All right, everybody, let's get this started. My name is Koji Ikeda. I'm one of the software analysts here at Bank of America. Absolutely thrilled. Thank you for joining us for the day two keynote, lunchtime keynote. To have Datadog CFO David Obstler with us, thank you so much for doing this. Appreciate it.
Thanks for having us. We appreciate it.
Thank you. Thank you.
We have a lot of people in this room. Thanks again for joining us. some people that know Datadog very, very well. There's also many in this room that might not. Let's start very high level. What is Datadog? What is the core problem Datadog is trying to solve today?
Yeah. Datadog has an observability and security platform that allows the deployment of modern software applications, mainly in cloud environments, in a safe and effective way. Most of our customers are those that have mission-critical software that's customer facing. Think about video or credit card companies or banks or airlines or hotels. All of them have digital applications that interact with their customer. The Datadog platform is used to monitor and secure the effectiveness and operations of those platforms. Most of this is in real time. Datadog, over the years, has had an expanding platform to cover more of the surface area in examining what's happening in those applications and allowing them to operate in a good way for the customers.
Your results and fundamentals are accelerating. Clearly, good things are happening. Why is observability and security becoming more mission critical as software complexity and AI adoption accelerates?
Yeah. Yeah, definitely. AI adoption is a part of it, but essentially, we are observing the development and the production of modern applications, mainly in the cloud. As they get more and more complex, and as more and more of those applications are moving from legacy technologies to the cloud and being modernized, that is what Datadog does. What is the effect of AI? One of the things is any time there's been new technologies, of which certainly large language models are one, there has been more of an impetus to modernize the tech stack, and therefore, create more applications that are in the cloud. That creates business drivers and has historically created business drivers that have enhanced the Datadog business.
This applies both to non-AI native companies who are modernizing their tech stack in order to have large language models in their applications, as well as a set of infrastructure companies who we call AI natives, who are experiencing a demand cycle and have rapid product releases. They're cloud native. Their whole stack is modernized, and they're using, in a very significant way, Datadog products to help observe the delivery of those products to their customers.
1Q results. Little while ago, but still.
Yeah.
Yeah. It's been a couple weeks.
Fun memories. Fun memories. Yeah.
It's been a couple weeks. I think our started with wow, right? Fantastic results all around. Accelerating growth. Winning new customers you never thought you would have before. Let's tackle the first part about just accelerating growth. Looking over the past six months, what sort of inflections were you seeing from core observability teams out there?
Yeah. This has been going on for, I think we've been accelerating for three or four quarters. We've been communicating this message that we have seen a good buying environment, that the investments we've made in our platform, we can talk about the number of different products, were resonating, so we're getting more platform growth. We also are having a significant demand cycle in AI native companies. This has been building on itself. We've also been investing substantially in our go-to market over the last year in order to deliver this to our customers. This has been building on itself, and essentially in the first quarter, it continued that trend of acceleration in many areas. The non-AI natives, the AI natives, different geographies, enterprise down to SMB. Those were all contributing factors in producing the first quarter.
The seeds of that started three to four quarters ago and have been building on itself.
Let's talk a little bit about the multi-products. You guys give a lot of metrics. Two plus products, I think four, six, eight, 10? I think I got that right?
10, yes.
What are the products that are driving the most multi-product usage? Out of those metrics, the two, four, six, eight, 10, which is the one we should be focusing on?
Essentially, the benefit of Datadog is that you can do all of your observation actions in a single platform. In some ways, it's a single product, the platform. At the epicenter, and this has been going on for some time, you have the, what we used to call the three pillars, which are Infrastructure or metrics, Application Monitoring or traces, and then logs. We've had a very substantial demand cycle from what we call Digital Experience. This is taking how an application interacts with customers from the back end all the way out to the mobile device, et cetera. That's called, that's RUM, and that's Synthetics, et cetera. Those are bigger products for us. Those have been growing very rapidly, and there has been a consolidation away from both point solutions and do it yourself towards our platform.
