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

Okay. Good afternoon, everyone. Welcome. I'm Mark Murphy, software analyst with JPMorgan. And it is a great pleasure to be here today with Olivier Pomel, who is the CEO and Co-Founder of Datadog. First off, Olivier, thank you so much for joining us here today.

Thank you for having me.

So most investors in the room are familiar with Datadog. You're approaching a $2 billion type of a revenue run rate. For those who are not, could you give us an overview of Datadog, help us understand the types of problems that you're out there solving? And how have you developed this amazing product that's allowed you to emerge from a very crowded field and become the leader in observability?

Yes. So what we do is observability and security for cloud environments. So we sell to engineers, to companies that are in the cloud or moving into the cloud, which as it turns out, it's just about every single company out there. And we instrument every single layer of their stack, be it their infrastructure, the applications, data logs, what their end users themselves are doing on the platform as well as understanding how to secure those applications. And we do it for a very, very broad set of customers that range on the low end from individuals and students, who don't pay us anything, all the way up to the largest enterprises in the world, that pay us tens of millions of dollars a year and everything in between visibly.

One of the advantages of Datadog that we consistently hear when we're out there doing our field work is, they will tell us that there is this very amazing breadth of capabilities that are contained in a single pane of glass. And you can feel that when you look at a product demo. So customers will tell us that it's a one-stop shop. They'll tell us they can cover a very large portfolio. They'll tell us they can consolidate a lot of different tools into this one Datadog platform. Can you talk about -- talk to us a little bit about what is behind that design ethos? And how do you benefit? How do your customers benefit from having this very clean and cohesive product?

Yes. When I started the company with my co-founder, we actually didn't set up to build a better mousetrap or a better infrastructure monitoring product or better application performance monitoring product. The starting point was to get teams that didn't talk to each other to actually see the world the same way and work together well. In this case, this was the developers and the operations folks. And in previous life, I used to run the development team in [indiscernible] and I used to run the ops team. And we are very, very familiar with the conflict and the finger-pointing that can arise in those situations. So as a result, our approach has always been to be -- to bring as many different teams, as many different data sets, as many different things under the same roof in the same platform as possible. As we go to market with that, we end up, for many, in a way that fits within the existing categories. So even though we have a unified platform without any hard boundaries between products, we're going to display infrastructure monitoring product. We're going to display application performance monitoring products, logging products, what used to be separate categories, but as far as we're concerned, this is a unified platform. So when we build a product and when we -- when we add to our platform, we focus on 2 things. One is getting adopted by as many people inside the enterprise as possible, which is why we don't charge by the seat basically that we want as many eyes on the progress as possible, that's core to our success. The second thing that we optimize for is, to have the maximum surface of contact with our customers' infrastructure and systems. And the combination of those 2 basically being used by everyone every day, being deployed in every single server, every single piece of infrastructure at every single layer is what gives us a surface of contact to then expand and get more product and solve the big and bigger problem inside of that surface of contact over time. That's been the recipe for adding all these products over the past few years.

So that surface area, that ability to have that contact area, as you've described it, it has allowed you to build up to 26,000 customers. You have 3,000 that spend over $100,000 annually at this juncture. We recently spoke to a consultant who told us he has seen Datadog contracts that range in size from $40,000 up to $23 million in annual spend. We know they can get above that. So you can cover a tremendous range of budgets. Most of the software companies that we cover are going to decide, they're either going to target small, medium or large customers. Why is Datadog trying to be out there covering the whole spectrum?

So we focus on the users. And what's interesting about the move to the cloud and modern software in general is that, the users actually look a lot the same, whether they are at small companies or start-ups or at very large enterprises. It is very different from what used to be like 10, 15 years, 20 years ago when I started my career. At that time, when you were at a small company or building something for yourself, you were building on open source and specific set of hosting providers and things like that. When you are at a large enterprise, you would decide whether you buy the Microsoft platform or the Oracle platform, then you have all of these enterprise tools that you build on top of. Today, the building blocks are the same open source components and the same cloud components for everyone. If you're a tiny, tiny company, you're going to build on AWS and some open source. If you are a very, very large company, you're going to be on the same thing. Maybe a few different flavors, a few different providers, but the components are going to be the same across the whole user base. So this is what allows us basically to cover the full spectrum. It's interesting to us. I mean even though when you look at the numbers, the bulk of our revenue comes from the larger enterprises. At the very low end, we have people who pay us a couple of dollars a month. Obviously, it doesn't pay the bills for us. We have -- I think, the bottom half of our customers is about 1% of our revenue. So clearly, we don't do that for the money. We do that the network effects that it gives us as a business. It allows us to be present in all of those different setups, all different situations, integrate ourselves really, really well and support everything that is emerging from a technical perspective, and it also gives us a basis of users to expand upon as people who used to work at small start-ups or who used to be students go work into larger companies and they can adopt our product there.

