Datadog, Inc. (DDOG) September 8, 2026 Conference Transcript

Good afternoon. Good evening to those of us joining on the webcast. I'll give you all a couple of minutes to settle in here. But without further ado, just a few introductions. My name is Fatima Bulani. I jointly head up our enterprise software research coverage here at Citi, and I have the distinct pleasure of hosting our keynote session this afternoon with the CEO and founder of Datadog, Olivier Pommel. Olivier, thank you so much for taking some time to sit down with me this afternoon.

Thank you for having me. We're actually a 15-minute walk away from here, so I can get right back to work afterwards.

You got your steps in. And we want to give you a balanced schedule today. I wanted to set the stage with you with the word sweet 16. Datadog is 16 years old as of June. And you're approaching right around half that time as a public company. You went public in 2019. So I wanted to ask you, you know, what is the latest and greatest on where Datadog is today and how the company has evolved, you know, not only in its tenure as a public company over the last 16 years, but most notably in the last 12 to 18 months?

So, well, first of all, it's been a lot of growth. You know, so when we took the company public, we were right in the middle of, I would say, in the early innings of the cloud migration. And that's what we were known for. You know, that's what made the company. Now, today, we're in the middle of the AI new transition, which I think is a lot of the same, quite a little bit bigger. We've come a long way since we've took the company public. I think we are in order of magnitude larger in terms of revenue. We're in order of magnitude. I think we also increased the share price in order of magnitude since we went public. So all that's great. And I think that's also, I mean, people ask me sometimes, you know, so why are we still here? And I'm here for the next 10 years. I'm here for this other order of magnitude we think is in front of us that we can get. So when you look at the last quarter, we've seen an acceleration of growth. I think earnings are, we had earnings a month ago. So I might get some of the numbers slightly wrong. But we accelerated to the, I think, 36% year over year growth after a number of quarters of sequential acceleration. What's especially exciting to us is that this acceleration happens throughout the customer base. So it's not just a matter of AI customers growing very fast, I'm not sure as they are, but the rest of the business, the non-AI customers, all the companies that existed beforehand and that are not primarily in the business of AI have been accelerating over the past year or two. I think we said on a call, we accelerated from one year ago from, I think, 18% year-over-year growth for that part of the business to the high 20s. So massive acceleration there. And what this really shows is that the AI transformation is happening. It's touching the whole customer base for us. And just as, you know, in the context of cloud migration, observability was a key part of the transformation, we also see that observability is a key part of the AI transformation. We see it in the way this happens with the customers, but we also see it when we think of the end state, you know, so where is everything going? Where do we end up, you know, five or ten years from now? Observability is probably the only category that remains. But if AI does a lot more of the work, whatever the work is, keeping tabs on the AI, understanding what it's doing, why, whether it's aligned, whether we're getting the right outcomes for it, whether we're doing it at the right cost, I think is probably the last software category that remains in the end. So I feel pretty excited about it and very busy building it up.

Ali, you touched a lot of hot zones that I want to take a lot of time unpacking. But before I do that, you've had the benefit and even the privilege of watching and experiencing not one, but now on the cusp of two massive technological and computing paradigm shifts. And I know myself, certainly, and a lot of investors in this room tend to look at historical precedents and analogs to form mental models about what could be the state of technology, the state of the enterprise software or infrastructure software stack three years from now, five years from now. And so with the benefit of hindsight having gone through the cloud computing transformation, I'm wondering if you can opine on, as we are on this cusp of a massive generational opportunity and shift with AI, what are some of the parallels that you can draw between this cycle of innovation and paradigm shift versus the cloud computing cycle?

So it's actually a lot of the same things. So when you think of what made us successful in the cloud edge, part of it was that the roles were changing. So you needed to ship faster, you needed to iterate faster. As a result, roles that used to be completely separate between development and operations became smushed together. And I think in the edge of AI, we'll see even more of that. I think we can talk a little bit more about it later, But, you know, we will see quite a bit of that between not just development operations, but also security, maybe some of the business functions. I mean, you see the way people can build software today, like non-technical product managers can build software. So all of those roles are being pushed together, you know, which benefits us as a business because we're in the business of bringing those people into one platform and having that one representation of the world that cuts across all of those different concerns. We, you know, in terms of what we can observe, you know, we do see a lot of the same underpinnings. Like we see companies need to be more digital. They need to interact with their customers digitally. They need to build up their infrastructure. They need to have applications that perform well. So I would say 80% or 90% of the AI build-out actually looks like the cloud build-out. And then there's a few new layers that are appearing. You know, there's a very deep stack we need to be able to observe and manage, you know, for the AI transition, but we'll see all that. And just to backtrack for a minute, like, you know, we talk a lot about AI and all of the net new and all of the new areas we can get into. But even if you just restrict yourself to observability and the core of what we do, It's a market where we're the leader today. We have about 13, 14% market share. That market is going, you know, 15, 20, 25, 30%, depending on who you ask year over year. And so you do not have to have a lot of imagination to understand how we can grow 5 or 10x from that. You know, so just the core of what we do, just extending to a stack that is going to grow even faster now, I think makes for a great business story.

