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

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

Thank you for having me here.

So most investors in this audience are pretty familiar with Datadog. You're approaching a $2.5 billion revenue run rate. For those in the audience who might not be familiar, can you describe Datadog? Can you explain the types of problems you solve? And how is it that you've emerged from this crowded field to become the leader in observability?

Yes. So what we do is we do observability and security for cloud environments. So we basically sell to engineering teams in companies big and small. Anybody from the tiniest teams of students or even individuals who don't pay us anything, all the way up to the largest banks and telcos and software companies in the world that use our software. We started the company not really as a better mousetrap. So the idea was not to look at IBM Tivoli or BMC or all these things and build a better one. The starting point was to get teams that didn't speak to each other to actually share the same reality, work together and solve the same problem together. So initially, it was between development and operations teams. Today, it includes many other teams, being the security teams, for example. But really, that's what led us to start with not just with a very narrow focus on the product category, but with a platform approach and trying to bring as many datasets and as many teams into the product as possible to make it as approachable as possible along the way. So the story of the company since founding has been one of continuously investing back into R&D, and we can talk some more about the efficient model that lets us do that, and expand the footprint we can have by covering more of those different use cases, bringing more datasets and more teams into our platform.

So Datadog's revenue growth actually accelerated in Q1, right, we saw 27%. It also accelerated for Amazon AWS and accelerated for Azure, but there wasn't much of that in this Q1 earnings season. We think, back to last November, you had actually called out a resumption in activity among the cloud natives. And in the recent Q1, you actually called out an acceleration in activity from your larger enterprise customers. So Olivier, since you do have this unique kind of window into the broader cloud economy, why do you think it is that we saw this trend of, let's say, the past 2 or 3 quarters, where there's a resumption in activity? Do you think it's going to continue? Or could it continue? Could there be further acceleration on both those sides, the cloud natives and enterprise?

Yes. So in general, I think we -- what we're seeing right now is we see, today, it's quite a bit better than what we saw 1 year ago. So 1 year ago, everything was slowing down and, in particular, the cloud-native part of our customers. So the folks that were all in on the cloud [ were too old ] already and were spending a significant fraction of their IT budget but also of their overall budget or overall OpEx into cloud. Those were slowing down quite a bit. I think, today, we're in a position where the world is different. We're seeing stabilization or growth again from the most of the customer base, which is much better. If you drill down a little bit further, what you see is that on the -- you should disaggregate the cloud native into 2 buckets. The AI-native folks are growing gangbusters right now. And I'm sure you got some exposure to that, and so they're all growing very fast. The other, I would say, we'd call them a little bit older, web native or mobile native, are growing but more cautiously. And then if you move up market to large enterprises, large enterprises are accelerating growth. They were still growing last year. Last year, when everybody was slowing down, the enterprise slowed down a little bit, but not all that much because the enterprise is a lot earlier in its journey to the cloud. And as a result, the cloud spend is a smaller fraction of the IT spend, and the IT spend in general also is a much smaller fraction of their total revenue than it is for the cloud natives. So we saw some reacceleration there, though broadly in line with what we had seen before. The result is what you had called out earlier, which is a small but perfectly form of reacceleration for us. And in general, I would say, things are looking good. The world is starting to grow again, but it's -- I would say, it's a stabilization and a smaller acceleration, not a massive acceleration of all the cloud consumption everywhere across the spectrum. And yes, that's about it.

So better than a year ago when you were here with us at the Boston TMC party, as we like to call it. It's -- you brought up Gen AI. Gen AI is explosive, we'll come back to that in a moment, but it's not a V-shaped recovery.

Yes. And in general, the growth of the group, I would say, looking at investor expectations leading into our quarter, I would say, in general, there's a higher expectation of acceleration in the short term. But there's an underestimation of what the growth is going to be in the mid- to long term, which is, again, what explains some of the things you might have seen in our numbers, such as the fact that we are accelerating hiring and accelerating the investment base.

We'll come back to that in a moment. Before we do, I wanted to think back to your recent Analyst Day, which was just a couple of months ago in February, and there was this comment that got our attention. You said, "We think observability alone holds the next 5x or 10x scale up for us." And given that Datadog exited $2.5 billion run rate, right, it's a pretty fascinating comment. So we wanted to pull on that thread a little bit. What do you think, Olivier, has to happen for Datadog to scale like that, where you would become a $10 billion or $20 billion revenue situation in observability?