That has been enhanced by some other products that we've put on our platform, including our Cloud Security products, our Service Management, which is basically interacting with the users to be able to manage cases, et cetera. What we call the products that allow you to A/B test on different applications and determine what's most effective. We've been adding on additional products on top of that, and then we started to add on what we call AI for Datadog and Datadog for AI. Datadog for AI is most of our business is created because there are many things that impact an application. What I just mentioned, plus databases, plus network, and now you have LLMs and other things.
We've been working to monitor those, and they are being adopted as well as AI for Datadog, which is how can the platform get smarter and service the customers, and these are things like our Bits SRE. All of those have been developed and are starting to get traction, which is enhancing what had happened over the last three or four years in the core pillars.
Infrastructure Monitoring, APM logs are all big ARR businesses.
They're all over $1 billion, sure.
Remind me, out of the other products. What sort of metrics you gave on scale? I think there's a bunch that are $10+ million . Is there the potential for some of those to reach $100 million, $200 million, $500 million?
We've been doing this. We've been giving a lot of metrics. Our RUM and Synthetics have passed through that, we gave metrics on that. We gave metrics, I think, that Security passed $100 million. We've been doing is over time, as we reach these metrics, generally around 50 and 100, we have been giving those metrics. We had the passing of 100, which was the ones I mentioned, Security. We had a number of other products that were reaching the 50, which were things like database and network. I'm not sure I have all of this exactly right. We gave metrics, we have a lot of other products that are passing through 10 in multiples.
Yes, we have a lot of products that have been scaling this, and what we said was there's lots of opportunities, and what we're going to do is we're going to give, as we reach these milestones, we're going to give those metrics so that everyone can follow along. Things that show a lot of promise are like Bits AI SRE. These things like the Product Analytics we talked about, which is really about how an application is constructed and interacts with clients. These are all smaller products, but the TAM there and other point solutions are much larger than we're at today, and so we're optimistic we can scale those different milestones as well.
Yep. In the oneQ results, you guys won a lot of, I mean, you guys have been winning big deals for a while now. What's going on there with the big enterprises? Is it the go-to-market motion getting better in the enterprise need something more like Datadog? Maybe it's a confluence of both. Help me understand the big deal activity.
Yeah, it's definitely a confluence of both. You have in a customer base that has been around for a long time, therefore, they're not cloud natives, they didn't just get birthed. They have legacy technologies. They have a long way to go. There's somewhat 25%-30% of workloads in the cloud right now and modernized. They are continuing to find use cases and modernizing. Datadog's platform is getting bigger and better, we're consolidating market share onto our platform, we're getting better about delivering the enterprise service model, whether that be channel partners, customer service, technical help, the whole ecosystem to be able to deliver. All of that has been what we've been investing behind for some time that is bearing fruits. That's resulted in some large lands. We still are largely a land and expand, some very substantial expansions.
When we've given our customer examples, if anybody is curious to go back and look at the scripts, what you'll see is a great combination of what the economy is. You'll see insurance companies, financial service companies, tractor companies, all sorts of different car companies, airlines. You'll see how this is evolving in the spread of the business towards cloud natives and certainly AI natives, which we'll talk about, but also the cloud nativity within very large traditional enterprises.
Is there any limitation to the type of company or size of company that might not look at Datadog anymore, or is Datadog available to all types of companies?
Well, there are some companies that have in their past want to do this themselves. They're very few. I mean, maybe I think Google's a good example of trying to do things themselves. That's not really core to our business, although, and we'll talk about it, there's examples where we've gotten those types of businesses, and we'll talk about that. I think the other thing is, if we generally are delivering our product through the cloud, so if you require an on-premise solution for regulatory or other reasons, that has by choice not been where we've concentrated our R&D. We are developing more of those products, so you can see companies which, because of either their practices or their regulation, cannot have data leave their premise. That would be a company that is not core to Datadog's end market.
Yep.
Yep.
