Hi, everyone. Thanks for joining 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, a technology analyst here at Canaccord Genuity. Really excited to have Datadog with us here today. David Obstler is CFO. David, thanks so much for joining. Thanks for having us. I hope it's a meaty discussion before Smith & Wollensky. We'll see. Indeed. Let's kick it off for those of you that aren't familiar. Let's start with the quarter. 36% growth, $1.1 billion of scale accelerating from 32%. You've accelerated the past five 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? 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 SMB to enterprise and globally. In order to distribute that, we've also successfully expanded our go-to-market. 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. Anytime we have a re-platforming and a modernization of tech stack, that's comple mented Datadog in their growth. So all of that came together, and it's been compounding over four or five quarters, which produced the results in the quarter. There's this idea that because you've been able to mo netize AI natives so well, and that's worked well for you, that growth is top-heavy or concentrated. But in reality, that is not all that true. You have 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? Is that driven by AI adoption, or? Yeah. This has been four or five quarters, as you mentioned, of acceleration up into the upper 20s, and that is enterprises and stuff. That is the adoption of the Datadog platform. That is 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 will see that the market share gains are very substantial, and it is 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, there is a real premium on having it all knitted together. That has really resulted in pretty strong adoption in a number of different areas. Any time technology has changed, et cetera, we have also had all the modern workloads tend to go to Datadog for observation. So there is probably also going on more of a weight towards modern workloads, some of which are AI enabled. It has been very broad based and, as you said, very strong. The AI natives themselves, where we have also won a lot of market share, have complemented that. In that, I think we said it is 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 stat for a number of years, how to judge breadth and 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's buying 10 products, what does that look like as a percent of their IT spend? Yeah. Well, in terms of the land and expand, all our clients, when they land with us, they have other vendors that have been there for a while. We do not tend to have everything switch all at once. What has been happening, this has been going on for five years, the wait has been to when those other contracts come up for renewal to use more Datadog products. We sell on a credit basis. We sell $2 million of capacity, and they can use the platform. What we find when we get that is we find that adoption. Our cohorts are 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 have expanded the product line so much. That is kind of what happens on an ongoing basis. That produces the net retention that we talked about in the 120s, low 120s, and that has been pretty persistent over a long period of time. That is 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. You were early in winning AI natives versus many of your peers, and that has grown wonderfully for you. Some investors were not really sure what to do with that category at first. They should underwrite it to zero. We've been particularly excited about the category. 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 in terms of the product? Yeah, 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. 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 a question that's come up, is there going to be volatility there? Yeah, there might be. There might be. 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. I want to build on that. Then there's been, before AI, the largest technology organization would always buy, procure, and build technology very, very differently. 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 maybe usage trends among largest customers. Did you get to that scale? Then just even how that conversation goes when you get to eight figures or nine figures of spend. Yeah. 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, 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's 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. 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. As you point out, one of the strongest elements of Datadog is that consumption can increase that quickly, and the customers often consume ahead of commit. How do you manage those customer conversations in that regard? Then also, how do you guide business and manage the street in that way? No question. 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 cannot spend less than that. For the most part, this is really about, it is the same thing with AWS and the hyperscalers. There is essentially capacity planning we do together, and then, for the most part, you can tell with the net retention that they are used more than they have committed to. 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 are volume-based pricing and term pricing. Most of the time, we have within the contract, they have a set amount, but it is in their advantage to expand that. 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 customer, so it is almost perfect information. Then we work with that. We have gotten pretty good at helping clients use it. There was, I would say, in the bubble after COVID, there was, in the ZIRP 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 compete 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. We can't be perfect, but in what net retention is broken down by clients and sector, et cetera. So that's how we work with the consumption model in making sure that we meet the obligations to the investors. Yeah. So there's two main kind of ways we're looking at an 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. Maybe on the agent side, that's very pervasive. Is that something that every customer could do? What kind of consumption changes do you see? Does it make them stickier? Yeah. We 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. We generally, for the most part, we monitor production environments. We're starting to do more in training. 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, 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. 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 AI. 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. 