Datadog at KeyBanc Forum: Strategic Expansion and AI Integration

On Tuesday, 12 August 2025, Datadog (NASDAQ:DDOG) presented at the KeyBanc Capital Markets Technology Leadership Forum, highlighting its robust second-quarter performance and strategic investments in cloud architecture and AI. While the company reported strong financial results, it also emphasized challenges in enterprise penetration and the need for disciplined growth.

Key Takeaways

• Datadog’s 2Q results were bolstered by a 10% contribution from AI-related activities.
• The company is investing heavily in AI and security, focusing on Bits AI and Cloud SIEM.
• Expansion into the enterprise market remains a priority, despite current low penetration.
• M&A activities are centered around enhancing product offerings and technological capabilities.
• Long-term financial targets include 25% margins and higher free cash flow.

Financial Results

• Growth: Acceleration in top-line revenue, with a notable 10% boost from AI-related activities.
• Customer Growth: 12 customers generating over $1 million in revenue; 80 customers exceeding $100,000.
• Security: The security product line surpassed $100 million in annual recurring revenue (ARR).
• Long-term Targets: Aiming for 25% plus margins, with free cash flow 200-300 basis points above margins.

Operational Updates

• Investment Cycle: Increased investment in product development and go-to-market strategies, with expanded quota capacity in R&D and across geographies.
• AI Focus: Significant adoption by AI-native companies; growth in large language models (LLM) and GPU monitoring.
• Bits AI: Announced developments at the Dash user conference, focusing on service management, developer tools, and security.
• Flex Logs: Positively impacting customers by enabling curated log storage for compliance.

Future Outlook

• AI Monetization: Exploring pricing models for Bits AI, potentially linking to activity levels.
• Security Investment: Increased focus on Cloud SIEM and brand development, with substantial investment in go-to-market strategies.
• Enterprise Penetration: Expanding efforts through key accounts and geographical markets, addressing low current penetration.
• On-Premise Deployments: Considering dedicated instances and new pricing options for on-premise workloads.
• AI-Driven Productivity: Emphasizing product delivery through AI-driven productivity gains in the short term.

Q&A Highlights

• Open Source: The competitive environment remains stable.
• Margin Trade-off: Balancing growth with profitability through disciplined investments.
• AI Code Generation: No current estimate for AI-written code volume.

Readers are encouraged to refer to the full transcript for more detailed insights.

Full transcript - KeyBanc Capital Markets Technology Leadership Forum:

Unidentified speaker: All right, good to go. All right, thank you everybody for being here day two. I’m very excited to have David here from Datadog join us at our new location for TLF in Park City. We also have Yuka as well. Thank you for being here.

Thanks for having us.

David, Datadog: Yeah. It’s a good location. We’re having fun.

Unidentified speaker: Yeah. The weather’s perfect.

David, Datadog: Weather’s perfect.

Unidentified speaker: Yeah. View is Breakfast was delicious. The view is outstanding.

David, Datadog: Yeah. The view is outstanding. Yeah. No complaints.

Unidentified speaker: All right. So thank you again. I think a lot of people probably are familiar with Datadog, but I’d love to just hear from you what is Datadog in the sense of what are you guys achieving from the business outcome solutions for organizations? But also maybe more interestingly, what are the exciting secular drivers that we should associate Datadog to longer term?

David, Datadog: Yeah, yeah. So Datadog is a modern platform to monitor and observe the cloud workloads generally that are modern cloud workloads, customer facing. We have a real time platform that enables those working in production environments for the most part to see how software is functioning and to investigate problems should they emerge and improve the efficiency. The major driver of Datadog over the long term has been the migration of applications from legacy and on premise to modern architecture and digital delivery. Companies whether it be cloud native startups or the largest companies in the world over different pacings have been modernizing their infrastructure.

And Datadog has distinguished itself in having a comprehensive platform that is sophisticated but easy to use and can be used by all the players in doing this monitoring to do their job. What we’ve been doing over time is extending the platform from originally infrastructure monitoring to now having a lot of SKUs including an APM suite, a full log suite, ROM digital experience, security, coding, etcetera. So the platform has gotten more and more valuable over time and with that we’ve grown both the number of customers and the number of SKUs they’re using the revenues.

Unidentified speaker: Yes. That’s excellent. And we didn’t talk about AI as another secular driver, but I’m sure that will come Yes.

David, Datadog: We’ve to save something for later.

Unidentified speaker: Yes, exactly. So 2Q was, I would say, exceptionally strong quarter for you, acceleration on the top line. But I’d love to hear from your perspective what you were pleased with in the quarter and maybe where the upside came from. Just a lot of things to talk about on the positive side of things. I’d love to hear from you just what stuck out there.

David, Datadog: Yeah, definitely. So sort of at the core of the company we have been going through an investment cycle both on the product side and the go to market side. We expanded very rapidly when COVID happened. We had an adjustment in our end market as customers the world was optimized. It became more stable and so we had moderated our investment a little bit and what we told everybody was that we think it’s a very long term and large opportunity so we’re going to increase our investment and we did that successfully.

We’ve been ramping our quota capacity, we’ve been doing it in geographies across the ecosystem, and also been doing it in R and D. And so in the quarter we continued to have some marquee lands and expand. If you look at the earnings release there are a number of very interesting and large use cases that where we cross sell products, we have very significant use of our SKUs, we save money for clients and added value, and we’ve been doing that across geographies. On top of that we’ve been successfully attaching ourselves to AI. There’s an investment cycle going on which you all know about.

