your model to understanding what it's actually doing every day in a real situation with real users and the host changing over time.

Okay. So then -- so that sounds like it would play into the AI tailwind that you have had, which you disclose that as coming from AI natives. And I think -- so it's the big model builders, et cetera. One of the big questions is when do we think enterprise adoption of AI is going to start kicking in? And how do we want to try to forecast it? Like in other words, when do you think a big bank, a big retailer, a big pharmaceutical company is going to be done training a model and it's going to actually be deploying it and then, therefore, driving revenue for Datadog?

I think the best way to think about it is to look at what the cloud natives or rather the AI natives are doing and see that as the future of what the rest of the market and the big enterprises are going to do. So if you caricature, you can think of 3 steps in the AI maturity. The first step is you test applications with third-party models. The second step is you scale those applications with third-party models. And then the last step is you keep scaling with applications, but now with some homegrown models. And if you look at the AI native companies, many of them are between step 2 and step 3 now. So we have -- we see a lot of companies that started by building on top of third-party models that are reaching some market fit that are growing very fast and then that are trying to -- or starting to augment these third-party models, these homegrown models and maybe even replace some of those models with homegrown models in the end. When you look at larger enterprises, we are between step 1 and 2 right now. So they're between testing and in some cases, they're starting to scale some of those applications. That's where we are. But when you look at the incredible growth of our AI native cohort, we see that really as a sign of the future demand we're going to see from those enterprise customers.

So what's happening with AI natives will inevitably trickle its way out into the broader landscape of enterprise?

Yes. And it's very similar to the -- like we've seen that movie before with cloud migration...

For sure...

When we started the company, there was absolutely no interest from enterprises into the cloud. I remember the first time I pitched a bank you might know, I was -- I think...

I was going to say it sounds familiar.

But that tune change pretty quickly. I think it became to be understood as not only viable for the large enterprises projects, but also a big competitive advantage and a true way to transform. I think with AI, everybody understood much faster than it was going to be a competitive advantage. I think there are questions about the safety of it and how fast the transformation can happen. But to us, there's no doubt that the larger enterprises are going to follow in the footsteps of the AI natives.

Okay. So AI, Olivier, is also impacting code writing itself very rapidly. And you may have seen the CEO of Anthropic recently said that in 12 to 18 months, 100% of all code could be written by AI. And I'm sure there's a bit of hyperbole in this as always. But can you speak to how that trend might play out for Datadog? Because I think, in theory, more code being written more rapidly, more applications being deployed, there's just more out there that needs monitoring.

Yes, I mean, that's right. I would say the opportunity is even bigger because when you think of the whole continuum for delivering value and delivering applications, right now, most of the time is spent coding still and conceiving the applications. And then after that, less time is spent bringing into production and making sure it works right. I think as more and more good can be written faster and without necessarily the intervention of humans, we have these situations where the humans have all these suggestions, all these lists of things that the machines have produced, and then they are the ones who need to validate it and make sure it actually works and it's secure and everything else. So we think we can do that. We think what becomes valuable and the problem that is truly valuable in the end is understanding how that code actually does what it's supposed to be doing. Is it helping the business? And also is it safe? Is it running? Is it -- how is it changing over time? What happens when other components that interact with it change over time, and how does it behave in production environments? So I think it's a huge opportunity for us.

Is AI helping Datadog itself? Is it helping you write code faster? Are there any other AI efficiencies? Like are you -- is it -- is AI handling support tickets for you?

Well, we -- I'll give you just a quick example on that, so -- just to show the acceleration. When we first started adopting Copilots, it took us more than a year to get the whole team to adopt Copilots. And the reason for that was that -- for coding specifically. The reason for that is that it was fairly helpful in a number of cases, but also fairly disappointing in a number of others. And as a software company that builds a lot of low-level software, databases and optimization systems and things that are, I would call hard engineering problems, we have engineers who are quick to dismiss output that is okay, but not great. That thing is not -- I mean, yes, it gives me something, but it's not great, so I'm not going to use it. Now fast forward a year later, when we started adopting coding agents, we -- it took us just a couple of months to have the whole team pretty much adopt the coding agents. And the reason for that is it's that much better. And everybody from the enthusiast to the skeptics, everybody sees the value and start adopting them much faster. And as part of that, we see more and more of the code that is being written or at least influenced by AI, and we think that, that progression is not going to stop.