Yes. So I mean if you look at the big categories in a top-down fashion, so the main one we play in is IT operations management, so ITOM. It's a market -- so we are the leaders right now in that part, and we have only 9% to 10% of that market today. So there's a lot more room to be had there. That market, ITOM, itself is growing, I think, the estimations are 10%, 11% CAGR over the next few years. If you look at the next closest market, which is cloud services, is growing 20% over the next few years. So it doesn't take a lot of imagination to project ourselves 5 years into the future, see a total market that's twice the size than it is today. And us having been a share taker of consistently over the past decade and being a share taker again in the future, it doesn't take a lot of imagination to see how we can get to 5x to 10x the size we are today. Obviously, there's a lot that goes into that. There's a lot of work that is required, a lot of investment, that's what we're doing, but we think it's there.

Okay. It doesn't require a lot of imagination, I like that. So now, along this path, Olivier, of 5 to 10x in the business, do you think we should subscribe to the view that Datadog would be able to kind of maintain revenue growth at a higher level than when we look across the hyperscalers? So if you look across AWS, Azure, GCP, et cetera, on the basis that I believe Datadog is something like 1% today of all that spend that goes into that stack, is there a scenario where this is going to rise? We're going to look back and say, now it's 1.5%. Now it's 2% and beyond because the workloads themselves are becoming a lot more complex. So I wanted to ask you what you think about the growth algorithm. Because I think we have felt it as a bit of a fallacy to just try to look at Datadog and say, it will grow at exactly twice the rate of AWS every single quarter. So something about that seems a little too simplistic, and you're exposed much more broadly.

Yes. And I would say, this is the kind of thing that works well in the mid- to long term, not so well in the short term when you try to line up the numbers. One thing to keep in mind is we're not a one-to-one to the cloud providers in terms of how our numbers work. We have other products that don't relate directly to what they see in the numbers. They have other products, especially the case for, let's call it, Google and Azure have a lot of other things in there, but it's also the case for AWS. We don't recognize revenue the same way necessarily as they do. And we don't have the same exact customer or geographical mix. So the numbers are really hard to reconcile on a quarter-to-quarter basis. That being said, we are exposed to the same trends as they are. I mean we -- the growth of our business is driven by cloud migration and digital transformation and now the curve of AI adoption. And I think this is what propels those 3 companies, and this is what's going to propel us as well. If you think of the ways our share of the overall cloud market is going to increase, there's a couple of ways. The first one is that for the products we have today, we're still largely underpenetrated with our existing customers. So our customers largely don't use all of our products yet. And even when they use several of our products, there's a lot of margin for growth within those products. So when customers adopt our APM, for example, it's going to take them years to deploy that across all of their workloads, all of their teams, everywhere they think they possibly can, which is why those products still have a lot -- a very long runway. We still have also a lot of customers to acquire. So we don't have -- we're not present at the same scale as the big cloud providers in all the geographies and all the segments. A good example of that will be government, where we are very, very small compared to the scale of cloud there. But there are many, many other examples across the world of segments and geographies we don't cover very well. And even in the geographies we cover today, we have less customers in the cloud providers. So there's a lot of upside to be had there. The last way for us to increase this share is that we are still expanding the product footprint. So there are still many categories that relate to running, securing, understanding, shipping applications in cloud environments. There's still many of these categories we don't cover yet, and we intend to cover in the future. So you should see us provide more products and have more of an even longer runway for our customers to expand into our platform. So when you combine all of that, basically, that this is what you get from the 1% we are today to 2% later and maybe more. When a customer is fully penetrated with Datadog today, the share of their cloud still represent might depend on the type of customers and what they do. But it's fair to say that when they use everything we have today, we have somewhere between 5% and 15% of their cloud bill. So as we grow and as we further penetrate the market, we can see how we can get up on that curve.

So you mentioned the evolution of the platform. And we have always put Datadog on a pedestal because we really hold these organic, homegrown, cohesive platforms in a very, very high regard. We think that, that is a different classification of a software company, right, especially when you compare it to the ones that are rolling up and bolting on. And it feels to us like this is the kind of platform that is going to continually expand and really become a center of gravity. Now I think back, Olivier, to the very recent earnings call, and you used this word consolidation many times. You mentioned a slew of these consolidation wins, where a company will take a whole bunch of point products, right, and they'll consolidate it all onto this Datadog single pane of glass. Are we reaching some kind of a tipping point here today, where you have this fragmented market out there and it starts consolidating on Datadog?

Well, I mean, that's definitely the plan is to make sure that customers consolidate on us. That's why we expand, we cover so many of the different categories that we need to cover, and we turn customers from being system integrators on their own to having a unified view of the world across teams and across datasets. So that's the strategy, so to speak, there. We're starting to see tipping points. The tipping points have more to do with how far along individual customers are in their cloud journey. So basically, as the cloud side of their business represent more and more or enough of their overall business and of their overall mind share, they're going to consolidate a lot of their existing stuff on us as well. And so we mentioned a lot of those on our earnings calls, that we see -- we expect to see a lot more of that moving forward. We've seen some tipping points already, one of which being the fact that, last year, customers were a lot more cost conscious. So taking out existing products or using tools when they were adapting more was more important to them. But we expect to see -- to have more tipping points in the future as we cover more and as they get further into their migration.

