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

Everyone, welcome to the Datadog session where we're anticipating strong suits versus weak in pop culture and Trivia. It's a real pleasure to have David, CFO of Datadog on stage with me. David, really appreciate you making the time to be with us this morning. We started having a couple of really interesting conversations over denim last night that I'd love to talk through a little bit with the broader audience.

The first one is this idea of applying proprietary data to an SLM or an LLM and Datadog has a really unique data set, if I think about the types of observability data that you've been collecting since the founding of the company. Tell us a little bit about what Datadog can do with that time series forecast and how you could apply it to the next product cycles in your business?

Yes, great question. So we used to call it ML or correlation. So for a long time, Datadog's one of their strengths has been to be able to aggregate enough information around the signals of the functioning of client-facing applications. and to be able to be somewhat predictive. And AI and models have provided a very unique opportunity. We recently made an acquisition of a company called Adaptive specialist in reinforcement learning around areas in IT management is arability that we cover. And that with the data we have, on observability and the functionality of applications. And the research lab that we've created -- we put out a model a while ago called Toto, but that's just the beginning of what we think will be a very strong competitive advantage, which is using AI and models, some of which will be open source, some of which may be the foundation companies to own the data and produce models, which are going to be predictive of issues around the functionality of applications, severability. And we're investing behind that. We talked about that last night, both in terms of people the GPUs, the infras, et cetera. And that data is not public. That data is Datadog has because of our position in size and isorability. And we think that will deliver a lot of value to clients and also be a competitive advantage and the evolution of the platform.

Let's say on the advantages that it can give your clients, if I think about the Holy Grail in observability, there's this idea of automated site reliability caring and you have a little bit of that with the bits product. So maybe just paint the vision for us when you talk to the founding team where could a technology like Toto go over time?

Yes. The vision here is to produce more accurate and quicker, real-time signals which can improve the functionality of the platform and go on the continuum to allow for auto remediation or close to it. and that will be the speed of analyzing problems all the way towards having the platform able to act independently -- that will be -- when we get there, we're not there yet. It will be a combination of the evolution of the platform. Our bit product line is what is going to use these models and data to be predictive -- and then clients in our vision will be able to make the choice of how much to auto remediate to say for this type of issue, the platform cannot remediate for this type, there'll be a suggestion and then someone will have to press yes. And that has tremendous ramifications, both in terms of the speed and also the efficiency in human capital in this endeavor, and that's the vision of our founder, Ali and where we're investing behind.

The other interesting trend to pull on here is the sider of an inference economy.

Yes.

And what I mean by that is we're so early when you look at AI adoption and inference in particular at classic enterprises, classic Datadog customers, such that if you take a step back and say, well, we're at the very early stages of an inference cycle here that could have really interesting implications for Datadog's growth rate, not just this year but over a 3-year time frame from a structural standpoint. So talk us through that a little bit. What are you seeing at the typical customer? When a customer goes from 0 inference to 1%, 3% of the OpEx budget going to inference. What are the implications for Datadog's opportunity at that customer?

So Datadog is in the business of monitoring whatever could infect functionality of an application. And the more complex it goes, there's more in it, the more opportunity to get revenue. And I think we talked about last night that early on, the first wave was essentially calling out to the model companies through APIs. So the first way to monetize this is Datadog for AI, meaning it's just like Datadog's monitoring, code monitoring, CPUs, Datadog's monitoring databases. This is something else that we've begun to monetize through our agent observability. And although early on, we're seeing thousands of customers use this, and we're starting to get the revenue streams from it. And what we believe is happening is if we look at some of the other metrics of building out AI-enabled applications through a combination of the outside models, open source and inference is that creates a whole another set of things to monitor that we're starting to see in our bits products, in our MCP server calls and other indications, a lot of activity, which, again, will increase the complexity, and we believe create a more importance and observability, more to monitor and more velocity and application creation, which should all benefit Datadog.

There is a sovereign thread in here around, we've had actually a number of companies. We think about such an Adela talking about Frontier ecosystems. Corewave talking about more customers wanting to run their entire trading, post-trading inference stack. We even at Goldman, we've been talking just this morning about proprietary data at Goldman that can be leveraged with the model. Are you starting to see what your customer base, are you starting to see that shift occur between, hey, we're just going to use the Frontier Lab. We're going to outbound APIs versus actually we want more of that to be in-house and we're going to run more proprietary verses internally?

We're starting to see that in the metric I mentioned on usage, but also a Datadog. So we talked earlier about our models we early on use the outside models and the foundational models, and we put that in the platform and similar to what you're talking about at Goldman, we own the data. We're the experts in observability. We have the ability to create from a functionality and also a cost model that we must perform in and we're starting to invest behind it, which I think is going to result in a reallocation of the cost of our GPU -- our token costs towards our proprietary models. Early on, it takes investment. It takes lift to do that. But just like you're talking about at Goldman, we're seeing that in the metrics we're seeing in our observability and also what Datadog is doing in its own research lab. Agree with you. Yes.

Let me ask you about what some of your more sophisticated customers are doing, including some of the Frontier Lab wins that you talk about in your AI native cohort. There is a tendency from the outside and will see reported ARR numbers from Frontier Lab. And we'll extrapolate animal say, well, Datadog depending on what the contract looks like should have a really interesting correlation with what's reported publicly. Now in reality, it's a little bit more comp than that. So I know you can't speak on any customer. Talk about that cohort in general, what are you seeing in terms of usage patterns? And how does that map back to the Datadog wallet?

