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

Hi everyone, thanks for joining. This -- one of the last sessions at this conference. Probably the last thing between you and Smith & Wollensky so we'll try to make it interesting. I'm Kingsley Crane technology analyst here at Canaccord Genuity. We're really excited to have Datadog with us here today. David Obstler, CFO. David, thanks so much.

Thanks for having us. I hope it's a meaty discussion for Smith & Wollensky, will see.

Indeed, -- so let's kick it off for those of you that aren't familiar, -- let's start with the quarter. 36% growth, $1.1 billion in scale, accelerating 32%, you've accelerated in the past 5 quarters. Just what were your takeaways from the quarter? And is there any simple way to describe what's going so well right now for the company?

Yes. It's really a combination of our investment in the platform, which has expanded the product line. That's enabled us to cross-sell and also take market share. We're seeing strength across -- all the way from SMB to enterprise and globally. And in order to distribute that, we've also successfully expanded our go-to-market. And then the environment is pretty good where we have the AI but in the overall customer base, there's a strong investment in platform right now partially to take advantage of what's coming with AI and that any time we have a replatforming and a modernization effect stack that's complemented Datadog and their growth. So all of that came together, and it's been compounding over 4 or 5 quarters, which produced the results in the quarter.

So there's this idea that because you've been able to monetize AI native so well, has worked well for you that growth is top heavy or concentration. Reality that's not all that true. You've seen non-AI accelerate for multiple quarters from high teens to high 20s. What is going so well in that segment for those incumbents? And is that driven by AI adoption or...

Yes. That's -- so that's -- so our -- this has been 4 or 5 quarters, as you mentioned, of acceleration up in the upper 20s, and that's enterprises and stuff. That's the adoption of the Datadog platform. That's winning marketer. For instance, in the last quarter, I think we said sequentially, we grew $115 million of revenues. So if you just want to -- on a quarterly basis, think of that as over $400 million of business and you look at that versus the competitors, you'll see that the market share gains are very substantial. And it's all because of the adoption of the platform, the fact that our end market wants to look at single pane of glass and in real-time observability and security, it's a real premium on having it all netted together. And that's really resulted in pretty strong adoption in a number of different areas. It's anytime technology has changed, et cetera. We've also had all the modern workloads tend to go to Datadog. -- for observations. So there's probably also going on more of a weight towards modern workloads, some of which are AI-enabled. It's been very broad-based. And as you said, very strong. The AI natives themselves where we've also won a lot of market share, have complemented that. And in that I think we said it's pretty diverse. We had over 750 names, over 30 of them having 1 million, 10 of the top 10 -- so that means that, that group of cohorts is adopting Datadog on top of the overall environment and the overall business accelerating.

So we've had this for a number of years, how to judge breadth and product adoption Yes. And we've moved from 4 to 6 and 1 of the premier stats is 10 Yes, and that's doubled in the past year. What happens when a customer moves from 4 to 10, like kind of give us a sense of that adoption timeline and then when a customer is consumer buying 10 products, like what does that look like as a percent of their IT spend.

Yes. Well, in terms of the land expand. So all our clients, when they land with us, they have other vendors that have been there for a while, right? So -- we don't tend to have everything switch all once. But what's been happening, this has been going on for 5 years, the weight has been to when those other contracts come up for renewal to use more data products. We sell on a credit basis, we sell $2 million of capacity and they can use the platform. So we find when we get that is we find that adoption. Our cohorts are very, very long, meaning our cohort signed 5 years ago, it's still expanding. The reason they can with us is they consolidate on Datadog. And part of it is that we've expanded the product line so much -- so that's kind of what happens on an ongoing basis. That produces the net retention that we talked about in the 120s, low 120s. And that's been pretty persistent over a long period of time. That's something very important to look at, like you said, each year expand the definition of cross-sell to the number of products, and then we tend to fill it up pretty fast.

So you were early in winning AI natives versus many of your peers and that's grown wonderfully for you -- some investors weren't really sure what to do with that category they should underwrite -- we've been particularly excited about the category, and it's grown well. But how about for you? I mean like what gets you excited about AI natives? And do you think that, that kind of helps prove that years getting to where the puck is going in terms of the product.

Yes. I mean it definitely does. It's AI, AI native, and we can talk about that momentum in native. So we've always been very successful in what we used to call cloud natives. Now they're AI natives. This group is a much smaller group as a percentage of ARR than when COVID happened, but it is growing very fast. And it's the who's who. And it basically says that if you're essentially investing in modern technology, those companies do not have legacy systems. They've been invented over the last few years. There's no displacement of something else. It isn't there. And so they're adopting Datadog early on. And I think it's a very strong endorsement of where the as you said cause. This is being pervasive whether you're talking about the model providers, the database providers, the verticals, the GPU providers. They're going to be tool companies, infrastructure for the overall digital economy and they're choosing Datadog for monitoring. So it's a really good forward-looking sign. In terms of a question that's come up, is there going to be volatility to the air. Yes, there might be -- there might be -- I mean, there's volatility in all these markets. But what is most important is you're compounding where technology is going over 5, 10, 20 years, which we've been doing successfully.

