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

Welcome, everybody. Thank you for joining. It's a great pleasure to be here with Olivier Pomel, CEO and Co-Founder of Datadog. Olivier, thank you for joining us.

Great to be here.

I think we'll start off and on a great note. This time last year, you were on stage with us. We highlighted that you were one of only 4 enterprise software companies growing mid-20s plus. We said something very was happening at Datadog year later and looking back, I think maybe we understate it a little bit. You're not growing over 30% at a $4 billion scale. So can you help us in layman terms understand the problem that you're solving out there? Why it's so critical for customers and what do you think the phenomenon that are fueling your growth at scale?

Yes. So I mean, look, we do observability and security. So we sell to engineers and productive at our customers. We help them understand whether the software, the services that they're shipping are actually working. They're working as appropriate for their customers, if they are fast enough and if those products deliver the right business value for them and for their customers. So that's a -- what we do. We serve every type of company from the 10-year startup the new company all the way to the largest and the oldest enterprises and with pretty much everything in between. . The reason why we see demand, I think there's 2 aspects. One of them is we're in still fairly early in a super cycle of digitalization and cloud migration. So we started the company 15 years ago. It was right at the beginning of the cloud migration. That's still ongoing. And that's still a big driver of our business and still something that's going to keep going for many, many years. And then the second aspect is, I'm sure it's not lost on anybody in this room that everything is changing with AI. There's a lot more that is being built that is being shipped in software. There's a lot more interactions that are being automated. And all of that creates new kinds of complexities and new kinds of surfaces that and also, frankly, quite a bit more infrastructure. And all of that needs to be managed, monitored, observed, secured, and that's what we do.

Yes. Yes. I was preparing for this. I want to say you guys made it easy to come with some of these questions had such a good Q1. Yes, I think one of the best trends we've had this earnings season, 32% revenue growth rate, as we mentioned, that $4 billion scale, accelerating for the fourth quarter in a row. Larger sequential ad in a while, a very, very healthy raise, really just a lot of strength across the board. I'd love to kind of decompose that a little bit. Where are you seeing those pockets of strength? I think it's more than just AI, right? Maybe what surprised you? And what do you see kind of persistent going forward?

Yes. So look, the -- as we said on the call, I think on the earnings call, we saw acceleration across every single part of our business. We saw acceleration with the brand-new AI-native companies, whether they're small or very big. We saw also acceleration with the rest of our business, which is even more interesting. So the non-AI part, all the companies that were around before AI is starting taking over. I think the drivers are a little bit different there. I mean, on the AI side, obviously, we see that part of the ecosystem blowing up, like the AI is getting into production, some use cases just coding are very real and scaling very fast. And this is feeding a number of large model companies but all sorts of application companies that are being built around that. So I think the drivers are fairly clear there. What's even more interesting for us is that the bulk of our business, the rest of our business that is -- that existed before AI became a thing. That business is accelerating as well. And part of that is those companies have to -- they understand they need to modernize and monetize faster so they can be ready for part of AI. It is just that we're still very early in cloud migration. There's a number we like quoting, which is that in Gartner has this report every year on the market share in IT, OM and a number of other fields. And where we're the leader in observability, we're the #1 there, but we still have only 13.6% of the market according to them. So this tells you early in the market for us, how much opportunity there is ahead of us, even factoring out all of the new developments with AI, all of the new explosion of demand we can see there. And so we think we're still early in a super cycle there.

Yes. And definitely, some threads will have to pull on in a little bit. But one topic that's been out there for the entire time we've covered you all is this concept of customers doing it themselves, using open source now by putting it and it's been persistent, even though none of the data points really supported that much. I think your also retention is stellar. But this last quarter, you guys started talking about getting the hyperscalers as customers, right? And if there was anybody who could do it -- they do have it, right? They have these tools within their own ecosystem. So if we think about it that way, like if even the hyperscale is concluded, they need to buy from you rather than use what they have. I mean, was that surprising to you at all? And what do you think that says about the value proposition that you have and even the moat that you've built?