It's never been a better time to be an observability company. And if I were to bifurcate your opportunity in maybe a simplified or simplistic form of there's observability for AI, and then there's AI for observability. So let's parse through those distinct opportunities for you and Datadog. And maybe how much of the business today is coming from just monitoring these novel AI systems, agentic AI systems, versus helping customers have better platform efficacy and utility in using Datadog by embedding and productionizing your own AI capabilities? How would you size or bucket those opportunities and their impact on the business today? Yeah.

So, I mean, thank you. This is almost all branding. You know, our branding is we talk about Datadog for AI and AI for Datadog. And that's where we present the various parts of our business. The part that is like right now growing the fastest is Datadog for AI and how we observe basically the whole stack. You know, so a lot of the stack is actually the same as the non-AI, you know, so most of the AI companies are built on infrastructure that, you know, other companies also use. Most of the AI agents are actually spending the majority of their time using tools. The tools that the agents are using are standard applications and those applications also need to be built and monitored and, you know, and so a lot of it is the same. I would say today, like 70%, 80% of the stack is the same. And then there's a number of new layers. At the bottom end of the stack, GPUs are a new type of infrastructure that has been around for a long time, but was never really used at large scale in production environments or in a way that was going to be repeatable across a large number of customers. That's something that is happening now, and that's a part of the product we're covering. And at the higher levels, now you need to monitor not just the applications, but also the agents, the models the agents are using, the outcomes the agents are meeting, the way the agents are interacting with the real world, the security of the agents. So there's this whole new set of problems that need to be handled by observability. So we see all that. Now, today, the part of the business growing the fastest is the part that has to do with the buildup at the infrastructure and the application layer. We also see an explosion of traffic in all of the AI surfaces, like so the agents and the number of traces we get from that and things like that. But I think it's more of a, it's showing us where the world is going rather than already today being the driving part of the business.

Bits.ai has been a fairly momentous set of product introductions. That portfolio of capabilities has expanded very swiftly. I know you've had new product leadership introduced into the organization. I'm hoping you can sort of opine here on the traction that you're seeing with Bits.ai as, you know, in my opinion, the manifestation of, you know, a lot of the AI for observability use cases that you're talking about. And where is that showing up in the geography of your financial results?

So this is now AI for Datadog. So how do we make everything that Datadog does even better through AI automation? And Beats AI is our AI agent. And underneath, it's a collection of different agents that perform various different things, but all branded under Beats AI. And we started, I think we released last year, the first version of it, which was focused on investigations. So Bits.ai would trigger when you get an alert, would investigate it, would tell you what happened, why, and would help you fix it. And we've now extended it to a number of different surfaces. So Bits.ai has a chatbot you can use. Bits.ai can optimize your code. Bits.ai can actually build agents and build applications from scratch inside the data context. It can do a whole bunch of different things that it was not doing before. We have a large number of customers that are using it, starting with the investigations. And it's actually pretty transformational for them. So I was actually reading this morning some notes from two customers in Japan. So I think maybe because they didn't have the weekend off, I got emails from the Japan team. And, you know, so there's actually, at the same time, two different customers, one large retailer, one bank that are using Bits AI. And that like it so much that they went out of their way to email us, say, this is great. This works super well. And in both cases, what happened is that they had some issue that happened in both cases was a recurring issue, something they've had for years. And they could never quite figure it out. They could always just apply Band-Aids and get things back. And that's it. No, it would happen again. And in both cases, they actually were able to figure out those issues thanks to BixAI and fix them in process so that they can't recur anymore. They were so impressed, they emailed us straight on that. So it's pretty transformative for them. In terms of what it is on the results, you won't really see it yet on the results. We're actually in the process right now of repackaging these products. You know, we used to have one SKU we sold for specifically for investigations. And now we're packaging it to a set of AI credits instead that can work across a lot of different surfaces. Because as I said, we expanded a product to cover a lot of different surfaces. And so we probably will comment on that in the future. But for now, the transformation is happening right now in terms of the way we're charging those. And by the way, if you zoom out, the world as a whole doesn't quite know how to charge for agents just yet. At the low level, you have the model companies that are charging by token. And then above that, it's a little bit unclear. As far as we're concerned, you know, we don't really care. We are completely usage-based. We can very easily add queues, change queues, attach it to different things that scale with the activity or the data volumes our customers send us. And so the question for us is more to understand what resonates best with the customers and also what is adopted industry-wide as the right way to charge for point intelligence.

