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

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 Boolani. I jointly had 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 Pomel, Olivier, thank you so much for taking some time to sit down with me this afternoon. .

Thank you for having me. We actually were 15-minute work away from here. So I can get right back to work afterwards.

Yes, you got your steps in. Lea.We want to give you a balanced schedule today. I wanted to set the stage with you with the word Suite 16, Datadog is 16 years old as of June, and you're approaching right around half that time as a public company, went public in 2019. So I wanted to ask you, what is the latest and greatest on where Datadog is today and how the company has evolved 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. So when we -- 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. That's what [indiscernible] -- 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 in order of magnitude larger in terms of revenue in order of magnitude. I think we also increase the share price and other magnitude since we went public. So all that's correct. And I think that's also -- I mean, people ask me sometimes, so why are you still here? And I'm here for the next 10 years. I'm here 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 we've seen an acceleration of growth. I think earnings are -- we had earnings a month ago, and I get some of the numbers slightly wrong, but we reaccelerated 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 customer is growing very fast. And that those they are -- but the rest of the business, the non-AI customers, all the companies that existed depot hands and that are not primarily in the business of AI have been accelerating over the past year or 2. I think we said on the call we accelerated since from 1 year ago from the 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 this, as in the context of cloud migration, profitability was a key part of the transformation. We also see that observability is a key part of the transformation. We see it in the way this happens with the customers, but we also see it when we think of the end state, so where is everything going? Where do we end up 5 or 10 years from now. Observability is probably the only category that remains. If AI does a lot more of the work, whatever the work is, keeping tabs on the AI, understanding what is 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 the -- probably the last software category that remains in the end. So we feel pretty excited about it and very busy building it out.

Oli, 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 2 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 for mental models out what could be the state of technology, the state of the enterprise software or infrastructure software stack 3 years from now, 5 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 the 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 successful in the cloud edge, part of it was that the roles were changing. So you had -- you needed to ship faster, you need to iterate faster. As a result, roles that used to be completely separate between development and operations became much 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 we will see quite a bit of that between not just development and operations, but also security, maybe some of the business functions. I mean you see the way people can build software today that nontechnical product managers can build software. So all of those roles are being pushed together, which benefits us as a business because we're in the business of bringing those people into 1 platform and having one representation of the world that cuts across all of those different concerns. We -- in terms of what we can observe, 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 buildout actually looks like the cloud build-out. And then there's a few new layers that are appearing, there's -- there's a very deep stack we need to be able to observe and manage for the AI transition, but we'll see all that. And just to backtrack for a minute, we talk a lot about AI and all of the net new and all the new areas we can get into. But even if you just restrict yourself to observability in 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 growing 50%, 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. So just the core of what we do, just extending to the 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 a simplistic form of their observability for AI and then there is 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, agenetic AI systems versus helping customers have better platform efficacy and utility and 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?

Yes. So I mean thank you, this is almost all branding. 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 developed for AI and how we observe basically the whole stack. So a lot of the stack is actually the same as the non AI. So most of the AI companies are built on infrastructure that other companies also use. Most of the AI agents are actually spending the majority of their time using tools, the tools that AI agents are using are standard applications, and those applications also need to be built and monitored 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 talk infrastructure that has been around for a long time, but was never really used that large scale in production environment. So 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 malls 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. This -- today, the part of the business is 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 -- is showing us where the world is going rather than already today the driving part of the business.

[indiscernible] 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 in my opinion, the manifestation of 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?

Yes. So this is now -- so AI for that. So how do we make everything that Datadog does even better through AI automation? And Bitz AI is our AI agent. And underneath -- it's a collection of different agents that perform various different things, but all branded under Bitz AI. And we started -- I think we released last year, the first version of it, which was focused on investigations. So Bitz AI would trigger when you get an alert would investigate it would tell you what happen why and we help you fix it. And we've now extended that to a number of different surfaces. So Bitz 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 better context. It can do a whole bunch of different things that was not doing before. We have a 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 2 customers in Japan. So, I think maybe because they didn't have the weekend off, I got e-mails from the Japan team. And so this actually -- at the same time, 2 different comers, 1 large retailer, 1 bank that are using Bits AI and that like it so much that they went out of their way to email us, this is great. You see this work super well. And in both cases, what happened is that -- they had some issue that happened in both cases was a recording issue, something they've had for years. And they could never quite sider it out. They could always just apply bandaids and get things back and that's it. Now it would happen again. And in both cases, they're actually able to figure out those issues, thanks to Bits AI and fixed and in process so that they can't recur anymore. They were so impressed they e-mail us straight on that. So it's pretty transformative for them. In terms of where 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. We usually have 1 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 the product to cover a lot of different services. And so you should -- we probably will comment on that in the future, but for now, there's -- the transformation is happening right now in terms of the way we're chartering those. And by the way, [indiscernible] the world as a whole doesn't quite know how to charge for agents just yet. At the low level, have the model companies that are challenging by TOKIN. And then above that, it's a little bit unclear. As far as we're concerned, we don't really care. -- where competitive usage-based. We can very easily add c, change cues, attached to different things that we -- that scale with the activity or the data volumes that customers send us. And so -- the question for us is more to understand what resonates best with the customers? And also, what is adopted in industry wide as the right way to charge for point diligence.