Last quarter, you guys talked about winning some AI labs within some large tech companies. Let's talk about that. What happened there? Why did they come to you? What were they looking for how are you guys helping solve that problem?
Yeah. As background, I think there's a lot of information we've given, a lot of information about how pervasive the AI business has been, this includes some of the foundation model companies in database, companies that are vertical. Already Datadog has been used pretty pervasively in the monitoring side. Production work environments, inference production, I think we said over 650, we gave a lot of statistics on spending over $10 million and 10+ products. What we added to that is that two larger companies, a hyperscaler and another very large tech company that have model creation within their businesses, foundational models, had used Datadog for training as well. Most of the time, our end market has been for production workloads.
As a couple things have happened, as there is a boundary between training research or training and when it has to go into production, that we've now been able to have some customers buy from us. In this case, they were some large customers, a hyperscaler, who traditionally does more things on their own but use Datadog. What we found is this is an example of the fact that they may not be using Datadog pervasively, but there are use cases where they're going to be using Datadog, and that's a great voice of confidence that these companies whose whole business is this are using Datadog. It's sort of like a seal of approval.
You mentioned earlier in our conversation that Datadog does well with inference. Now you're saying training too.
We're saying it maybe. Maybe we're saying we have, but we haven't said. We generally don't overpromise. When we have a certain amount of training that is spread out, we'll tell you. Right now, it's more of a sort of centralized.
Yeah.
It's good, but we have said most of our revenues we expect to be from production and inference. We'll see what happens. We'll bring everyone along.
But does it, as we think about AI in the future and more enterprises, organizations building their own things, large small language models, for all. How does Datadog think about that opportunity and maybe even going after it a little bit?
We'll prepare ourselves for that opportunity. I think that for the most part, when you think about production, so I think we're always, even if we're in training, we'll always be somewhat proximate to production. There could be market extensions, but essentially you use Datadog when it can't go down. If you're training, yeah, if you're training, and by definition the sandbox you're not putting into production. You have different impetus. Maybe that'll happen, and we certainly are preparing our products. They're the same products. We certainly will have the products, and we certainly are in touch with customers, and we certainly will push that if it makes sense for customers. We just don't know the answer whether that's going to be a core market or a specialist market for us.
When I think about observability in the most simplistic way. It's data ingestion and analysis. I know it's much more complicated than that, and the architecture is very important in the way to address that.
Yeah. Mm-hmm.
Maybe help explain to those in the room, again, less familiar with Datadog. What is it specifically about the technology architecture that takes this what could be a simple concept into a very complicated something that you have to invest in and difficult to replicate?
Yeah. Before we get to the architecture, one of the things is data. Datadog. Okay. It isn't true. There's not one data. We have 1,000+ integrations. When you think about the operation of a cloud application in real time, CPUs, GPUs, databases, lines of code, network, all sorts of things. One of the things that, and this gets to the architecture, that Datadog did very early is they developed a common architecture to take all that data from all the different parts that could affect and organize it and put it in one place and make it transparent so you can see.
That is very hard to do, and that's the reason why point solutions don't really make sense because none of the customers are saying, "I want to see what happened to that line of code." They say, "I want to see what happened to the functioning of the application." So you have to see all of it. That's one thing. The architecture is that Datadog organized that data, knitted it together, provided user interfaces and other ways to see it in real time, correlated. This is being enhanced by AI now, which is using large language models to do diagnosis and maybe even one day self-remediate, and produce it all knitted together. That's hard to do because there are a lot of factors.
Many other companies have tried to do this piecemeal, and it's all gone back to that integrated data model, putting the analytics on top of it, having a simple but not simplistic, meaning everybody can use it. We don't charge by seat. We benefit when everybody goes into this utility and uses it, and that helps us. All of that architecture has been an architecture which provides the most value to our customer base in analyzing. It all has to be real time because this isn't like, okay, I produced some marketing collateral. There's an error in it. I can fix it. This is your whole business on the front end. That's what's created it. Then all the different pieces, adding on all these different pieces and knitting together, have been complex.