I think the notion of pricing and packaging is an important one. And in a conversation, I think it's very important to have, you know, I don't think there's ever been a time in the technology landscape and from a procurement standpoint where decision makers have been so stretched from a budgetary standpoint, but at the same time have to solve for, again, transformational capabilities that they have to embed in the IT stack. So, you know, with this in mind and the magnitude of innovation that the average large organization now has to productionize by way of AI, how do you expect Datadog strategy to evolve from billing on units like hosts and log volumes? and where are you and where are customers in their conversations with you as it relates to their acceptance of decoupling a lot of the revenue attribution to a unit of data? And I know you've made some forays here, so this is not sort of an uncharted territory for you, but what does that evolution look like when the BITS AI portfolio is eventually potentially not even going to touch any data and we'll have outcome-based resolution. So how does that paradigm shift? And again, the philosophy around buying change and how are you influencing that for buyers?

So, I mean, look, there's many possible solutions on the table there. And I think, again, everybody is trying to figure it out at the same time, including customers in terms of what they prefer, what they like or what they don't like. You know, for the cloud edge, usage pace has been the best way for us to sell and customers to buy. I think the AI edge is going to be some version of that. But you're right. I mean, initially, we priced everything according to the volumes of data we were receiving. Nothing about what comes up, nothing about what happens inside the platform, because the volumes of data were enough to model basically the usage and the value we provide to our customers. In the future, so we're shipping those AI agents. We have some AI agents that even work on data we don't ingest. So we announced the decoupling of our security agent from our Cloud C, for example. So you can actually use our AI smarts on all the data source today. And we also can generate code. We can do things that don't necessarily manipulate a lot of data. So for now, we're packaging that as AI credits that our customers can buy on top of the rest of the data consumption. So far, it seems to be very well accepted. But again, we'll see where this goes. If we need to repackage that in different ways, attach that to different parts, such as the data volumes, the host, or anything else, that's something we can easily do. Now, when you look at the reason customers consolidate on us, one of the reasons is that it's a lot easier for them to have one big pool of commitment and spending with one vendor as opposed to managing 12 different pools with different companies and have to make sure they hit the right number for everything. So when customers contract with us, especially large customers, what they use across different parts of the catalog is fungible. So they can say, you know what, we're going to actually, we change our mind. we don't need as many hosts, but we need more smarts or, you know, we don't need, you know, we're going to use more IPM and less logs because it's more efficient, you know, in these different ways. Customers don't have to care. Like all of that is fungible, fully exchangeable. And that's why they're one of the reasons they consolidated with us.

I wanted to continue on this thread around and maybe zoom out a little bit and talk to you about the addressable market opportunity. You know, earlier in your comments, you mentioned, hey, actually, you know, the shift to AI and agentic AI and AI systems actually has 80 to 90 percent of the same underpinnings that was the impetus for organizations to move to the cloud. And you've had a remarkable ascendance on the back of that secular trend. And so if I were to just distill the addressable market conversation to pricing, which we just talked about, and some of your earlier comments around, hey, there's so much more of a proliferation and diffusion of AI surfaces that now you can touch. So from a P times Q equation standpoint, where do you see the most torque? Because in prior models of monitoring, you're sort of constrained and limited by the rate and level of penetration, let's just say you could have, because at some point an organization is just going to decide that only 80% of my environment is worth instrumenting or only 40% of my environment is worth instrumenting. So how does, you know, that calculus change entirely and what are you doing to effect the most positive change that it's disproportionately beneficial for you?

Yeah. So, I mean, first, I would say we have so much more penetration to be had in the market than we currently have. You know, so I mentioned earlier, you know, even though we're the leader, we have, you know, 13, 14 percent market share. If you look at the largest customers, we are in about half of the Fortune 500. And I think the average contract, the annualized contract is $400,000 or something like that.

It's a little bit higher than that.

So I think we can do so much more. We can grow so much more with all of those customers. And so there's a, again, it doesn't take AI, it doesn't take imagination to see the huge amount of growth in the 5X or even 10X. we can add on top of that. Now, you know, as customers expand and do more with AI, what drives them to bias is that they have an escalation of complexity. They do so many more things. So they're so much more productive, you know, in general. And what I define by productivity is the ratio between the output and the time you spend on it, or the human time you spend on it. that ratio has been exploding in software engineering over the past, you know, 40, 50 years. And it's going to keep exploding even more, you know, now with AI. So with that in mind, we end up with crazy amounts of complexity. Our job is to have customers deal with that complexity. And so that's why they're going to spend at the end of the day, in the cloud, you know, when we're flipping everything with the customer, they spend between, let's call it, around 10%, maybe between 10% and 20% of their cloud bill on observability and security and everything else with us. And I think some version of that will remain true in the AI world, you know, where the spend on us is going to be this percentage of their total bill that they actually justify that pays for itself, basically not only in the savings you make from the usage of the underlying compute or applications you pay for, but also in your ability to deliver the outcomes you want and the trust in the quality of the outcomes you can deliver. So we think that this is going to remain true. And this is why this is such a great opportunity in the long term.