Okay. So you're able to cover the whole pyramid very gracefully. And with architectural cohesion. But Datadog is also working at a cloud scale, right? So some of these metrics, you're handling over 10 trillion events per day, you have millions of servers that are running at any point in time. So it turns out, that's all it takes to keep the entire Internet running, right? That's all we have to do. What does the engineering project look like? So you're able to do this in a way where you're helping companies prevent bottlenecks and you're actually keeping that large of a surface area secure.

Yes. So the [ number ] you mentioned is actually now it's around 100 trillion events a day that we process. And at any point in time, we're going to have tens of millions of containers run by our customers and on our systems. And we're doing that with a team that's a bit more than 2,000 engineers. And obviously, we have really high constraints in terms of availability, in terms of time to -- freshness of data and things like that, as we are absolutely business-critical for our customers. The hardest part of maintaining that is actually to be able to keep shipping very quickly and build more product as we keep that system up and reliable for our customers. And a lot of efforts and a lot of the tooling we've built have to do with that. The way we do it is we pretty much [ we lean ] in hard on the cloud. The cloud allows us to ship quickly, allows us, for example, to stand brand new instances of our product when we ship new functionality to test different versions of our software in parallel for correctness. It allows us also to change our minds very quickly when we want to do something differently or rebuild a system, which we do on a regular basis, like we've been rebuilding all of our internal storage systems, for example, on a regular basis. The cloud allows us to do that to change our minds. And the last thing we do is we actually -- we dog food quite a bit. So we use -- I think we're not quite the largest user of Datadog, but almost in terms of the number of eyes we have on it, the amount of services and the amount of data we sent to it.

Okay. So Datadog is eating its own dog food. Olivier, if we step back and think about the history of it, Datadog has been known for monitoring and analytics. But you -- very recently, you've made this concerted push into the security market, specifically. And you have said that this strategy is very ambitious. You already had crossed through 5,000 security customers. So this feels like something that's moving along very rapidly. Can you talk to us a bit about your longer-term vision in security? And why would you say that, that is a logical adjacency for Datadog when, as we all know, that security market has very established vendors that have been working on trying to solve some of those problems for a long time.

Yes. So the premise for that is that -- yes, there's plenty of technology and vendors for security. But the problems that these vendors set out to solve are not solved. And we think actually that the problems are getting worse. So basically, the complexity and the attack surface or vulnerability surface of applications has been growing faster than the solutions that have been brought to combat that. And we think it's only getting worse. So the -- our approach is a little bit different. If you look at the way most of the vendors try to approach security, they rely on the security teams to solve that. And the security teams or the security engineers are vastly outnumbered by operational people ops engineers for one thing, about 10:1 and even more vastly engineered -- more vastly outnumbered by the software engineers, who outnumber the ops people by 5:1 or 10:1. So basically, our approach is if you want to solve the security issue, you have to leverage the teams that have the -- that are -- have the most firepower and also the teams that are actually writing and maintaining the software, not just security engineers or security analysts that come further down the pipe and that are vastly outnumbered by the other teams. So that's the premise. In addition to that, it so happens that we already gather all the data we need for security. We are ever at every single layer of the stack. We see everything that is happening, what all engineers are doing, what all end users are doing. So we have all the information necessary. And we don't need customers to go and instrument things another time with another vendor to incur more friction to do that, to incur a tax on data or a tax on CPU to instrument everything again. So we think if you fast forward a few years, it should be irrational not to consolidate your security alongside your observability.

Okay. So -- and maybe security will be an area that will not be subject to this kind of cloud spend optimization trend, right, or not as subject yet. Let's spend a moment talking about that. It has been a crazy 12 months, right, for cloud software. If you go back 1 year ago at this conference, AWS and Azure were growing in the 30s and 40s. Our survey work started to show some cracks there last summer or last fall. It's a little bit of a plug for our survey work. But I think we underestimated the severity of that as well. So customers started pulling back. We now have AWS growing 11% in the month of April. Azure slowing into the high 20s. And so we're sure that you have a good vantage point into this. What do you think changed? And where do you think we go from here on the topic of optimizations?