Definitely. It's an important thing. We basically have invented this AI native and non-AI native. Fundamentally, the AI natives are cloud native companies. What they have in common is they're investing in modern applications. they're experiencing an accelerated demand environment, and they don't have legacy infrastructure. So essentially -- and their whole business is delivering this functionality in the models. So what we're seeing is, one, we're seeing strong growth in workloads; two, we're seeing a pattern of outsourcing to Datadog. So a number of these companies started out doing -- there's always a trade-off between do-it-yourself or using a Datadog. Now these companies have a huge R&D pipeline. And what they're increasingly realizing is it's not efficient to spend that, building your own durability, use Datadog. So we're seeing a movement from open source or initial efforts or cobbling it together towards Datadog. We're seeing a use of because they're newer customers, a use of many of our products, the platform. So we're seeing the average use of products to be at the higher end, but the main thing we're seeing is accelerated growth. They're very similar to other modern software companies that are experiencing demand cycle. Now most of this is in production, inference and production. What we're also seeing, which we talked about on our earnings call, which really struck us originally said, well, Datadog's really the production company, inference and production, we're really not going to see a lot of demand on training. But then what happened to us is some of the larger hyperscalers and other companies came to us and said, "We want to use you for training or post training workloads." Which we talked about. And that was a surprise to us, but then we thought back and we said, you know what's happening. The speed of this is such that the preproduction or late training into production is starting to merge, which created this revenue stream. We don't believe we not be evidence to say that we're going to be the training company, but we're also seeing in some of these AI native companies, the use of Datadog in training, which has been an additional source of revenues.

The question we get, and you address this every earnings call with your comments on large customer.

Yes.

How do you think about the risk that you will have more large customers over time that will say, look, we're willing to take on the pain of running a more complex stack or more DIY stack because the cost savings make it worth it.

Yes. I think when you look -- step back and you look at our gross retention means are you staying on Datadog or not, it tends to be, we said in the upper 90s with larger enterprise being at the high end of that. So we don't retain every customer, but the vast majority of customers have been making a decision to use the Datadog platform. And in fact, over time, as I mentioned, the trend has been that it is economic, priority-oriented to instead of building an sorely brought from yourself to put your scarce R&D dollars into your product and your business. So that's been the weight of it. But it isn't the case all the time, and it also isn't 100% or 0%. Many customers are doing a combination. And what we said all along, which is from our largest customer, is that we have commitment contracts and that customer is growing rapidly and spending above the commitment. But we know that in certain customers, there'll be sort of a trade-off between and what we said in our last earnings call was that, that customer renewed with us, staying with Datadog, extending the contract and the usage there, has moved around relative to what they're doing. Sometimes it makes sense to hook up a database that doesn't have to be or a metric store doesn't have to be real-time with Datadog and put the other work that has to be real-time. So we had some volatility and what we decided to do, given the investor focus on this is to derisk our guidance by putting in our guidance only the commitment it can't go below the commitment. But we've always said that's a fringe case. For the most part, our business is not about outsourcing the hyperscalers as durability. We're not a concentrated company. So and awaited, and you see that in our gross retention, the vast, vast majority of our customer base given the gross retentions we're talking about have stayed with Datadog and the fact that the net retention has been increasing means that they've been putting more of their workloads on Datadog rather than less.

Let's stay on the vast majority of the customer base in that case. There are some really interesting comparisons that you and I have talked about, about the 2021, 2022, 2023 optimization cycle. And how your customers have actually learned from that, and you as a company have learnt from that, too. So when we have investors say, look, this category by definition, has waves to it of optimization versus growth? Could we be entering a phase of optimization or how do you know when not about to enter a phase of optimization. Tell us a little bit about the forecasting tools that you use and how you think this time is different?

I think things are pretty different than they were when the bubble burst. So first of all, we were growing at 70%. We're growing well, but we're not growing at 70%. There was that 0 interest rate growth at all costs. And we're not in that market. We also had a much higher percentage of these cloud natives than we do now. So that's a bit of a different environment. We always will have a ying and yang of growth and optimization in cloud software. It's really the weighted average of that, that matters. And you can see that in the expanding net retentions that were net-net in a growth environment. I would say, to your point, we also, I think, learned ourselves, first of all, we're much bigger, we're much more diversified in all ways, geographically type of customers. Our highly growing AI native is a very important factor because those are some of the progressive customers, and that's a great sign that they're adopting us, but it's much smaller than that cloud native. And I think our customer base has not forgotten what happened and has, if you look at the net retention, acted more responsibly. We also have built out a number of functions to provide information transparency to our customers, work with them account managers, SKU and non-SKU types of arrangements where we help our clients use it, use the product in the right way. So I think we've gotten a lot better in helping our clients. We also have things with contract extension, volume pricing, a number of things to ameliorate the risk. So I think you're right on, is that the end market has learned. We are much more diversified. And we've learned, I think, how to be better in helping our clients to have long-term growth with us.

One of my favorite examples of this is actually on infinite cardinality.

Good point.

And this could be a much longer discussion, but maybe give us the shorter version of why Infinite cardinality addresses one of the budget pushbacks that you would have gotten from customers?