Right now we have a competitive advantage in that we have a very large platform. Its scale can handle anybody's scale. It basically organizes all this for everybody, and it allows you to have a significant platform investment to amortize application investment on top of it, which is a big competitive advantage.
I wanted to dig in on the Datadog AI strategy also the data for your AI strategy. Let's hit the data first. What is so special about the data that Datadog has? How long do you think it would be, even if it's feasible for a competitor to amass that type of data to be competitive?
Yeah, it wouldn't be feasible at a price point that would be competitive. In other words, large language models are by definition large. What they're doing is they're looking at lots of data. Our strategy in the model side is to offer that, but also then have a set of models that are very specific to observability and security use cases. Then to make them because of all the data we have, and then both have a higher functioning model and a cheaper model. If it's not a generalist model, you don't have to pay the cost of all that stuff that doesn't apply to your use case. That's what we're doing in the model side. There's a whole bunch of different things. I'm speaking now about AI for Datadog. We'll get to Datadog for AI, which is all the DNA that goes in.
There's also the ability to connect to all the software creation on the agent side, whether it be an agent or machine or person, we don't care. Whatever's creating and getting towards an application to see what's going on there and then correlate that with everything else that's affecting the application. All of those are things that we're putting in our platform. We're having our user conference in, is it a week?
Next week. There'll be a lot of product releases. Our investor day also had a very strong articulation of this. What that will enable us to do is, and is already, is basically making the platform smarter, being able to diagnose very quickly what's going on, being able to make recommendations on what to do about it, and in some cases to actually implement those recommendations. That's the vision. That's what the whole Service Management vision is in the model. That's like the investment in AI for Datadog to make the platform enabled to see all those things and use large language models, whether they be the third party or our own to be smart.
Let's talk about your AI products. The Bits AI products. Actually I don't even know how many you have. What do you have and how should we think about the AI products?
Heading first into monitoring data flows. We have LLM Observability. That is the functioning of an LLM model. Think of it as you have databases, you have lines of code. That is where you have LLMs in a production model application. You're using signals to understand is that affecting the performance of the application. I think we've said that that is germane to having LLMs in production. We've had significant growth there, still early days. We're getting revenues from that. We have GPU, which is essentially like an infrastructure product, but instead of CPU, GPU, where you're seeing how the application might or the model might interact with GPUs in delivering. That's similar to our other products where you have to see how the servers or the GPUs are doing.
That is sort of examples of Datadog Monitoring things that affect, so that's Datadog for AI. You have AI for Datadog, and those products are the Bits products. Bits is sort of a general name. Bits is our mascot, our dog, that's why we are using Bits. It's very cute. For different end markets, SRE would be the systems reliability engineers, et cetera. That product is out there. There are 2,000 customers using that. I think we have 100,000 or more investigations. We also have products in for development and for security that we're rolling out in the Bits suite. We also have, as I mentioned, the ability to connect to the code generation through our MCP Server, when that will enable that information to get into Datadog and monitor that.
We also have a bunch of other products, Cloud Cost Management, et cetera, that is being able to monitor on sort of the cost and management side, how much you're spending on tokens, who's using it. We have Cloud Cost Management, and now we're extending that into agentic monitoring. That was a long talk. Apologize for that. There's a lot of names, but those are some of the product lines that are being put out to market in those areas.
Yeah. I want to talk about platform consolidation. Good driver of growth for you guys. What is driving platform consolidation today with the large enterprises? Does that theme of what they're doing today for consolidation just continue into the future? Does consolidation maybe change?
Yeah. You might wonder why didn't all this happen already? Why didn't everyone just buy everything in Datadog to begin with, and it's already pre-consolidated? The reason is, one, Datadog didn't have these products 10 years ago. There are other products out there, and there are contracts that customers have with those products. We're in an environment that it's happening over time. Why is it happening at all? It has to do with that if you are a practitioner and you need to operate in real time, you do not want to be context shifting. You don't want to be going into a lot of different data sources. It's slower. It's actually more costly. As we've developed these products and clients have looked at the utility and they've gotten off of contracts, this has been going on for some time.
