Everyone, welcome to the Datadog session where we are debating strong suits versus weak suits in pop culture and trivia.
Trivia.
Hey, it's a real pleasure to have David, CFO of Datadog, on stage with me. David.
Thanks for having me.
Really appreciate you making the time to be with us this morning. We started having a couple of really interesting conversations over dinner last night that I'd love to talk through a little bit with a broader audience. The first one is this idea of applying proprietary data to an SLM or an LLM.
Yeah.
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.
Yeah, great question. We used to call it ML or correlation. 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.
AI and models have provided a very unique opportunity. We recently made an acquisition of a company called Adaptive ML, which is a specialist in reinforcement learning around areas in IT management and observability that we cover.
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, observability. We're investing behind that.
We talked about that last night, both in terms of people, the GPUs, the inference, et cetera, and that data is not public. That data Datadog has because of our position and size and observability, and we think that will deliver a lot of value to clients and also be a competitive advantage in the evolution of the platform.
Let's stay 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 engineering.
Yeah.
You have a little bit of that with the Bits product.
Maybe just paint the vision for us. When you talk to the founding team, where could a technology like Toto go over time?
Yeah. 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. 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, will be a combination of the evolution of the platform.
Our Bits 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 can auto-remediate. For this type, there'll be a suggestion, and then someone will have to press yes.
That has tremendous ramifications, both in terms of the speed and also the efficiency in human capital in this endeavor, that is the vision of our founder, Oli, and where we are investing behind.
The other interesting thread to pull on here is this idea of an inference economy.
Yep.
What I mean by that is we are 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 are 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 timeframe 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 zero inference to 1%, 3% of the OpEx budget going to inference? What are the implications for Datadog's opportunity at that customer?
Definitely. Datadog is in the business of monitoring whatever could affect the functionality of an application, and the more complex it goes, there is more in it, the more opportunity to get revenue. I think we talked about last night that early on, the first wave was essentially calling out to the model companies through APIs.
The first way to monetize this is Datadog for AI, meaning it is just like Datadog's monitoring code, Datadog's monitoring CPUs, Datadog's monitoring databases. This is something else that we have begun to monetize through our Agent Observability. Although early on, we are seeing thousands of customers use this, and we are starting to get the revenue streams from it.
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, that creates a whole another set of things to monitor that we are 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 in observability, more to monitor, and more velocity in application creation, which should all benefit Datadog.
There is a sovereign thread in here around. We have had actually a number of companies. We think about Satya Nadella talking about frontier ecosystems.
Yeah
CoreWeave talking about more customers wanting to run their entire.
Yeah
training, post-training inference stacks and how.
even at Goldman, we've been talking just this morning about
Yeah
proprietary data at Goldman that can be
leveraged with the model. In 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
Right
outbound APIs-
versus, "Actually, we want more of that stack to be in-house, and we're going to run more proprietary processes internally.
We're starting to see that in the metric I mentioned on usage, but also at Datadog. We talked earlier about our models. We early on used the outside models and the foundational models, and we put that in the platform. 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 most perform it, 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 cost towards our proprietary models. Early on, it takes investment. It takes lift to do that. 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's doing in its own research lab.
Agree with you. Yeah.
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 in, we will see reported ARR numbers from Frontier Labs
and we will extrapolate that, and we will say, "Well, Datadog, depending on what the contract looks like, should have a
Right
really interesting correlation with what is reported publicly.
Now, in reality, it is a little bit more complicated than that.
I know you can't speak on any specific 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. Their whole business is delivering this functionality and the models.
What we're seeing is, one, we're seeing strong growth in workloads. Two, we're seeing a pattern of outsourcing to Datadog. A number of these companies started out doing There's always a trade-off between do it yourself or using a Datadog.
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 observability. Use Datadog. We're seeing a movement from open source or initial efforts or cobbling it together towards Datadog.
We're seeing, because they're newer customers, a use of many of our products, the platform. We're seeing the average use of products to be at the higher end. The main thing we're seeing is accelerated growth.
They're very similar to other modern software companies that are experiencing demand cycle. Most of this is in production, inference in production. What we're also seeing, which we talked about on our earnings call, which really struck us, we originally said, "Well, Datadog's really the production company, inference in production.
We're really not going to see a lot of demand on training." 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. That was a surprise to us.
We thought back and we said, "You know what's happening? The speed of this is such that the pre-production or late training into production is starting to merge," which created this revenue stream. We don't believe we know the 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-
Yeah
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-
Yeah
a more complex stack or more DIY stack because the cost savings make it worth it.
Yeah. I think when you step back and you look at our gross retention, meaning 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. We don't retain every customer, but the vast majority of customers have been making the decision to use the Datadog platform.
In fact, over time, as I mentioned, the trend has been that it is economic priority-oriented to, instead of building an observability platform 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 that doesn't have to be real time with Datadog and put the other work that has to be real time.
We had some volatility, and what we decided to do, given the investor focus on this, is to de-risk 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' observability.