You know, one other observation and theme that you've been very consistent on is, hey, you've shared with the investor community this sort of bifurcation in your revenue between, let's just call them the AI natives, as you do, and the non-AI natives. So classic enterprise, right? But you've been pretty steadfast in your opinion and observations that actually the behavior is the same, the problem and the pain points are the same. But can you help shed light on why there is such a massive distinction between the way the AI natives are operating versus the non-AI natives?

Yeah, and we talked about them separately because we saw different growth profiles. You know, obviously the AI natives, more recent companies, they're all, I mean, you've seen all the, they're mostly private companies. Some of them are not, some of them are, we could classify as AI natives because they're substantially all about AI, even though they're not new companies. But they all are going through massive build-outs of their infrastructure and their applications and massive levels of investment. So we separate them out for that reason. And in the long run, we probably won't separate them anymore just because as the emergence of AI goes further and further into the rear mirror, like it doesn't really make sense. Like now pretty much every new company is an AI native and everybody else is not, you know, so that and the AI is spreading to the rest of the world as well. So the distinction doesn't make any sense anymore. So in terms of the usage dynamics, what's striking is how similar they are, as you said. Everybody starts with infrastructure and applications and logs. Everybody has to get things working into production. Everybody has to make sure the humans actually understand what's happening at the end of the day. Some of the day-to-day usage patterns are different. And, you know, so especially when you go into the frontier labs, for example, like you see customers that are or users that are manipulating, you know, thousands or tens of thousands of agents, you know, individually. Some of that is transferable to the rest of the market and we expect to see it happen to other customers. Some of that is not. Obviously, when you have free inference and all the incentives in the world to push your models to the max, you're not in the same world as the enterprises that get the bill at the end of the month or every single token of inference they use. But we benefit a lot as a company from serving a very large part of the new wave of companies, You know, whether that's the Frontier Labs on one end, but also the slightly smaller AI natives that are still scaling very, very fast because they give us a pretty good idea of where the world is going and what we need to build for the rest of the customer base in the end. But in terms of product footprint, a lot of it's the same.

You brought up the growth in the AI native cohort. I believe you were somewhere in less than 100 zip code in terms of size of AI natives. That size of installed base was less than 100 about two years ago. It's almost 8xed. So that's 750 customers in the AI native cohort. I know we spend a lot of time and energy talking about some of your very, very large customers. But we'll put a pin in that and come back to that. But this is a 750 large cohort of customers. what are you seeing in terms of their behavior, posture, disposition in how they're building their stocks? And the flip side of that coin is there's a lot of concentration of capital in those companies, right? So the movie that all of us watched during the pandemic era was you had the consumer internet discretionary companies really skyrocket in their end user activity. And there ended up being a little bit of a feast famine type cycle where there was an optimization cycle down the pipe, right? And so when you think about this AI native cohort of customers who are the most innovative companies in the world, and they have you architected into their day zero stack. I mean, what are some of the challenges and considerations on the other side of, hey, these businesses are basically three years old, and there's a lot of concentration here?

Yeah. So that's a great question. And obviously, that's something that we're very careful about, because we've been through the highs of COVID. We've been through the lows of COVID after that. We've seen a lot of those cloud native companies at the same time, digital native companies expand like crazy and then contract quite a bit. We were very, very exposed to it at the time. I think it was about 40% of our business at peak. And then we've suffered from the compression there. So we are very, very careful about how we assess these accounts, who we handle, who we watch for unhealthy behavior, for example, from customers and things like that. And we're very, very good now at getting in front of all of that in a way that we've learned over the past couple of years. The thing I would say, though, is that the exposure we have to these AI natives is much smaller. So our business has grown quite a bit. It's very diversified. And we're very, very far from the exposure we had to the cloud natives at the time in terms of the exposure to the AI natives today. So we think in the equation, we see a lot more upside there than potential downside in the future. And again, we're very, very disciplined as a business in terms of how we deal with customers, customer expansion, what's healthy growth, what's unhealthy growth, what's healthy usage, and how we think this will play out in the end. The other thing I will say is that when you see all of those build-outs from the AI natives, all of that is serving the rest of the customer base. I said earlier, I started the keynote with the fact that we've seen massive acceleration of the non-AI customers. And all of that is happening on the back of those infrastructure build-outs and those capabilities that are being shipped by the AI natives. And so we see it is not an isolated benefit to one part of the, an isolated runaway investment from one part of the ecosystem. We see it throughout the ecosystem with companies in the other 85% of our business that are very, very careful about the cost equation and making sure that they build sustainable businesses.

Along those lines, this notion and idea of price deflation, I think you've been generally very candid about the fact that you're going to get more volumes from your customers in providing the single source of truth and eyes on glass on the health of their environment. Well, the more volumes you're going to see, you know, there's going to be more price deflationary impacts. That's a feature. It's not a bug. And so thinking about those dynamics with the role of the rising role of open source with open telemetry, you know, potentially, you know, democratizing, even commoditizing your ability to price on data. How do you think about that influencing your market strategy and also relatedly how you think about embedding open source into your own processes from an R&D standpoint?