I think as the product suite has been getting better and better and better, the components all getting to product parity and beyond, it's been accelerating. Yeah, I think we're very early on in this. There's proliferation. There's a lot of observability point solutions out there. Like I said, all of the decision making is leading towards consolidation in the single platform. We think we're pretty early on in that trend.
Do your buyers still think about, and we'll talk about in the three core applications. Infra, APM, log analytics. Do they still think about that as three separate products, or are customers beginning to come to you and saying, "Please solve this"?
No. For a long time, we've been selling as credit. You buy $2 billion of Datadog, you go in and you use all this. No, they've never thought of it as different products. We're doing that in order to provide transparency to you all. It's one product. They don't care what they're called. They only care about doing their job, site reliability engineering. This happened a long time ago. That's what made Datadog, and they think of it as the platform.
I want to touch a little bit about security. $100+ million business. It's been out there for two plus, three, four years? Three, four, couple years now. Maybe for those that are a little bit unfamiliar with security, tell us about your security journey, what you're doing on the product side. Maybe more importantly, what are you doing on the go-to-market side with security?
That's a very good thing. First of all, you have to say what security. Okay, these are the things we're not in. Endpoint security, there's some really good companies. We're not in network security. We're not in email security. What we're in is Cloud Security. Cloud Security has three components. It has how the cloud's working, it's called Posture Management, and set up to secure digital applications. Two is Cloud SIEM. How are you using logs and other information to investigate what's happening? Three is Code Security. How are you engineering security into the code? Not on premise, not legacy, but for cloud applications, we've built this suite of the three products. I would say the one that we probably made the most progress on is Cloud SIEM.
Some of the reasons are it's off of our logs business, and we have an over $1 billion log business. We have a great largest observability logs, and the end market has dynamics where we've been an innovator in it. We've had two major strategies. One is attach the whole suite to cloud-native emerging companies, and two is attach Cloud SIEM to more traditional companies where we have a strong observability logs. That's been the strategy, and it's working. That's the product strategy. In terms of go to market, particularly for enterprises, it's a different go to market in that, one, you have the influence of a buyer that is not a traditional buyer, the CISO. And even though they're getting closer together, that is a different buyer than DevOps.
For some reason, I don't know, I'm too young, but for some reason, there was a bottoms-up distribution that facilitated direct in DevOps. Security has been highly centralized, and the CISO has the grip on it. It's a different buyer who buys through channels. What we've been doing is we've been working on selling our direct to our champions, but also investing in product experts or specialty salespeople who are going to try to penetrate CISOs and others and channels, where we have a channel program like the other security companies. It's still early. It wasn't our DNA, but we're investing behind it as part of the overall security investment.
One thing that we've heard over and over from customers and partners is. I'm the observability buyer. That guy's the security buyer. Is that how it's always going to be in your view, or do you think one day that might converge?
We think the whole strategy is that it makes no sense. There's certainly territorialism. Essentially, if I were up here, you would say it's idiotic not to design security into applications and to have all this come through. DevSecOps, we have seen signs, and we believe it will come closer and closer together. A thing that we think will accelerate that is agentic coding because that's going to speed up everything, and it's going to require everyone to work together. Even though you're right, there are these separate buying groups. We're seeing signs of them coming together, and we believe in the future, since we believe that businesses are in the end logical, that they will come together. If we can use the gains in technology from agentic content of detection of penetration remediation, that will most likely help us in our attempt to scale security.
In that vein, when you say agentic coding, it's getting pushed out.
It's getting pressed, yeah.
From the software development side, and security has to deal with it. Does that feel like you're beginning to pull in some of the security budget because of where that velocity of coding is coming?