So I want to build on that -- and then there's been before AI, the largest technology organization would always buy procure and build technology very, very different. And so the fact that you've been able to win a hyperscaler labs or some large native is a big testament that your product is that useful that they want to use it rather than build it themselves -- that being said, talk about like maybe usage trends among large customers like did you get to that scale? And then just even how that conversation goes when you get to 8 figures or 9 figures of spend?

Yes. I mean we've always been with larger customers -- it's always been not one or the other. They do a portfolio of things and they tend to put their more modern workloads and mission-critical workloads be observed by data dogs. So there's always going to be the back and forth. The net weight has been towards not doing it yourself, but buying Datadog. That's what's produced from 0 Datadog over $4.5 billion. That's produced Datadog from not being in the industry to being the largest player. So the weight of this has been that way. That doesn't mean every client is going to do it. But with very traditional enterprises, we have a very long set of data on the net retention, i.e., the expansion of that. That's a very, very powerful motion. So anything from car companies to banks, insurance companies metal vendors, video, all of the media -- all those companies are essentially weighted average expanding with Datadog if they're using Datadog, they may do some things themselves, but they're putting more and more of their workloads into the cloud or more of that is being observed by Datadog.

So as you point out, 1 of the strongest elements of Datadog is that consumption can increase that quickly and customers often consume ahead of Commit. And so how do you manage those customer conversations in that -- and then how do you guide Just manage the Street in other way. .

No question. So we have take-or-pay contracts. And they tend to be a year or more. They tend to be out to 3 years. And then we have a base of commitment, so they can't spend less than that. And then for the most part, this is really about -- the same thing with AWS and the hyperscalers. There's essentially capacity planning we do together. And then for the most part, you can tell with the net retention that they're used more than they've committed to. And so the motion is we can work with them to figure out what their next consumption is going to be. And they have an incentive to do it because we're volume-based pricing and term pricing. So most of the time, we have within the contract -- they have a set amount, but it's in their advantage to expand that. And so what we do is we have long histories by customer of what happens. We know because every day we see the users. I can see the usage when I wake up in the morning, I can slice and dice it by customers. So it's almost perfect information. And then we work with that. We've gotten pretty good at helping clients use it -- there was, I would say, in the bubble after COVID, there was -- in the ERP eye, there was probably some usage that was over usage -- but we step in and we help clients then use it effectively. We help them figure out what logs to put in and what logs not. And then in terms of guidance, what we're able to do and that's what -- how we can beat and raise is we take that growth rate that we see over a long period of time, and we discount it. So that we have that cushion. And then what's been proven out in the eat and raise is that the clients spend more than the amount we're discounting. So let's say net retention is blank. We discount net retention. And then we have a long time series. I can't be perfect, but and what net retention is broken down by clients and sector, et cetera. And so that's how we work with the consumption model in making sure that we meet the obligations to the investors.

So there's 2 main kind of ways we're looking at AI for Datadog you described as AI and AI 4 Datadog -- and on the Datadog for AI front thinking about how a customer would move from monitoring microservices into GPUs or training models or agents. Is that -- I mean, maybe on the agent side, that's very pervasive. Is that something that every customer could do? And then like what kind of consumption changes do you see? How does it make them stickier.

Yes, we're basically setting it up. So whatever they're doing, if it's a human, it's an agent and it's a coding agent. If it's a large language model, we're set up to monitor it. And we generally, for the most part, we monitor production environments where starting to do more in training. So we basically set that up, and we've been seeing good -- very good growth in that area. So -- we have to do the investment to set it up and then as clients introduce them to production environments, there are a lot of metrics we've been giving out on the growth of agent monitoring, the growth of MCP calls, lots of metrics, if you read the scripts, et cetera, you'll see these things are growing at a very high rate. It's still early on. So if they're training models and they're not putting in production, and it's in-house or they're using it for their marketing collateral, internal. It doesn't tend to be our market, but more and more of it is being our market. So we're monetizing it through pricing we publish as we put into GA and it generally is on like everything else, it generally is on the amount of data consumed or the amount of investigations and things like that. Still early days, but really good growth signs. That's Datadog for -- then there's AI for Datadog, which means when you're using the platform, are you able to automate more quickly, investigate, use models, figure out what's going on, route cases and things -- and that's what we're putting in the model itself. That's a lot of what the bits product is. And again, it's early on, but we're seeing traction in that, which we're optimistic. Those of you that know us for a long time know that we don't call it. We don't go like -- we're going to have $1 billion of this. What we do is we say that we're getting traction. And then when we get to certain amounts, we tell everybody we've done it. And so we're seeing that high growth.

Within that -- or data for AI I'm thinking back to like 2024 before a lot of this infrastructure spend took off. We've had LLM monitoring we have GPU monitoring -- what -- so you've had some recent wins there. Like what do you think is driving maybe more interest or inflection there? Is it just maturity of the ecosystem? Is it maybe the rise of open weight models or like what's...