So I mean I wouldn't call it surprising, but it's definitely a very interesting proof point because the way we see it is the reasons to build it yourself are usually mostly cultural. That's because you want to build yourself, you want to have your teams do it or somebody on the team wanted so bad and nobody else around them wants to prevent them from doing that. So that's cultural. Typically, you don't get nearly as good of a result when you do that. For one thing, it takes a lot of time and a lot of focus that you should spend somewhere else. So typically, when you try and build it yourself, you end up with a solution to -- in 2 years from the -- to the problem you had last year. And that's if not great. You also end up with despite what you might think when you go into it, much worse economics, and that's not a fantastic solution long term. That's something that many of the hyperscalers, again, are happy to live with because for cultural reasons, they want to in-source everything. Now what we see happening now is there's an extremely competitive situation right now around the development of AI and I think it really focuses the mind for many companies. When they realize, "Hey, wait, actually, instead of waiting 2 year studies, we can have it now, and it's going to run better and cheaper in the end." So what are we doing? What should we be doing there? So again, our business model in the end is not to serve to hyperscalers. There's not enough of them. They are too focal also for cultural reasons, as I mentioned earlier. But for us, it's a great proof point. Even the companies that have usually unlimited access to top tenant and a strong cultural by us for building use our product. It means that it really doesn't make sense for anybody else to build their own.

Yes. Yes. I think that it's an excellent ironman argument for what you guys have been differentiated. And I recently had a conversation with a partner where you made a very interesting point that I hadn't thought about, which we're asking about -- coding all that. And their comment was AI has accelerated the pace of code being generated, and it's increased the competition because everybody can do it out there. And so the effect that it has is the opposite is now if you don't use something that's really trustable and powerful like data Datadog, you might actually be hindering your development process, and that's your bread and butter. So in fact, the effect is that it makes people more reliant on something like that. Do you see that dynamic?

Yes, definitely. And look, most of the -- actually, pretty much all of the top 8 or 10 coding companies or -- coding companies, whether you're talking about the models people using production or the large companies or the products that are built more for the consumers, all of those companies use Datadog behind the scenes. And so that tells you about the very specific need there is there. Look, at the end of the day, we see there's so much more stuff that is being built it's being produced so much faster that, by definition, the folks who produce or that have no idea who it actually works. That's the least understood thing about productivity is that the more you increase productivity, the more complexity you create because folks manipulate way more things in way less time and as a result, doesn't go through their brand and they don't understand what's going on so they need help to actually understand what happened to actually make sure it works properly to make sure it delivers what it's supposed to deliver for the business in the end to make sure it keeps working, when everything keeps changing around it to make sure it's secure, and that's what we do. .

I think a great point to kind of get into the AI native cohort that you guys have 22 native, spending more than $1.5 million spending more than $10 million. And a lot of the focus tends to go on some of the larger ones. But think the impressive part is how diverse even that said, has got foundation models, Cogen, Vertical AI. So when you look across your book, where in the value chain do you kind of see the strongest opportunity? And what is the differentiation you have for those customers?

So I mean, those customers have like a high pressure to deliver a lot and deliver very fast. And look, the core of what we do is what they all start with. So we cover everything from end to end from the bits that go through the CPU, the network, the GPUs all the way up to the end users, if they're using a product, where are they coming back, whether they're completing what they're supposed to be doing, how much business value to generate for you in the end. And we cover absolutely everything in between in a way that's fully integrated. If you go to any of our competitors' website, they all said to do it, the reality is it's actually really, really hard and really differentiated to do it well, and that's why those companies all use us. This is -- when you think of the investments they are making, whether it's on their engineering teams, their research teams, their GPU fleet, all of that goes to waste if you do not be able -- understand how to build the right thing and then ship an experience that works for your end users.

Yes. Yes. The needs perform is the highest for them. I think one of the most striking comments that you had on the Q1 call was the change in posture with regards to training. I think the quote was last year, we said training was not really a market for us yet, and we're actually starting to see it become a market. So that's -- it's a big step function change, I think, in people's mind. When did you start happen? Was it -- maybe you don't like the word surprise, but what is it surprising to you guys? And what do you kind of see about that opportunity looking forward?

Yes. So this one was actually a bit of a surprise, yes. So it made intuitive sense to us before that training might become a market and because we saw -- look, a while ago, training was mostly pre-training. It only made sense for 5 to 10 companies in the world to do it towards large scale. It was completely homegrown not a great fit for building a product typically. But we saw that the technology changed quite a bit. So models went from being mostly pretrained to being largely post-train. The post training was becoming increasingly specialized to different types of verticals. The stacks that are used for post-training also are becoming richer and richer. So now when you post-train a model, you're going to run all of those different environments, basically, you're going to run all the applications you can run, simulate behavior in those applications capture what comes out. So you end up running way more complex tasks for doing that. And then we saw -- instead of having 5 to 10 companies doing that now there were 50 to 100 so intuitively, it made sense to us that something might be more interesting there. we were surprised, though, to see a number of different customers, including multiple hyperscalers come to us for training at about the same time and which tells us there's something happening and there there's potentially the emergence of a new market there. Again, too early to call it because the technology is changing fast as the markets are changing fast. But if we get into a situation where we go from 50 to 100 of these companies to 500 to 1,000 to maybe more, then that becomes a really interesting market and there's a real problem to solve, and we can do it. The other thing that's interesting, if you look at the evolution of the technology is we're probably going towards models that can learn on an ongoing basis. So instead of training a model, whether that's free trading post rating, if you keep improving your model with online evaluations and online improvement, then that becomes really very much ongoing production concern and something that you can repeat in many, many, many different companies over there. So we are hopeful interested in that -- it's not the core -- it's still small compared to the inference business compared to everything else that happens in the stack that our customers are running. But it's an interesting new green shoot, I would say.

