Datadog, Inc. (DDOG) August 12, 2026 Conference Transcript

Hi, everyone. Thanks for joining this, one of the last sessions at this conference. Probably the last thing between you and Smith and Walensky, so we'll try to make it interesting. I'm Kingsley Crane, a technology analyst here at Canaccord Genuity. We're really excited to have Datadog with us here today. David Opsler is CFO. David, thanks so much for joining us. Thanks for having us. I hope it's a meaty discussion before Smith and Walensky will see. So let's kick it off for those who that aren't familiar. Let's start with the quarter, 36% growth, $1.1 billion in scale, accelerating from 32%. You've accelerated the past five quarters. 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?

Yeah, 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 S&P to enterprise and globally. And in order to distribute that, we've also successfully expanded our go-to-market. And then the environment's pretty good where we have the AI natives, 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 anytime we have a replatforming and a modernization of tech stack that's complemented Datadog and their growth. So all of that came together and it's been compounding over four or five quarters which produce the results in the quarter.

So there's this idea that because you've been able to monetize AI natives so well and that's worked well for you that growth is top heavy or concentrated but in reality that's not all that true You've seen non-AI natives 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?

Yeah, so this has been four or five quarters, as you mentioned, of acceleration up into the upper 20s. And, you know, that's enterprises and stuff. That's the adoption of the Datadog platform. That's winning market share. 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 is a real premium on having it all knitted together, and that's really resulted in pretty strong adoption in a number of different areas. Anytime technology has changed, et cetera, we've also had all the modern workloads tend to go to Datadog for observation, 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 a 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 stat for a number of years, how to judge breadth of product adoption, and we've moved from 4 to 6, and now one of the premier stats is 10, and that's doubled in the past year. What happens when a customer moves from 4 to 10? Kind of give us a sense of that adoption timeline, and then when a customer is buying 10 products, what does that look like as a percent of their IT spend?

Yeah, well, in terms of the land and 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 at once, but what's been happening, this has been going on for five 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 what we find when we get that is we find that adoption. Our cohorts are very, very long, meaning our cohorts signed five years ago are 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 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 we 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 it to zero. We've been particularly excited about the category, and it's grown well. But how about for you? What gets you excited about AI natives, and do you think that that kind of helps prove that you're skating to where the puck is going?

Yeah, I mean, it definitely does. It's AI in, not AI natives, and we can talk about that momentum in AI natives. 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 puck's going, as you said, because 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 companies for the overall digital economy, and they're choosing Datadog for monitoring. So it's a really good forward-looking sign. In terms of the question that's come up, is there going to be volatility there? Yeah, 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 differently. And so the fact that you've been able to win at hyperscaler labs or some large AI natives 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 largest customers, like, did you get to that scale? And then just even how that conversation goes when you get to eight figures or nine figures of spend.

Yeah, I mean, it's 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 being observed by Datadog. 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 zero Datadog, you know, over four and a half billion dollars. That's produced Datadog from not being in the industry to being the largest player. So the weight of this has been that weight. That doesn't mean every client's going to do it. But with very traditional enterprises, we have a very long set of data on, you know, 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 benders, 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, and more and more of that is being observed by Datadog.

So as you point out, one 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 regard? And then how do you guide your business and manage the street in that way?

So we have take-or-pay contracts, and they tend to be a year or more. They tend to be out to three 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 then 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 usage. 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, you know, in the Zerp side, 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 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 beat 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, can't be perfect, but in 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 two main kind of ways we're looking at AI for Datadog that we've described as Datadog for AI and AI for 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. Agents, exactly. 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? Does it make them stickier?