And how about the progression of DeepSeek? When DeepSeek kind of dropped onto the landscape, which was back in January, we had hosted a large investor call, and we had a contact saying it's going to reduce the cost of inferencing by 90%. And I think one of the questions is, does that cause a flywheel? If the cost of building an AI model comes down, then are you just going to have a lot more AI models coming out into the marketplace? Do you see any lasting impact from it?

I mean we see much more enthusiasm for the models in general. I think there's two main many impacts. The first one is, yes, if the cost is down by 90%, it means you're going to do 10x more of it. And some things that were impractical before, because the AI model was too expensive to run. And really, if you wanted to -- for it to have a good chance of being right, you needed to run it 100 times. Now you can do that, before you could not. So that's one of the effects. The second effect we've seen is that it really was a wake-up call for many companies that had sort of decided to believe that you needed $10 billion in investment and 200 researchers to build differentiated AI models. And it turns out that you don't, it turns out that many companies in their domain can have an impact, can innovate, can build state-of-the-art models. And we've seen many, many more companies start investing and start building those models. I think as a result, we probably are looking at the future where it is less likely that we will see the AI innovation concentrated in 1 or 2 players, and that there will be a more robust ecosystem. There will still be leaders. There will still be dominating companies that will capture large parts of markets where massive investments can be brought to bear against a variety of use cases. But I think we'll also see a lot more specialized vendors and local vendors that will be able to innovate there.

So Olivier, in the remaining several minutes that we have, I do want to ask you about philosophically how you view investments especially in the headcount. We published these statistics every quarter where we try to look at hiring trends across the software landscape. And basically for the last 2 or 3 years, it's been very sluggish. After I got off the stage with Microsoft today, Microsoft announced a 3% layoff. And so it feels a little different where you have growth companies that are very healthy and very strong, and they're trimming out some of the headcount. Datadog feels like more than any other company, it feels like it's investing to win. And you took head count up 25% last year. What are you seeing differently with respect to this overall investment cadence?

I mean we see that we're early, and there's so much white space, whether that's on the product side or on the demand side and the market coverage, that we keep building the engineering teams, and we keep building the sales capacity on the go-to-market side. We're constrained by capacity in both situations. I would say the only limit that we've given ourselves is we invest around 30% of our top line in engineering, and we keep that going. And if we get more efficiency with AI. We'll probably keep investing that because we'll be able to produce more and we'll be less constrained on capacity on that side. I think that's the equation.

How do you think about -- in the last minute that we've got here, we've always felt that the best software companies out there, the ones that you want to align with, they're going to try to consolidate point products, they're going to try to put it on a single platform. But the key thing to look for is they're going to do it organically, right? They're going to be builders rather than acquiring. And we see that very clearly. The -- one of our discussions said there's growth in every Datadog contract that I see. Culturally, how do you think about preserving this level of organic innovation and kind of avoiding the pitfall of becoming the big slow-moving company?

Yes. I mean, look, we're -- again, we're building a lot. We have a very humble attitude to customer conversations in terms of -- whenever we talk to customers, we're here to listen to them telling us what the problems are and what works and what doesn't. We're not here to tell them how the world should work. And that gives us a lot of insights on what it is we need to build. And the other thing we do is, as a company, we're fairly disciplined about understanding what's valuable and what's not. And we do that by having fairly transparent pricing in terms of what we charge for, what we don't. We don't do heavy bundling. We don't do -- we don't keep adding features to the same stuff, whether that works or not. We try to have very clean signals to keep ourselves honest so we know what valuable and what's not. And we want to keep that going as long as we can.

Great to see the discipline. Thank you for keeping the Internet running for all of us. And thank you so much for joining us again, Olivier.

All right. Thank you.

I appreciate it.