Yeah, I think we've like the picture hasn't changed that much, you know, in the 16 years of the company, you know, so there's always like a large number of companies that are doing observability, you know. And there's also the main thing people are using or start using from day one is open source. And there's a rotating cast of companies there and technologies and everything else. Some of them were very popular 16 years ago, don't exist anymore or not so popular anymore. Some of them were very popular seven, eight years ago, are past their peak today. And then there's a few more that are popular today. So that's a constant. And for us, that's a reality that we've always been dealing with and integrating with. So when our customers come to us, they use a bunch of open source. We need to make sure we connect to it very nicely. And we also need to make sure that if they want to consolidate out of it for some part of their system, we can do that for them. And we do that routinely. If you look back or if you go and look at the transcript of our latest earnings score, I think we have, we call it like five or six customer consolidations. And all of those typically involve, you know, a number of open source products that consolidate on us. In terms of, you know, open telemetry, like, it's amazing. Like, it reduces the friction to instrument. It gets us more workloads faster. It makes customers more confident in expanding and getting, you know, both fit into our platform. And so that's great. You know, so we support that. We invest in it. And we're glad it's there. And it's actually a tailwind for us. So we feel great about that. If you look at our history, we've been pretty good at maintaining margins. We've been pretty good at maintaining the amount of value we can deliver to customers. Because again, when you sell, there's only two reasons customers buy your product. They save money or they make money. And we're very good at making that case and demonstrating it. And having customers see it with the first few products they add up from us, which drives them to consolidate and send us even more in the future. So we've been very good at that. As a business in general, I think our business model and our metrics are a differentiator. We have a very, very efficient go-to-market. We have a product that has a fairly high gross margins. And as a result, we are in a position where we can reinvest about 30% of our top line into R&D. And that's what allows us to remain relevant and build a future for our customers, but also to broaden the platform and be more of a consolidator of all of our customers' needs. If you look at what makes us the best company five, ten years from now and what got us where we are today from when we took the company public six or seven years ago, it's that, the fact that we can keep investing and we can be the best in the long term. And our customers recognize that. That's why they partner with us because they understand that we'll be there for the long run. and they won't just get a low price for something today, but they'll have to switch that in two years because the vendor will not be the best vendor at the time.

Just on this notion of competitive differentiation, because there's a lot of facets here that I think we can tie together that would underpin your competitive differentiation. But what I specifically want to ask you, Ali, is where do you believe Datadog's competitive differentiation has actually widened the most in the last year? So, you know, if we were to stack rank between product depth, your AI capabilities, your distribution and go to market, I mean, how would those filter into your ability to actually continue to widen?

Yeah, yeah. So there's two things I would give you. So the first one is the one I just mentioned, which is the structure of the business. And by that, I mean very lean, very efficient go to market, which allows us to deliver both investment and profitability, which I think is very unique. You know, some companies don't have to do that. If you're a private company, you don't have to do that. You can get away without doing it for a while, but in the long run, you have to do it. And that's actually fairly differentiated. The other part of the business structure is that we serve a very wide customer base. We serve everybody from the small startups and individuals all the way up to the largest companies in the world that are paying us tens or even more in the millions of dollars a year. And all of that is fairly unique. Most companies have to choose either one or the other. They can't be both. And that gives us a lot of pretty impressive flywheel in terms of how we can get into new customers, how we can expand, and also how we can get to reading on what's coming up next in the market, thanks to the long list of smaller companies and individuals and startups that use us. So that's the first one. The second one, and I think that's the one that's becoming more and more important, is the fact that we're a SaaS vendor with access to a lot of extremely clean, operationally relevant data that we can use to then train models to automate all of the operations, securing all of the other problems we deal with for our customers. You know, when we started the company, it was always the intent. It was, hey, you know, we're going to be, to build a company is going to be SaaS. So we can do that. When you start building these kind of companies, you get all sorts of offers from customers that say, hey, you know, we're a big bank, we will use you, but we'd like an on-premise instance that we can manage ourselves. We've always said no. And the reason was always no, no, we actually want to have the data, we actually want to be able to iterate over it. And also, by the way, when we're going to build more functionality, we'll be able to do that so much faster if we have direct feedback loops from what we see customers use and not use and what we see work and not work in their environments. So we've done that. Today, I think, is when this promise comes to life because we're at a time where, of course, we see the Frontier Labs build incredible models, but there's different kinds of models we can build. We can build those models ourselves. We can tailor them to our needs. We can build those models based on very specific types of data that are relevant to observability. You know, so think of it not so much in terms of, you know, words, as you see in the LLMs, but more in terms of, you know, traces and metrics and logs and, you know, network topology and all of those things that take a slightly different shape that we can ingest into our systems and that we can turn into predictions. So we've shown some version of that with our open source time series models. So we've released two models of Toto and Toto 2.0, which were released a few months ago, both of which were state-of-the-art at the time of release. And these are fairly small models. These are not gigantic models that consume gigantic server farms. But the quality of those models is completely based on the quality of the data we have. And we also announced a few months ago the acquisition of Adaptive ML, which is a company that was building reinforcement learning to tune and build custom models. And that team had actually joined our research team to accelerate the development of next generation models for us. So you should expect to hear and see more from us in the future on that topic.