Well, what changed is -- look, there's a lot of economic uncertainty. A number of large companies are trying to save money on -- not as large companies have had their own economics fleet quite particularly. We've been -- we're going from a situation where a lot of the fast-growing cloud native companies stopped growing as fast. And we have pressure to deliver more profitability to their own investors. I'm sure you're very familiar with that. And that's what yielded the crowd optimization we've seen. I would say this is a system working exactly as intended. The promise of the cloud is that you can change your mind. You can adapt it to your business realities. It's not all some cost. And just because you made a commitment at some point, you don't need to live with it for the next 5 years or 10 years and wait for your data centers to expire. So I think it's exactly -- it's very healthy. The way we see it from the inside is we've always seen cycles of optimization with our customers. That's just the way cloud is adopted. We see customers adopt the cloud, typically not have a lot of visibility into how much they're going to consume, grow, grow, grow. Get to a point where they ask themselves, we need all that, optimize a little bit and then start growing again. And we see them do that on a regular basis. I think what happens in these situations, what we've seen over the past few quarters is that everyone is doing it at the same time. And we see also customers taking several bites at the apple, and you've seen that also publicly with the multiple rounds of layoffs, certain companies have had, where they're not quite sure where they need to end up, or they still have some uncertainties about the environment and optimize on multiple occasions.

So coming off of Microsoft's most recent quarter, the vibe we were getting was their view is that this elevated optimization trend is going to be something that is going to last for quarters, not years. Is that something that you would agree with?

Yes. I mean I can't give you the number of quarters. That's -- I think we're all trying to see what's going to happen here. But definitely, we're very, very optimistic about the mid- to long term. We're cautious about the near term.

Okay. So maybe then we can zoom out and talk about the multiyear view on this. Every year, we run a large-scale CIO survey. In the most recent one, it showed that 22% of all IT spend is being -- is going to public cloud. These large CIOs see that rising to 42%, 5 years forward from today. So there's a near doubling, right, in the mix. This is a major kind of rearchitecting. So I think what investors want to know is there's a debate out there, has cloud spend reached a maturity level where, say, growth is just going to be in the teens. And I'm talking about bigger picture, including the hyperscalers. We would assume Datadog would grow well above that because you always have, and there are good reasons why. Do you think that, that is going to happen? Or do you kind of foresee a bigger rebound coming at some point? Where we would look back and say that really -- this really was more of a temporary slowdown.

Yes. So look, it's hard to say what the numbers are actually going to be. When you look at all the folks whose job it is to try and forecast the outside macro trends. I mean, you look at -- I think there were reports from Gartner recently that we're pegging the growth -- annual growth over the next 3, 4 years at 20% for cloud environments. We think it's a reasonable estimate. We'll see whether we come under or above that. To your point, historically, we've outperformed the growth of the cloud providers, and there's a few reasons for that, the fact that we're increasing scope and distribution as we do so. And if you step back a little bit and if you think about the big drivers for this increase, the main 2 drivers are digital transformation and cloud migration. I think -- if you think of the latest developments with the rise of AI and -- it is only going to accelerate digital transformation and cloud migration. To be able to use AI, everybody will need to transform with all the new technologies we're seeing and the new innovation in AI. To be able to use that, you need to be digital, like you need to have your data. You can't do it without it. So you absolutely need that. And then the only way to adopt it is to actually be on the cloud. I don't even know how you would build an on-prem AI strategy today. Who would you even know to buy what to do? Like technology is changing so fast that you would have to redo everything every 6 months or every year. So I think those 2 trends are going to be, if nothing else, accelerated and emphasized by the rise of AI.

Okay. So -- and thank you for bringing up generative AI without me having to. We're, of course, going to get to that topic in just a moment. But before we move on from talking about the optimization trend, does it matter to Datadog which hyperscaler is growing faster? Because people are noticing that out there today, it's actually Azure and Google. Right now, at this instant, those 2 are growing a little faster relative to AWS. And I think people try to run the math and they try to understand, do those relative weightings matter to Datadog?

Yes. So the answer is no, it doesn't really matter. We're equally well positioned with all of the various cloud providers to capture new workloads. If you look at our business today, we're more heavily weighted towards AWS, and that's because that's where the bulk of the market was when we started, that's also where the cloud workloads were initially. Like if you compare AWS and Azure, for example, Azure has a lot more of traditional lift and shift purely Microsoft environment, for which we don't bring as much value as for more modern workloads, more workloads that are heterogeneous in terms of the technology stacks that they involve. But if you look at the newer workloads, what actually is going to drive the growth for AWS for GCP and for Azure, these are all very cloudy, these are all very heterogeneous. And in all likelihood, they all are going to give quite a bit of room to capitalizing on data and AI.