In cloud-progressive companies, we have been for some time, meaning we have clients where it's like I said, it's all knitted together. You have a whole big world out there, it's a process. I don't know if I can say that in the last six months, we would see a sea change. We think it's an evolution, and we believe it's going to happen. It's going to happen over time.
Yeah. You guys are highly successful with cloud-native companies and cloud-native strategies. At your investor day, you talked about Cloud Prem, a product that you have. Can you tell us a little bit about Cloud Prem? What's the strategy there, and how are you leaning into go to market with that product?
Essentially, we're leaning into it and go to market in the same way, meaning that if a client would find utility or a cost benefit from having data kept in there on their own servers, we want to have a product. So far it's been that we have the analytics, or we have the log, and you keep the data. We also have products that allow the data to get in in an efficient way, Observability Pipelines. That's what we're doing. Now, we're investing behind it. We have customers that want it, and we've been successful. It still hasn't been the vast majority of the way our customers want to buy. What we're working towards is indifference, meaning that if you want to keep the data, great. If you want to give us the data, great.
If you want to have all the functionality go over to your side. That's taking time to get complete product parity where we have exact functionality. That's taking time, and that's what Cloud Prem means. We're working on that. That's mainly for the first use case logs.
I got you. Wanted to ask you a question on durability of growth. You guys have great growth trends, and so how do we think about the drivers of the durability of growth, whether that's cloud migration, platform consolidation, and AI? What signals are you guys seeing that's giving you the confidence that growth continues in the fashion it has?
Well, the durability depends on what your timeframe is, okay? We always have believed, and we're seeing that this is a very long-time investment cycle because such a high percentage of applications and infrastructure are still in legacy technologies. We've always said that if you want to look at this is a secular trend, and it can be cyclical, but it's also secular, and it's got a lot. You have 70%+ of workloads that aren't in the cloud. We believe this has very long legs. In terms of, is it going to be a straight line, meaning on every month, is it going to have the exact same thing? We don't know. It's likely, if proven, you're going to have periods of investment. You may have periods of optimization. It's cloud software. It's consumption cloud software.
We can't predict exactly what the line's going to be in every moment, but we have confidence that it's very durable, and it's very long because we're attached to huge market drivers. We also, if history repeats itself, we found that new technologies, of which AI is happening, is a huge accelerant of this conversion. Again, it's still too early, but if history repeats itself, that is likely to inflect the line upward faster to get to the same place 25, 50 years out.
Yep. That makes sense. I wanted to ask you a question on how Datadog thinks about product velocity and R&D investment. Frankly, your guys' own use of. AI within the organization. You mentioned, I think we talked about it yesterday, you got about 4,000 developers within the organization. How do you think a bout leveraging AI and investment?
Yeah. To step back, we've always been a product-led company. We've been out investing. We've been spending over $1 billion in R&D. That's had tremendous benefit, meaning it's allowed us to invest in the platform and scale and the functionality. We also have our company that has no shortage of things, of functionality that we want to roll out. That's been epicenter to Datadog. Out-invest everyone in R&D, look at the pipeline of what we want to put in the market, at a profitable price, and creating value for clients, and just look at your R&D resources against that. That's been what Datadog has done. What about coding agents and agentic coding? That is essentially another piece of raw material that we are using to try to accelerate the launch of features, where we're not, what do you call it? Token maxing, what's it called?
Agent maxing, what's the token maxing?
Token maxing.
We're not token maxers. We've never paid anybody, rewarded anybody on the number of lines. We basically think about products, so it's all product. All those software developers we call product people. Essentially to the extent we can become more efficient in speeding up feature release, it's good for us. We're finding that. We're basically experimenting around the interplay within that envelope on R&D between tokens and people. We have a lot of experiments. We have places where we have, keep the people, let's see how fast you are with less tokens. No. Constrain the people, let's see how fast you get. We're pretty nerdy engineering wonky, so we have all this stuff going on, and we're trying to see what's going to happen. We believe that the componentry of R&D will shift somewhat.
We don't know exactly, to tokens from people, but we don't know the exact amount.