This is a good segue into the next question I wanted to ask you. Still sticking to the competitive lens here, but I think what a lot of the investor community and myself included have been positively surprised about, Ali, is this idea of the DIY or the insourcing kind of debate, right? You have, as customers, some of the most innovative companies in the world. They are doing very avant-garde things in changing the world at the jagged edge. And so what's been very interesting to see is that they've knocked on your door to solve some of their most pernicious challenges, right? So can you talk about why, you know, in terms of their decision and decisioning and their reasoning around, hey, we want to align with Datadoc to solve these challenges. where these are actually the most financially and intellectually, most well financially and intellectually resourced companies in the world, right? So, you know, how do you bury that debate around at some point some of these very large companies may in source, they may do it themselves?

Yeah, so I'll say a few things. So one is every single engineer thinks that they could and should be building observability. And I know because I've done it.

Spoken like a true engineer.

Yeah, I mean, that's when I was confronted with that problem, I decided to go build a company around it. I was in the company of Alexei, so I can't blame people for thinking that. And so our business is to make sure that people understand how much more value they're going to get by not having to do that themselves so they can focus on other problems. And it turns out that for most businesses, I mean, pretty much every single business except us, there's some other problem they should solve and observability and automation is in service of that and they should focus their energy on that.

So that's number one.

Number two, it's not economical for almost any company to do so. And we know that because, look, a lot of the people we have at Datadog who run our operations, who run some of our products, actually used to run observability at very large companies internally at some of the top five hyperscalers and didn't know exactly what it costs in these companies to do that at scale. And actually, it turns out it's not any different from what it costs to completely leave it out to us. And we know from all of those people who used to work at those places that we offer a much better value, a much better experience, and in the end, better outcomes that they could get at their previous employers. So that's number two. Number three, like when people reach large scale, there's often folks who want to build it themselves. And in some cases, they will. Like we've had some customers that say, you know, we're going to build it ourselves. And a number of them have turned off and some of them have come back. You know, we've seen that. We call some of those out on our calls. It's a very small fraction of our customer base. If you look at our aggregate numbers, our growth retention is extremely high. You know, it's in the high 90s, including the whole business, you know, from SMB all the way to enterprise, which is incredibly high. There's basically no upside there, like you can't imagine of anything that's substantially better in terms of retention. And what we've seen fairly recently, I think we called that out a couple of earnings calls ago, we've had actually a number of the largest hyperscalers come to us. So these are companies that were completely homegrown, that had built their own systems, that don't run any software from other providers, that came back to us and said, you know what, for our AI buildup, we need your help. And the reason they did that is that they're in a situation where they're all competing extremely hard. And it focuses the mind. They are not under the assumption anymore that, yes, they have enough resources to do everything themselves, and it's okay if they dilute their impact or if they devote some percentage of their smart people and some percentage of their compute on that problem and that problem and that other problem that are not their main problems. Now what we're hearing from them is their teams need to ship as quickly as possible, and they want the best so they can do that, and they don't want to waste their time, you know, whether they're on the user side because they have to use inferior solutions that are built in-house, or on the higher management side, wasting resources to work on problems that are not core.

This brings up an interesting other angle that I wanted to have you opine on, Ali. This idea of dogfooding, pun intended, I myself would say drinking your own champagne, but very consistently, Datadog has kept a high R&D envelope, about 30% of revenue, as far as I can remember, notionally about a billion plus per annum, just going back into the business, pushing the envelope forward on the technology roadmap. Can you talk about your own usage of AI internally, agents, how that's improving your own shipping velocity? And then relatedly, you talked about adaptive ML earlier, and I think you were sort of flirting with the idea of the benefits of having small and contextually rich training models and running inference off of those versus the large language model. So how does all of that factor into your R&D strategy and how you allocate R&D capital from here?