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 Datadog for AI, I'm thinking back to 2024 before a lot of this infrastructure spin took off. We've had LLM monitoring. We have GPU monitoring. So you've had some recent wins there. What do you think is driving maybe more interest or inflection there? Is it just maturity of the AI ecosystem? Is it maybe the rise of open-weight models? Or what's been well there? No, it's that our clients are putting LLM-enabled applications in production. We are following that. Essentially, as most of you know from following it, the early part of it was very training and research. Most of the early applications, this is what our consumer or training, et cetera, and now we are starting to get to the next stage. That is why you are seeing all this information about enterprises. I will use Datadog as an example, developing their models, not just doing API calls out to the large foundational models, but also using open weights in their own data. For instance, Datadog itself is in the evolution of its own models. We have a research lab. We just made an acquisition, and all of that is very typical of what is happening across enterprises in getting to the next stages of moving beyond API calls to create their own models, et cetera. Then tune inference on their models. That is what is happening at Datadog, and that is most of what we are doing, and that is starting to accelerate in terms of our clients as well. You talked about the research lab, I think Adaptive ML. You have a world-class R&D team, I would describe them with primarily organic and an ability to integrate some organic R&D. Datadog was actually one of the first companies that we ever launched on, and it became apparent. It was a world-class team, but it became apparent over time how that could compound. 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, it all goes back to the platform has tremendous weight. 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 datasets 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. Our view is that that's going to deepen the moat, 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. We've been really excited by the Bits AI product. 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. Now it's broadened. It's on a token basis. Right. Exactly. Maybe talk about if you've heard anything from customers or trends there and just how you think about pricing and b alancing gross margins with the tokens. Yeah. 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 early signs are a lot of good reception and use. We also are expanding what we're doing with Bits. 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 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 Dat adog. I brought this up in another fireside today, but 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. Bits right now is still a nascent portion of the business. How big do you think that could become? Do you think that in some ways, not your user, but your customer could be changing? Yeah. I'm not going to say humans are going to become a. 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. 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. 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. It's all probably going to be our friend if history repeats itself. If it's becoming easier and cheaper to build any kind of software, and in theory, you could build some kind of internal observability tooling. Not that it would necessarily work well. We've seen that impact SaaS. Maybe just like a simple reason why that could strengthen the role observability rather than displace it. Yeah, our platform is the integration of data. Is the data public 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 in a public way? No. What about security of the whole thing? No. Essentially, I think the world is seeing that things that are basic to the infrastructure, the closer you are to the infrastructure layer, the more it's not like you go into Claude or ChatGPT, and you create Datadog. I think a lot of it has to do with Datadog. 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. We feel that what w ill happen here is software will get developed more quickly. You're still going to have to 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 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. That's what's been happening. 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, 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. 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. I think for right now 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 can see our margins haven't changed, right? But we're not token maxing. We're basically using tokens in order to create good soft ware and products to release to 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 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. Okay. Final quick question. Somebody's out there. You have everyone in this space trying to expand their platforms. The big security companies, are trying to do more under their platform. The Snowflakes of the world trying to do more on their platform. When you see that dynamic happening in Datadog, how do you expect that all to evolve or where does Datadog fit in that environment? Yeah, Datadog is knitting together all of that to observe software in production. I am now eight years at Datadog, it has been public for over 6. There has been the same thing. Is Snowflake in your market? Is Palo Alto Networks in your market? Is Splunk in your market? Is blah, blah. Easier said than done, and it has not changed the competitive dynamic because when you are spending that amount of R&D and you are focusing on a problem, and you are good at it, and you are relentlessly investing, the market is not there. The same could be said for us. What about us? We are trying to pick the areas like in security or in service management that have a lot of synergies to what we are doing with our observability platform. We are not saying we are going to secure desktops. We do not use it. We are not saying that we are going to be a coding repository. We are not saying that we are doing email security. We are basically saying that we are working on the security of modern cloud workloads, where it is using a lot of the same raw materials, in the case of Cloud SIEM logs, and we have a right to win. I think that is not saying, "I am going to take over the whole security market." It is saying, "I am going to expand into the parts that are around the management of cloud workloads." That is how we are approaching it, and I think you see when you look at everyone else who is trying, that it has not affected the market, broadly speaking, in this period of time. Who knows 20 years from now? But it is pretty much resulted in a strengthening of the competitive position of Datadog, not the weakening. David, I wish we had more time. Yeah. Thank you. Thank you so much for joining us. Thanks a lot. Thank you. Thanks, everybody.