I think the notion of pricing and packaging is an important 1 and a conversation, I think, is very important to have I don't think there's ever been a time in the technology landscape and from a procurement standpoint, where decision makes 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 with this in mind and the magnitude of innovation that average large organization now has to productionize by way of AI how do you except Datadog's strategy to evolve from building 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 unchartered 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?

Yes. 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 it online. For the cloud edge, the usage base has been the best way for us to sell and customers to buy. I think the AI is going to be some version of that. But you're right. I mean, initially, we price everything according to the volumes of data, we were receiving. . Nothing about what comes up, not thinking 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 agents. We have some agents that even work on data, we don't invest. So we are -- we announced the the decoupling of our security agent for more Class C, for example, so you can actually use AI smart on other data sources today. So there's -- and we also can generate code. We can do things that don't can sell any period 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, that had 2 different parts, the data volumes, the holes or anything else, that's something we can easily do. Now when you look at the recent customers consolidate on us -- one of the reasons is that it's a lot easier for them to have 1 big pool of commitment and spending with 1 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, they use co-differential is fungible. So they can say, you know what, we're going to actually -- we change our mind. We don't need as many costs, but we need more or we don't need -- we're going to use more IPN less logs because it's more efficient in these different ways. Customers don't have to care. Like all of that is fungible, prolextengible, and that's why the -- 1 of the reasons they consolidate with us.

I wanted to continue on this threat around and maybe zoom out a little bit and talk to you about the addressable market opportunity -- earlier in your comments, you mentioned hey, actually, the shift to AI and agentic AI and AI systems actually has 80% to 90% 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 that calculus change entirely? And what are you doing to affect the most positive range that it's disproportionately beneficial for you?

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

A little bit higher than that?

So, I think we can do so much more, like we can grow so much more with all of those customers. And so there's -- 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 as customers expand and do more with AI, they what drives them to buy us is that to have an escalation of complexity. They do so many more things. So they're so much more productive in general. And what I defined by productivity is the ratio between the output and the time you spend on it or the human time is spent on it. That ratio has been exploding in software engineering over the past 40, 50 years and is going to keep exporting even more now with AI, so with that in mind, we end up with crazy amount of complexity. Our job is to help customers deal with that complexity. And so that's why they're going to be to spend at the end of the day in the cloud, when we're fully penetrated with the customer, they spend between, let's call it, around 10%, maybe between 10% and 20% of their cloud bill on their on observability and security and everything else with us. And I think some version of that will remain so in the AI world, where the spend on is going to be this percentage of the total deal that they actually justify that past 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 their trust in the quality of the outcomes we 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.

One other observation and theme that you've been very consistent on is hey, you've shared with the investor community, the sort of bifurcation in your revenue between, let's just call them the AI natives as you do and the non-AI native. 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.

Yes. And we talked about them separately because we saw different growth profiles. Obviously, the [indiscernible] is more recent companies. They all -- I mean, you've seen all the demos private companies, some of them are not theme could classify as [indiscernible] because they're substantially all about AI, even though they are not new companies, but they all are going through massive build-outs of their infrastructure and their applications and massive levels of investment. We separate them out for that reason. 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 not pretty much every new company is an and everybody else is not so that and the AI is fitting to the rest of the world as well. So the distinction doesn't make any more any sense anymore. So in terms of the usage agentics what's striking is how similar they are, as you said. Everybody starts with infrastructure and applications and logs, -- everybody has to get things rinto 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. So especially when you go into the Frontier labs, for example, like you see customers that are or users that are manipulating thousands or tens of thousands of agents 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. And obviously, when you have free inference and all the incentive in the world to push your models to the MAX. You are not in the same world as the enterprises that get the bill at the end of the month or every single token avinferance they use. But we benefit a lot as a company from serving a very large part of the say, the new wave of companies, whether that's the 2011, but also the slightly smaller Ares 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 customer base at the end. But in terms of product footprint, a lot of it is 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 native. That size of installed base was less than 100 about 2 years ago. It's almost 8x so about 750 customers in the AI native cohort. I know we spend a lot of time in 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 the 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 and user activity and they 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 0 stock. I mean what are some of the challenges and considerations on the other side of, hey, these businesses are basically 3 years old, and there's a lot of concentration here.