Yeah. So, I mean, look, obviously we use a ton of AI everywhere in the company. In the, I would say in the non-engineering side of things, we're not any different from many of our customers. You know, there's so many things we can automate and we're well on our way to do to doing that so for example you know we we used to scale the number of people we had to react to you know support tickets and things like that along with us as a customer base now that curve has gone the other way and instead of um having all this reactive work now we can allocate people to do proactive work with customers who can build for deployed engineer for deployed engineering to work with customers and all all sorts of things that we didn't have before and all of that all those doors are opened by the AI automation. On the engineering side of things, we're transitioning the whole organization to build with agents and we're obviously dogfooding for that, you know, and so we're finding, again, what most of our customers are finding too, which is that, yes, you can accelerate the development quite a bit, but the hurdles after that remain, okay, now we need to shift that into production, now we need to make sure it actually works, now we need to make sure that actually delivers the right outcomes for our customers. And by the way, on the way to doing that, we're seeing our compute bill or token bill explode and we have no idea, you know, how much of that we need, don't need, et cetera, et cetera. So we're heavily dogfooding all of that. So we're building the right products for our customers on that. And there's a lot of opportunity in each of those areas, whether that's shipping to production, keeping it running on production, measuring the value, you know, with the end users, are we actually shipping the right things, understanding what to work on, or optimizing the costs, the token costs of writing code and automating. In terms of how we allocate the spend, the mental model you should have is that the overall envelope doesn't channel that much. So we still invest 30% in R&D, but the makeup of those 30% might change. And so we're obviously spending more and more on tokens. These engineers are using a lot more agents and doing a lot more there. and we also started spending a lot more on GPUs. I mentioned earlier that we're training models. The first of those models were pretty small, but now we're starting to scale them and you should see more spend there also that's relative to that.

And on the labor side, and this is going to end up becoming a little bit of a philosophical debate, the classically defined role of the software engineer now fusing with the developer, with the site reliability engineer, the cloud infrastructure engineer. So the average engineer persona or the technical persona inside Datadog, are they now just wearing a lot more hats simply because there's more scope for one individual?

And maybe you can put to rest the debate around, hey, does the labor productivity of the average technical person in your organization surmount your need to hire more head so how do you balance that uh equation for yourself yeah so i mean look we we definitely see that people can wear many hats and we also see that you can empower much smaller teams um to uh to build with ai you know so where you needed to build a team of eight with a lot of different roles now maybe you can do with a team of three and so that completely rewrites the uh the the equation in terms of how you know how you structure the teams how you scale them you know all these sort of things. So we definitely see that. I don't think we know exactly what the end state is, because we're still working on a process for that. The models are still evolving. So I think we will take maybe a few years before the dust settles on that. But yes, in terms of the... Sorry, part of your question was...

The human labor component, right? You said the envelope is going to shift, right? Token costs are going up.

On that side, we're definitely still scaling the engineering teams. So when you think of our business, we're limited by two things. We're limited by how much of the right products we have to sell for our customers. And then how wide our distribution is, how much sales capacity are we deploying across the world in front of the right customers. And so we need to scale both. And so right now, if we get more productive, if we produce more, you know, with every dollar invested in R&D, we will just do more R&D and we'll produce more products and we'll go deeper with our products and we'll expand into neighboring categories and we'll help our customers consolidate more. We see no end in terms of the demand on that side for more customers. We're more limited internally by what we can produce.

What are some of these neighboring categories that you have aspirations to have a stronger foothold in?

Well, I mean, look, we've mentioned security before. there's quite a bit we need to do also in terms of going up to the like further into the business side of things you know so we started with the infrastructure monitoring then we went up to application then went up to end user monitoring on top of that and what are they actually doing with it from the end user we're getting into the business value are these users creating value what are they doing are they buying more they're spending are they're staying longer and I think we can get further onto the business side from that we also announced at our conference, a data agent that you can use to query your data and to actually run all of our internal data analytics on that at Datadog now. So I think there's plenty of opportunities for us there too.

On the cybersecurity side, because you put my antennas up on that, just talking about the product portfolio and strategy here, it's about a quarter of the base penetrated with security SKUs. It's a $100 million ARR franchise. So you've made a ton of progress, but I think there's still a lot of opportunity. And I think by your own admission, there is still work to do there, right? So relative to your initial expectations, how is this unfolding? What has gone well? And where do you see opportunities and scope for improvement?

So if I look back at the opportunity, so first of all, we think the opportunity is huge. if you look at the fundamentals if you look at what I mentioned earlier in terms of the roles getting smooshed together like security is a big part of that and so it's extremely clear to us that selling security to security people is not going to be a thing like everybody's going to have to bear the responsibility and that's just the way companies are going to be building products in the future and so the DevSecOps is not going to be an emerging trend anymore but it's going to be the way most companies are running things so that's number one number two is and that one is even newer like we've seen over the past three months that security tooling has been completely upended we've seen that the models are so good and they move so fast that pretty much anything that was built you know more than a few years ago in security is obsolete and the very notion that you're going to have you know 12 15 25 different security products installed that are all contributing to a central repository that then humans review and prioritize that that's just not going to work anymore I mean now you need everything to be fully integrated you need to respond at machine speed you'll have agents constantly probing everything that can be pulled on your end. And so we think it completely flattens the space and negates any advantage there was to be an increment in security. So we think it's a tremendous opportunity and all of that is going to be developing over the next couple of years. So we feel very good about investing and building more in that department.

And how does the playbook from a go-to-market messaging standpoint change when you are going to be interfacing with a completely different new class of competitors who, by the way, are in some cases also meandering into your core territory, right? Some of the largest cybersecurity companies are more assertively talking about observability as part of their mandate, right? So how do you interface with that changing dynamic? And frankly, other very large tech companies like A ServiceNow, for instance, who have actually also been acquiring their way into cybersecurity and how do you interface or how does the messaging playbook change?