Yeah, we're basically setting it up so whatever they're doing, if it's a human, it's an agent, 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. We're starting to do more in training, so we basically set that up, and we've been seeing 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 etc you'll see these things are growing at a very high rate it's still early on so if if they're training models and they're not putting in production and it's in-house or they're using it you know for their you know 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 data dog for AI. Then there's AI for data dog, which means when you're using the platform, are you able to you know automate more quickly investigate use models figure out what's going on root 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 you know 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 a billion dollars or 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 a data dog for ai yeah i'm thinking back to like 2024 before a lot of this infrastructure spin took off we've had llm monitoring um 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 the ecosystem is it maybe the rise of open wave models or like what's no it's it's it's that our clients are putting um llm enabled applications in production so we're following that so essentially as most of you know from following it the early part of it was very training and research and most of the early applications this is what we're our consumer or or training etc and now we're starting to get to the next stage. That's why you're seeing all this information about enterprises. I'll use Datadog as an example, developing their models, not just doing API calls out to the large foundational models, but also using open weights and their own data. So for instance, Datadog itself is in the evolution of its own models. We have a research lab. We just made an acquisition. And so all of that is very, very typical of what's happening across enterprises in getting to the next stage of moving beyond API calls to create their own models etc then tune and you know inference on their models that's what's happening at Datadog and that's what where we are that's most of what we're doing and that's starting to accelerate in terms of our clients as well you talked about this the research Lab, I think AdaptiveML, you have a world-class R&D team, I would describe them, with primarily organic and then ability to integrate some organic R&D.

Datadog was actually one of the first companies that we ever launched on, and we always knew it was a world-class team, but it became apparent over time how that could compound. But when you look at what you're doing with Bits.ai, is there an additional advantage that you gain by being able to post-train using the data asset that you have today? Yeah, I mean, it all goes back.

So the platform has tremendous weight. Like, if you have a model and it's not part of the overall operation of the platform, it has much less value. So we have, one, the data sets on observability, right? We have a large customer base. We have a platform that is already being, we used to call it ML, is already being used with analytics. So there's this tremendous competitive advantage, not in training models for legal processing of contract, but in observability. And that's the competitive advantage where you're going to have specialized intelligence that we're investing in. And our view is that that's going to deepen the mode and it's going to be something that's going to move towards self-remediation, meaning some cases you'll have enough intelligence that you know what's going wrong and the client will push, yes, self-remediate and without humans or less humans, you're going to have it. We're on the journey there. We're not quite there yet, but that's what the vision is and it's happening.

Yeah, we've been really excited by the Bits AI product and it's continued to broaden. I think when it first came out, you were pricing on a per-investigation basis. That's evolved a little bit. but now it's broad and it's on a token basis. What have you, maybe talk about if you've heard anything from customers or trends there and just how you think about pricing and balancing gross margins with the tokens.

There's two things going on. One is you're right. We have sort of, and this is very typical of Datadog, we play around with pricing, see where we think we can add value, and so that is being launched, and, you know, early signs are a lot of good reception and use. We also are expanding what we're doing with BIT. So we started out with these investigations, right? And now we're also doing it on to the left with development and security. So the first was, you know, was basically reliability engineers handling cases. And now we're also investing in security and software creation. So there's a number of vectors here with Bits, which is why you're probably getting signals of excitement, because it's broadening out the end market. It's broadening out the workloads that are being pushed into Datadog.

So I brought this up in another fireside today. So one of the quotes from Matthew Prince recently was that humans are going to be a rounding error for traffic on the internet over the next decade. And so Bits right now is still a nascent portion of the business. How big do you think that could become? And then do you think that in some ways, not your user, but your customer could be changing?

Yeah, we think all we can say, I think that I'm not going to say humans are going to become What we are seeing is that the weight between models and human and compensation is shifting so that you're, this is happening in coding agents, this will happen in observability, so that the customer will get more intelligence, more automation. There should be a lot of cases that should be able to be largely automated. There probably are going to be, at least in the next bit of time, humans that are going to have to take that information and make decisions because some of this is about the delivery of their product. So we're still in evolution. We're early on. Definitely the weight's going to change. Where it's going to wind up, I don't know. But the good news is it probably doesn't matter, meaning we're not a seat model. we are basically monetizing based on the workloads that go through and it really doesn't matter if those workloads are created or looked at by agents versus not you're going to have to we believe you're going to have to observe and secure this and probably all this is going to accelerate the pace of change which means you're going to have to move from legacy legacy still is the biggest part of the market because it was there for 50 years. So it's all, you know, it's all probably going to be our friend if history repeats itself.