We're not a concentrated company. So in a weighted, and you see that in our gross retention, the 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.
Yeah
of the customer base in that case. There are some really interesting
Yeah
comparisons that you and I have talked about the 2021, 2022, 2023 optimization cycle.
Yeah.
How your customers have actually learned from that, and you as a company have learned from that too.
Definitely.
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 we're not about to enter a phase of optimization? Tell us a little bit about the forecasting tools that you use
Definitely
and how you think this time is different.
I think things are pretty different than they were when the bubble burst. First of all, we were growing at 70%. We're growing well, but we're not growing at 70. There was that zero 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 yin 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 natives 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. 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. It's 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 the announcement on Infinite Cardinality Metrics.
Good point.
This could be a much longer discussion, but-
Right
maybe give us the shorter version of why Infinite Cardinality
Yeah
addresses one of the budget pushbacks that you would have gotten from customers.
Definitely. I will focus on Infinite Cardinality, but then I will also mention it is one of a portfolio of technology innovations where we have met the customer where they are. Flex Logs, Frozen Logs, Metrics without Limits.
I have another five questions on this.
Yeah. Five. Yes. It is a really good question. Basically, what we have been, I think, much smarter at is understanding how the client is using the product. Cardinality is the sampling and the use of the data, and what we came to understand is in some examples of how a customer is using it is much better to, I would say, curate or figure out how all of the metrics do not flow in and get processed, but they get sorted before.
I am using very simple language. That is good for the customer because they get more value in their metrics. That is also good for us because it does not help us to have metrics or even logs that flow in and are costly to us.
There are many, many examples, I think it is a very good point, of how we have been evolving the platform to meet the client at the value point. What we have been doing is proactively converting those customers that can benefit from Infinite Cardinality Metrics to our SKU, which has essentially been retentive and also margin-enhancing for us, given the weight on our platform of large cardinality of our metrics. Yeah.
The other pricing announcement, it is not really a pricing announcement, but I think we could frame it as driving value to customers.
Federated Logs.
Federated logs, yeah.
This is really interesting.
Yeah
It actually sounds like you can bring a more heterogeneous architecture into how customers organize their logs, for example, with ClickHouse.
Yep.
Talk about how Federated Logs work.
Yeah. I will talk about Federated Logs. It is in a group of innovations where we used to have all the data have to flow into Datadog to be used, but we know that is not efficient. Federated Logs, Data Observability Pipelines, Bring Your Own Cloud.
What this means in the case of Federated Logs is that we know that not all logs have to flow into Datadog to enhance the platform, that we can link into and logs can be stored, in this case, it was ClickHouse and Databricks. What we are doing is we understand we want to be able to have more data available in the platform without having to port all of your data into Datadog.
That we have proven, and we have gotten feedback from clients, enhances the value of the platform, eliminating one of the objections is, "I have to bring all my data into Datadog even though I am storing it elsewhere." This is a better way to use Datadog, to have what flows in to be the most important, but to leave what does not need to flow in there.
Essentially what that is doing is that is exposing our platform to more data that is not in Datadog, enhancing the value of the platform. You could say that is also what we are doing with Bring Your Own Cloud.
There are many examples where it might make sense for a client, either because of the volume, regulatory reasons, or otherwise, to leave the logs or the information on the client side, but use the Datadog analytics.
The Federated Logs are a good example of one of a number where we are opening up the Datadog platform, and we believe that will be enhancing value to clients, improving the cost structure, and opening us up to business that we would not have had otherwise.
This word that you are using on opening, I think-
Yeah
is important because one could argue, okay, if I structure my logs differently
Yeah
less budget goes to Datadog because now
Right
part of my observability budget on the log side is going to ClickHouse and Databricks.
Right.
Talk about the point on, well, actually that trade-off is worth it.
Yeah. We've analyzed that. We actually thought, okay, we want the logs that need to be accessed there to be in Datadog, but we want the platform to be able to use as a central point. The more we have our users centralized on Datadog, we've proven that we get higher average revenue per customer.
To close Datadog off and force them to go to another platform is not as optimal as staying in Datadog all day long and using the data efficiently. We actually, getting back to how we've evolved, we actually go to clients and we say, "You know, you set this wrong.
You're flowing too much data into Datadog that you don't need for this purpose." It creates a much better long-term value of customer, and also, in many cases, is margin-enhancing for us because to store all that data in Datadog that is never used also is a burden on our system.
Every two to three quarters, we all have to ask you about some new competitor in this space.
Sure.
whether it's an AI native or
Yeah.
Tell us how you would respond to the premise that this time is different, meaning, well, because there's an architectural shift happening because AI involves different scalability of data, therefore the observability companies founded 5, 10, 15 years ago are not the same ones that will benefit from a true AI native observability stack 3, 5, 10 years from now.