Yeah, look, the thing that's the most important to us is to deliver true platform experience. So where we're different from everybody else is that, so we target maximum usage by the maximum number of users. So the developers and the operations people and folks that, and that's typically not something that you find in security products. Security products have a very small user basis and two we offer a fully fully integrated platform you know so whether we build or we acquire we always do platform and we have fully fully integrated platforms most of the companies that have scaled in security tend to be asset allocators and consolidators you know so they're going to buy a number of companies and they're going to integrate for the sales channel what we think is that when you need to get to get a large number of users using you every day and also when you need to act at machine speed across different streams of data you need an integrated platform you cannot have separate products that just happen to be bundled together by the salesperson so our advantage what makes us special is that we are this integrated platform that's the dna of the company and at the same time we have a business model that also lets us be acquisitive and accelerate the growth of the company.

You said you're essentially constrained by your, well, I'm paraphrasing, you're constrained by your imagination in terms of your output because your velocity in R&D is just infinitely higher now. Two questions around that. What do you expect to be Datadoc's next billion-dollar product franchise, number one. Number two, because your ability to build and innovate is orders of magnitude faster than three to five years ago, how does that alter or modify the way you think about your build versus buy decision? You've been very disciplined. It's been very talking-oriented. So kind of two different sides of the same coin as it relates to R&D.

So in terms of the next billion-dollar product, I mean, look, there's a number of candidates, internally. The one thing I will tell you is that when we brought together the various trends of observability together, we called ourselves three pillars. You know, we said metrics, traces, and logs, like infrastructure, applications, and logs, like whatever, that these are three pillars. I think now we consider that we have four pillars, you know, we're breaking out the digital experience into the fourth pillar, you know, which is really some monitoring and synthetic testing. And it's growing fast, actually accelerating growth over time as it gets bigger and it's a critical part of all of our customer stacks and whether that's the non-AI companies or even the top AI labs you know this is something that is extremely valuable and absolutely everybody needs perhaps even more so in the world of agentic AI and so we see that as a great growing part of the business but yeah there's many parts of the business that could be billion dollar business and we want them all to be in the end like Whenever we start with a new product, we definitely want those products to be $100 million plus products. And we have hopes that it can cross the billion dollar mark. On the build versus buy. Yeah, on the build versus buy, look, we're very, there's no category where we think, oh, we need to buy there. Like we always are very opportunistic in that we look at absolutely everything that we might want to do. we're ready to build everything but we look at the opportunities we have from accelerating from some of the assets the companies we see out there historically it's mostly been about teams and product advantage you know so we have we're going to take a shortcut of maybe two three years by having a team that's been doing that for two three years and has learned the market and we can get to product market fit a lot quicker by acquiring them so that's most of what we've done. We might also do the same thing for distribution. You know, if we think that a specific company will give us a differentiated distribution with some buyers, some part of the market, we might do that too. But the underlying principle is always we're shipping an integrated platform. We're bringing the users together into that platform. We'll bring the use cases together and that's what makes us different in the long run.

On the continuum of size, just to reiterate, you have been doing tuck-ins, the checks have been smaller, but you do have very robust share price currency. So in thinking about where the bar is to do something bigger and bolder, you know, naturally the rubric is going to be different. Wondering, Ali, if you can help share what the parameters of a potentially larger than historical cadence transaction.

Look, there's no hard and fast rule. I think by definition, the larger deals are fewer and far between. The chance that you find exactly the right assets with the right economics and the right alignment and the right everything are very, very low for larger deals. But nothing is out of the question. I think the point here is as long as we believe that you can accelerate the path to someplace we want to go and that we can deliver on a unified platform that everything's on the table.

Ali, I wanted to end our conversation and have you bring out your crystal ball. If you materially outperform financial expectations, investor expectations in the next three years, and there's a lot of goodness that we talked about, right? So barring execution, what will investors have materially underestimated in your abilities or in the market environment at large?

Well, I think the opportunity as a whole. It's pretty clear to me that observability is a major part of any transformation story and the AI story in particular. It's also pretty clear that observability is the last frontier. That's what remains. Like keeping tabs on AI, keeping tabs on the machines, keeping tabs on the agents, whatever the job is, is going to be absolutely key in the long run. So that's a huge opportunity, like a gigantic opportunity. I think it's also pretty clear that the question is not just, it's not who's going to, whether we're going to be there, the question is who's going to be there with us in the end. Like it's a market, great market, great opportunity. It's going to be us, maybe one or two other companies. So with that in mind, I think it would be foolish not to invest in Datadog. You know, that's my take.

I think it's a good place to end the discussion. Thank you so much, Ali, for your thoughts and insights. This was a fascinating discussion. I appreciate it.

Thank you very much.