Yeah. So if it's becoming easier and cheaper to build any kind of software and, you know, in theory you could build some kind of internal observability tooling, not that it would necessarily work well, but we've seen that impact SaaS. I mean, why, maybe just like a simple reason why that could strengthen the role of observability rather than displace it.

Yeah, I mean, it's basically our platform is the integration of data. So is the data publicly available or not? No. Is it data that is consumer, not mission critical? No. How many integrations and curation of the data integrations do you have to do? And can that be done like in a public way no what about security of the whole thing no so essentially there you know i think the world is is is seeing that things that are basic to the infrastructure closer you are the infrastructure layer the more it's it's not like you go into clutter chat gbt and you create data dog and so i think a lot of it has to do with data dog a lot of it has to do with how it's all integrated a lot of it has to do with mission critical and all security and so um you know we feel that that what will happen here is software get developed more quickly you're still going to have to um quality control it curate it put it into production somehow you're going to have to observe it you're going to have to still manage your loads of you know provision of gpus or CPUs, et cetera, all of that still has to be done. And anything that can be done to have more of that flow into modern platforms or modern infrastructure is going to create more workloads for the modern companies like Datadog to observe. And that's what's been happening. I mean, you can see it in the numbers. And we're not predictors of the future, but it looks like that's happening again. Right, yeah. I think the AI native success, too, helps.

It gives conviction there. As we near time, I just want to make sure that the audience has a chance to ask a question they'd like. All right. So, I mean, you're accelerating revenue. You're holding margins roughly flat, free cash flow in the high 20s. Can you just give us a sense of how you plan on investing in this, again, the world-class R&D organization and how you also manage the pace of hiring sales and what's the kind of dynamic consumption environment?

Very good question. So I think for right now, people, there needs to be salespeople to sell, meaning we haven't gotten agents to be able to sell. So we look at that as what's the TAM, what are we covering, what do we need to continue to be able to sell our software. And right now, we've done a good job. We have successfully ramped quota capacity on a global basis. and it's been roughly in line with revenues. And we're not at the point where we saturated the market. We still have many areas. I can spend a lot of time on that. So that we're going to continue to invest in. When it comes to R&D, I think we're going to continue to invest at a high rate, but it's very likely, and it is happening, that the percentage of that, the allocation of that, is going to move more towards tokens than it is to humans. We're playing around with it, but you asked about margins. You know, you can see our margins haven't changed, right? But we are also, we're not token maxing. We're basically using tokens in order to create good software and products to release the clients. But you can see it's more of a distribution than a margin erosion. And that's what we see right now. So I think that's what's going to happen. In many ways, it's a little easier because, you know, to have to basically, to all depend on humans and recruitment of humans takes more time than if you could find a way to use coding agents and everything to speed things up. Final quick question. Somebody's out there, yeah. Yeah, yeah. Yeah, Datadog is observing, is basically knitting together all of that to observe software in production. And there have been, I'm now eight years at Datadog, it's been public for over six. There has been the same thing. is Snowflake in your market, is Palo Alto in your market, is Splunk in your market, is blah, blah, blah. Okay, so easier said than done. And it hasn't changed the competitive dynamic because when you're spending that amount of R&D and you're focusing on a problem and you're good at it and you're relentlessly investing, you know, the market's not there. Now, the same could be said for us. What about us? So we're trying to pick the areas like in security or in service management that have a lot of synergies to what we're doing with our observability platform. So we're not saying we're going to secure desktops. We don't use it. We're not saying that we're going to be a coding repository. We're not saying that we're doing email security. We're basically saying that we are working on the security of modern cloud workloads where it's using a lot of the same raw materials in the case of cloud sim as logs and we have a right to win. So I think that is not saying I'm going to take over the whole security market. It's saying I am going to expand into the parts that are around the management of cloud workloads. That's how we're approaching it. And I think you see when you look at everyone else who is trying, that it hasn't affected the market, broadly speaking, in this period of time. Who knows 20 years from now, but it's pretty much resulted in a strengthening of the competitive position of Datadog, not the weakening.

David, I wish we had more time.

Thank you so much for joining us. Thanks, everybody.