Yes. Can you -- do you have a sense you said they kind of all came to you at the same time. Do you have a sense of what the trigger was from their perspective? Was it a lot of this stuff going to post training? Is that just something that makes more sense for you guys they don't have solutions for it like, what was the catalyst?

I think the -- a lot of these companies are on a bit of a similar clock. So everybody woke up to ChatGPT at some point and then they will have a number of internal efforts started in a number of different places. Some of these efforts work on don't yet you have these cycles that committees leave through and when something didn't work exactly the way you want, initially, then you try and reset and do things differently. I think part of it is that part of companies being somewhat aligned on that on the same because the old competes in market.

Yes, yes. Okay. So let's think about the fact that blossoming out of AI, right? We're seeing this move, like you said, beyond just a handful of companies. I think there's been a lot of indications from people across the stack in the software space talking about that hinting at it. I think maybe that's underappreciated by a lot of people. Like I said, a lot of focus just goes to those kind of very well-known LLM companies. But could you provide your thought on that? You said it might democratize quite a bit more, right? So both on the training and inferencing side, whichever one you going to dig into, like how are you seeing that the marketization happen? Are we literally in the very first inning of that? How do you see that curve growing?

I think we're still super early. I think the focus for most companies or most of users of AI today used to make sure it works. So everybody is getting from step one is, let me make it work once -- and then step 2 is, okay, so now let me deploy that at scale across my company across similar use cases and things like that. I think we're still very much in that phase. We are not at all in the phase of okay. So now what else can I build on that or how do I rationalize it? Or how do I optimize that's for later. . That later might come sooner if we see the explosion of the AI company's revenue continue at the rate it is right now, that money is coming from somewhere. And it's -- there's probably going to be a push for rationalization sooner rather than later. But the mode right now is still very much, let me get it to work. Our mental model for what the market looks like in the end is that it's similar to what you see in the overall cloud infrastructure or even the database market. Like database market you have a number of options up there. You can have -- you can buy a close source database that you're going to run yourself. You can buy close-source cloud data business that you don't run yourself that are completely black boxes or turnkey and if somebody else is running them for you. You can use the prior ones that you run on infrastructure somebody else provides for you. You can run different source one on your own infrastructure. You can build your own databases and all of those actually coexist like there's reasons for all of those to exist and customers typically are going to mix and match a number of those. They're going to have clinical providers they're going to have different database providers, we're going to have a bit of everything in there. My guess is we're looking at a market that looks like that for AI inference.

Yes. I want to get to non-AI cohort accelerating, which I think is, again, a big part of the story, but one last one a couple of years ago, we kind of gave you this analogy at least that was our thinking, which trainings like a bottle or a at an inference is going to be like a steady compounder to get the time you kind of agreed with that framing. We touched on it a little bit, but do you think that maybe that framework has changed a little bit where the training might be a little bit more of a steady compound there on its own as well?

So it's inching closer to being an ongoing recurring thing but it's still a little bit one-off, like what we see still you have these large training runs or these small and as renting rounds, but there's still basically runs like you do a run, then you do another 1 and then maybe nothing happens for a few days, then you do another one. And these also tend to be still somewhat like some coded, like you don't have like a standard way of doing it that every single company out there is using. So it's an improvement from where it was a couple of years ago where again, only a handful of companies were doing needed was extremely hand-cooly not production minded in general like it was kept up basically by people may be seeing those training jobs in night and day. And being like very large pretraining runs and then nothing after that. but we're still not yet at the point where it's an ongoing, always on every day of the week, live with customer data kind of operation. I think we still -- we still don't know whether the market is going there. We think it might be, but that's for the future.

Yes. Got it. But moving to the non-AI cohort. So the core customers they've been accelerating, I think, every quarter for a little while now. As you've said, as we've said many times, your story is about a lot of customers and a lot of things going in the right direction. But it has been accelerating. So I'm sure the pieces are kind of similar, but what's changed? I mean, a year ago to now, what is going on in this core drivers that's pushing that a little bit higher?