Yeah. A couple different questions. I am 8 years into Datadog. We have been public for 6-plus years. There are many, many companies outside observability that have tried to do this, and there is one that has been speaking about it now. It is easier said than done. Very few have been able to, or none, I would say, have been able to organically grow an integrated platform where you can see everything that is going on.
A lot of the attempts have been through acquisition platforms that are not integrated together. Essentially, there have been a lot of companies that have tried to do this. They have not succeeded. This is really up to us. The question is, there are point solutions that are out there. Generally, there has been a preference for a single pane of glass and everything knitted together. That is a big lift.
It is very important for Datadog to not rest on that, but to basically out-innovate all of these emerging companies by modernizing our own platform. This is AI for Datadog and Datadog for AI. In this case, I think we are essentially investing behind making sure that we are the leading innovator here so that the balance of trade between modern point solutions and the platform of Datadog is weighted in our favor, and that is why we are investing significantly.
I think it is a very big competitive advantage, Datadog, and if Oli were sitting here, he would say the thing that investors should think about is Datadog has consistently invested 30% of its revenues in R&D. That is over $1 billion. That is out-investing everybody else in the market. Why that is important is exactly your point.
We have to get there first and more scalably and more knitted together in the modern platform than the point solutions that are emerging. I think we have been pretty successful in that, but we are not resting our laurels. We have to continue doing that.
I would like to spend a couple of minutes on a secular trend that has been core to Datadog since day one.
which is the move to cloud.
Yep.
There are two things happening that we've picked up in parallel. One is you're investing more on enterprise sales.
And two is we have a number of enterprises telling us that migrating to cloud is more of a strategic imperative today
than it was two years ago or even five years ago.
Yeah.
Talk to us about how those two trends intersect. Is the share of market and observability that is going towards cloud taking another step function up right at the same time as you are doubling down on the go to market?
Definitely. I think that is very true. Despite the fact that cloud has been around, one, there is so much legacy infrastructure. The research companies say that somewhere upper 20s, 30% of applications are in the cloud. And there are tons of enterprises that, you would be shocked, are so immature, meaning they really have not even started their first material projects.
But the fact that anytime there has been change of technology, of which AI is a major one, the impetus or the urgency of both modernizing applications and putting them in the cloud is enhanced. And we are seeing that.
That is some of the reasons why we are seeing the non-AI business accelerate. At the same time, it is not going to happen by itself. Our bottoms-up selling that is very effective has to increasingly be complemented by top-down and between that.
I think it has been really important for us over the years to build out our enterprise sales team in order to catch that wave. And some of the good examples of that are we have a key accounts group. We used to, I would say, bank on the fact that our sales cycles were going to be discreet, our commission plans were done that way.
But I think what we have learned is some of these enterprises, this is a very long sales cycle, and you cannot have an enterprise salesperson covering 10 accounts. They are covering one or two accounts where the IT budget and conversion to cloud can be in the tens of millions. And we stick this person on that account, and they are going after all the projects there. And this is the evolution. We did not used to be like that.
I think in many, many ways, whether it be key accounts, named and major accounts, the partners, our investment in channels, our investment in data centers, all of this is aimed at exactly what you are saying, is to follow that evolution of the enterprises that are still very early in their migration and be there at the time they do their migration projects.
And when you look, if you go back through our earnings and you look at what we are announcing and you see the industries, banking, insurance, automotive, manufacturing, airlines, you see evidence of this happening, and it would not be possible if you were not building out this enterprise sales team aggressively on a global basis at the same time this transformation is happening.
Innovation and R&D has been a consistent theme over the last 30 minutes.
Yeah.
Give us a couple of examples. When you reflect on how your team operates, how you see the R&D team
operating within Datadog, give us a couple of examples on how things have changed over the last year.
Yeah. Well, one thing I think is very important to think about is the value of the platform. The value of the platform in doing this has meant that it has been very efficient for us to build functionality on top. One thing I think as the platforms got. We talked about a lot of the innovations that have happened in the platform.
That's accelerated, I think, the new product and has enabled us in a very time-oriented way to get innovation. What's happening now is a very important innovation. We are working on the balance between AI and coding tools and human capital.
I think in our history, we've invested very rapidly in human capital, and we believe we'll continue to have to do that. But we are starting to see signs of real efficiency in terms of using coding agents to improve the velocity.
It is still early. We have A/B teams, where we have a team that has a lot of people and less access to coding, and we have teams that are access to more of the coding tools. We are experimenting with what the A/B testing is. I think that is a very important evolution. Another important evolution is the research lab.
When you think about what we talked about first, we are investing significantly behind our own models and the proprietary nature of that. That is starting to result in the weight of the R&D budget.
I think there is still a lot of consistency in that we still have our eyes firmly on the customer, firmly on what are the product innovations they demand, and we are still very commercial people in that we have the feedback loop always putting that with the revenues and the SKUs we create.
I think that is some of the evolution of the R&D at Datadog.
I think that is an excellent place to leave things. Please join me in thanking David for his time. David, thank you.
Thank you very much, everybody. Thank you. Thanks. Thank you.