Yes. So that's a great question. And obviously, that's something that we're very careful about because we -- so we've been through the highs of COVID. We've been to the lows of COVID after that. We've seen the -- a lot of those cloud-native companies at the same time, digital native companies extent like crazy and 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 suffered from the compression there. So we are very, very careful about how we assess these accounts, who we handle the -- who we watch for an healthy behavior, for example, from customers and things like that, and we're very, very good not getting into 1 of all of that in a way that we've run over the past couple of years. The thing I would say, though, is that the exposure we have to the NAT is much smaller. So our business has grown quite a bit. It's very diversified. And we are very, very far from the exposure we have -- we had to be club native at a time in terms of the export of the antics 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, with healthy growth, what's unhealthy growth with healthy usage and how we think this will play out in the end. The other thing I will say is that the -- when you see all of those build-outs from the ANA, -- all of that is serving the rest of the customer base. As said earlier, I started the keynote, we have decided 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 -- and so we see it is not an isolated benefit to 1 part of the isolated runaway investment from 1 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 to 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, 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 -- in the rising role of been sourced with open telemetry, potentially 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.

Yes. I think we've -- the picture hasn't changed that much in the 16 years of the company. So there's always like a large number of companies that are doing observability -- so -- and there's also -- the main thing people are using or start using from day 1 is open source. And there's a rotating cast of companies there and technologies and everything else. And -- some of them were very popular 6 years ago, don't exist anymore or not so player anymore. Some of them were very popular 7, 8 years ago as 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 the PSM, we can do that for them, and we do that butane. -- if you look back at the -- or if you go look at the transcript of our latest earnings call, we -- I think we have, we call it like 506 customer consolidations. And all of those typically involve a number of open source products that consolidate on us. In terms of open telemetry it's amazing, like it reduces a friction to instrument. It gets us more workloads faster. It makes customers more confident in expanding and get both fit into our platform. And so that's great. So we support that. We invest in it. And we're glad is there, and it's actually a tailwind for us. So we feel were 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 2 business customers buy your product, they save money or they make money. And we are very good at making that case and demonstrating it and having customer seat with the first few products they are 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 the 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 5, 10 years from now and what got us -- well, we are to different when we do the company public, 6 or 7 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 2 years because the vendor will not be based at a 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, Oli, is where do you believe Datadog's competitive differentiation has actually widened the most in the last year. So 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?

Yes. Yes. So there's 2 things I would give you. So the first one is the 1, I just mentioned, which is a 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. 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 differentially. The other part of the business structure is that we have a very wide customer base. We serve everybody from the small start-up 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 1 or the other, they can't be both. And that gives us a lot of pretty impressive flywheel in terms of who we can get into new customers who we can expand and also who we can get a reading on what's coming up next in the market, thanks to the long list of smaller companies and individuals and started that use us. So that's the first one. The second one, and I think that's the 1 that's becoming more and more important is the fact that we are a SaaS vendor, we access to a lot of extremely clean operationally relevant data that we can use to then train malls to automate all of the operations, securing all of the other problems we deal with for our customers. When we started the company, it was always the intent. It was, hey, we're going to be to build the company that's going to be SaaS, so we can do that. When you start building a companies, you get all sorts of offers some customers that say, Hey, RebitBank, would use you, but would like an on-prem -- 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 not using see what we're not working the environment. So we've done that. Today, I think is when this promise comes to life because we're at a time where, of course, when 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 tell them to our needs. We can build those models based on very specific types of data that are relevant to observability. So think of it not so much in terms of words as you see in the terms, but more in terms of traces and metrics and logs and network topology and all of those things that take a slightly different shape that we can inject 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 2 models of Total and 2.0, which we released a few months ago, both of which were state-of-the-art at the time of release, and these are fairly small models that these are not gigantic models that consume genetic several 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 company that was building reinforcement learning to tune and build customer models. And that team has 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 future on a 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 community and myself included, have been positively surprised about Oli is this idea of the DIY or the in-sourcing kind of debate, right? You have ask customers, some of the most innovative companies in the world, they are doing very Avant guard things and 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 in terms of their decision and decisioning and the reasoning around, hey, we want to align with Datadog to solve these challenges where these are actually the most financially and intellectually most well financially and intellectually resource companies in the world, right? So how do you bury that debate around at some point, some of these very large companies may in-source. They may do it themselves.

Yes. So it's a few things. So why is -- every single engineer thinks that they could and should be building observability and I know because I've done it, you don't say .