Have you been able to use AI internally, with all that development that you have to help out with your own homegrown solutions in sales and marketing and G&A at all?
AI, there's three or four major use cases. One, in the product itself. We talked about t he models in the product. Those would be components of the product. Two is accelerate product release through software coding tools. Three would be use AI for productivity of the employees. Four would be what you're talking about. Are we able to use large language models or AI to improve the productivity and the effectiveness of the different functions? Yes, there's many, many examples. We're using AI to automate the creation of deals and the processing of deals, to enable our salespeople and our market people to understand propensity to buy and direct our salespeople in places that have a higher chance of success. We have all of those. I'm not going to list all the names, but there's lots of them like that.
How do we enable salespeople in product training and things like that? How do we speed up all of the back office functions, whether it be legal, accounting, cash cycle, marketing collateral, all of that. Yes, we have experiments or projects going on in many areas to try to do that. What we're really thinking about mainly is how can we convert repetitive administrative tasks towards more insights so that we can act faster and inflect the business. We have all that going on, too.
Wow.
Yeah. Again, it's not token. Basically, you've got to produce something from this. We have a lot of opportunity.
Remind us, what is the investment strategy in sales and marketing for you? How do we think about that?
Yeah. The investment strategy in sales and marketing is bottoms up to be able to cover the high potential customers around the world. It has to do with investment in enterprise all the way up, government and enterprise, the biggest entities all the way down to startups to be able to cover them comprehensively and to be able to get lead gens from them, et cetera. It has to do with geographic expansion. Like many U.S. companies, we were most developed and followed where the investment in cloud software was most intense was the U.S. We're finding many areas with great payback, Brazil, Korea, India, et cetera, where we did not have on the ground presence. We're developing packages of salespeople, sales engineers, marketing dollars, maybe even data centers to be able to do that. That's sort of where it is.
The ways you reach customers direct and then also channel, we're expanding. It's a bottoms-up business plan aimed at that micro level and how we can reach the broadest swath of customers around the world.
I got you.
Yeah.
I know we're running up on time here. M&A strategy.
Uh-huh. Yeah.
You guys are not afraid to go out there and acquire things. How are you thinking about your M&A strategy? Any changes in w hat you've done in the past, and how do we think about that going forward?
Yeah. Think we've basically, to date, not acquired streams of revenue, but acquiring product capabilities and the people, and we continue to do that. When you think about building product, we often build product, and then we can enhance that through acquiring a product capability. That's the epicenter of what we do, and if there's opportunities, we'll continue to do that. In addition, I think we're open to something that's bigger, where we do acquire some customers as well, but that'll really be dependent on can we integrate it in and can we accelerate? Does that help us? We're pretty principled on that. One of the things we really insist on is that the R&D teams and the product teams stay at Datadog. That means for certain companies, sell, move. We don't want that.
We make it in the incentive structure, so we only acquire companies where they want to stay. That's sort of what we've been doing. It's been very successful. We've created some very big businesses through that with not that big acquisitions, and I think that's the core, with the reservation that we could look bigger to the extent it fits in in our disciplined way.
I gotcha.
Yeah.
Last question for you, David. Thanks so much for doing this.
Thank you.
Yeah. You got DASH next week.
We got DASH next week.
We'll try and get a little bit out of you of what, as investors in the room and myself, as I look at the schedule, is there anything I should be focusing on just to make sure I don't miss anything big coming out next week?
Well, as background, DASH is a place where we make a lot of product releases. We do product strategy. It's for users. I think you're going to get a pretty good roadmap. If you look at who's speaking and everything, I think you'll see it's going to have, no surprise, a lot of AI content. Some of the things that we're discussing here, the AI for Datadog for AI, coding, all that, I think will be fleshed out a bit more at DASH next week.
Got it.
Thank you.
We're all out of time.
Yep.
Thank you so much, David.
Thanks a lot.
It was fun. Thank you.
Thanks. Thank you. Good interview.