Okay. Looking forward to more tipping points. So okay, let me explore 1 other possible tipping point. Splunk and New Relic were acquired, both, in the last year. That's a real change to the competitive landscape in a span of about 9 months. Can you comment on that, to some extent? I think we're curious if that's opening up any more of these consolidation opportunities a little faster.

Yes. So one thing I will say is that, in general, we tend to obsess over customers and all of our competitors. So we don't overfocus on whatever happens to them. I will say, though, that it is true there's going to be opportunities just because those 2 companies have been acquired. And so New Relic is a relatively small business, but Splunk is a sizable business. We think there's definitely opportunities there. And I know that internally, we're accelerating some work on the product side that might be helpful for that. So we're -- it's not lost on us, and we think we -- there is some things we can do to help.

Olivier, one of my favorite quotes came from a major partner of yours that we were speaking with. And I had asked this person, I said, "What is the marketing message that is coming out of the Datadog sales team?" And he thought about it, and he said, "Datadog lets the product do the talking." And we don't hear that too often. So it feels like it's not marketing propaganda. It is a product that you have built that is demonstrating mastery in this domain of observability and beyond. Can you kind of speak to that engineering ethos? What is it you've done that's allowing the product to speak for itself?

Yes. I don't think that's necessarily just engineering ethos. That's about the way we structure and we build the company and the kind of feedback loops we have within the company. So there's one thing I will mention right away, which is that we're very careful about platform purity, which means that when we acquire companies, teams, products, we replatform them. So what we deliver to our customers is a really unified platform, not a set of different products bolted together. And that's very important to us, and we'll keep doing that. So we won't stop doing any M&A, and we'll keep doing that. Another thing we're doing is that we're structuring the company in a way that will serve a very wide array of customers. So I said in my intro that we serve everyone from the students and the people who don't pay us anything, tiny companies, all the way up to the largest enterprises. But our money mostly comes from the larger side of the spectrum. So the bottom half of our customers represent just, I think, a couple of percent of our revenue. So from a purely monetary standpoint, it doesn't really make sense to sell the bottom half. Why would we bother? We bother because it gives us very broad exposure to absolutely the whole set of possible setups and stacks and new technologies and everything that is being done and also gives us a lot of awareness with developers and the folks that might work in smaller companies. And that awareness we have of everything that's going on, as well as that feedback loop we create with all those smaller companies and individual users, helps us build a broader and better product in the long run, a product that doesn't get overfitted to the needs of a few very large, very sophisticated customers. In the long run, it's a big advantage. Another thing we're doing, again, in terms of the feedback loops and the way we structured the company is that we are very careful about having very clear metrics and revenue metrics associated with every single product we develop. So we avoid bundling whenever we can. And as a result, we get a very clear signal on whether any given product is useful and invaluable. What can happen very often is that customers are very excited about the product, but they just don't want to pay for it or they don't want to pay what's needed to actually sustain the product. I think having this very clear, honest feedback loops in the company helps us build the right thing and not just accumulate good stuff that in the end is not all that useful.

Yes. And not being used and not generating revenue.

Yes, yes.

So okay, so you're describing to us the breadth of customers that you're able to take on. It is impressive. What about the breadth of the product scope? How ambitious do you want to be here? Because you've gone from infrastructure, you've gone to app monitoring, you've gone into logging. They've all scaled to a very large size. But quietly, you've been planting the seeds, you've been planting them in security and elsewhere. And so should we think about Datadog as somehow becoming -- I don't know if best-of-breed is the right word, or becoming a market share leader in security, in service management and all these other areas outside of observability?

Yes. I mean, look, at the end of the day, we want our products in each and every one of these categories to be best of breed. So even though our strategy is to land with observability and land in general with infrastructure monitoring and then expand to the other products, each and every one of those individual products should grow to be a market leader in its own right. And what makes them even more special is that the platform integrates everything, takes the headache out of managing and paying for data multiple times for customers and integrating everything internally and having teams in different silos. So we solve all that for customers. The way we make that happen on the back end of it, in relation to all the good stuff I said earlier about the way we structure or the signals we have for running engineering and running product, we also are very disciplined about reinvesting into building product. So one key way to look at our business is that -- so we built a very efficient business when it comes to G&A and go to market, and I would say also very efficient when it comes to gross margins. As a result, we can reinvest about 30% of our top line in R&D. And that is what helps us stay ahead with the products we already have, but also keep expanding the footprint and building more. And then the end result with that in a few project -- us 5, 10 years into the future is that we get to a level where it should be irrational for customers to use anything else because we cover the full spectrum, and we cover it better than the others, and we keep improving it at a way that the rest of the field cannot follow.