Most of the companies that have been successful have been pretty public about it and we’ve had a growing cohort of software companies that specialize in AI tools where Datadog has been the preferred monitoring solutions. And in the quarter we did growth as well as breadth. We had growth that complemented our top line by about 10. We also had 12 customers get over a million dollars, 80 get over a 100,000, and so we’re attaching ourselves to that use case. And I think another one is we’ve over time given some examples of when we get to certain milestones of products and we’ve been working on security for some time and announced this quarter that we’ve crossed the $100,000,000 mark in security.

So those are some of the successes of the quarter.

Unidentified speaker: Yeah, and I think it could get lost like the forest and the trees with investors. I think it’s a massive testament to Datadog and your innovation thought leadership in the space that you have this extensive AI cohort of customers that are the next generation of technology leaders that are using the Datadog platform.

David, Datadog: Yeah, we’ve always said, and this goes back to that when new technologies form, when we move from development, modern development, when things got containerized, serverless, now AI, that created a more complex set of applications with more of an impetus towards modernization. And that’s complemented Datadog as long as we’re keeping up. I think it is important to note that we are winning in investment cycle.

Unidentified speaker: Yeah. So those customers have been a beneficiary but and keep me honest but they’ve kind of been using Datadog in the traditional sense that other customers would use it. Yeah. What’s the AI opportunity both to monitor AI applications and for you you can break it up but for you to use AI to deliver a better solution.

David, Datadog: Definitely. And that’s a good point because you really have to look at this across the ecosystem. I think the most proximate was that there are a whole set of customers being birthed that are modern software companies that are perfect for Datadog. Then there are a whole set of enterprises that are beginning to move from training and experimentation into putting large language models in their applications which Datadog will monitor. We’re still early there.

And the evidence there is the use of our integrations increasing and the use of our LLM and GPU monitoring. Now most of the monetization to date has been through enterprises calling out through APIs to these other companies. So the monetization has been very much in these tool companies, but we’re confident that as they mature more and put more applications into production it will spread. So that’s the second opportunity. The third opportunity is the Datadog platform itself.

And this was a big feature of our Dash user conference where we announced developments in bits.ai in a number of different ways. The first one was in the platform in service management for production engineers, being able to use large language models to understand the root cause of what’s happening faster and then to remediate. And that’s in private right now. We have a number of customers using it. That was a very exciting part of Dash.

It was one of the places where you could hear in So the why? Because service management or the ability to diagnose problems and remediate them is one of the best use cases for AI. We also announced AI bits in both developer tools and in security. So I think that we have the opportunity to have it in the platform and make the platform more valuable to clients. And the last would be, is the fourth, how do we use it internally?

And so right now we’re trying to remove the barriers and let our coders for instance use such tools as cursor. We’re early stages on trying to see if we can increase productivity and output. And so that is what we expect in many companies but we’re eating our own dog food you say and using it internally and trying to see if we can use AI internally to increase the velocity of innovation.

Unidentified speaker: Yeah. I thought that was an interesting comment. You said the AI workloads moving into inference is mostly happening in the enterprise customers and are they doing tracing to understand like the latency of the OpenAI calls that they’re making?

David, Datadog: They would be, so that’s early stages for the most part. Enterprises have been there’s been greater investment in training and experimentation but as they’re moving into production that’s when LLM monitoring, model monitoring, you know, and integrations will be used. And we’re seeing usage increase over time which is a good early sign that it’s moving into enterprises in their production applications.

Unidentified speaker: Understood, okay. And then Bits AI, I mean I agree that resonated deeply at the conference and customers we talked to. I’d say Bits AI one point zero maybe didn’t get as much adoption it was tangible in the room, the two point zero version. Right. And I don’t think the users of the Datadog platform necessarily want to spend It’s not differentiated for them or interesting work for them to detect and remediate kind of just issues.

It really resonated when we were talking to customers at Dash. And Given that potential value that you’re providing, how do we monetize that?

David, Datadog: That’s a good question. We are working on right now the economic model where we’re thinking about how do we link this to what’s happening like per investigation or per activity. And this is true about all of our SKUs. We have floated out pricing, seen whether it is the right pricing for that type of activity. And that’s what we’re doing now.

And we don’t know the answer. We’ll tell you sort of as we get farther along. But what we’re thinking about is are we going to have, let’s say, SKUs where you get this capability in the platform and you pay, you know, pro level, championship level, whatever you want to call it. So it’s not something that we put out publicly but we do have customers that are paying, that are using, and we’re getting good feedback.

Unidentified speaker: Coming back to the AI native cohort of customers and you mentioned the optimization we’re all too familiar with back in the day. What are your learnings from that prior cohort of optimizations where they maybe you were surprised, I’m not sure, but obviously they had to correct and get profitable. How are you thinking about the learnings from that to make sure as these companies are scaling that you’re proactively setting yourself up to make sure it’s not as much of an optimization potential It’s

David, Datadog: a great question, and I think if you look through some of the other companies who have faced this, what we’ve been trying to do is look at the workloads and try to help companies through our account management and our engineers use Datadog in the right way. And so that would be if they are putting too many logs in or logs that aren’t related to real time production, we’ll try to help them send the right amount of logs. That’s one way. Two, we’ve been innovating the product stack, whether it be Husky or Flex logs or Frozen logs, to try to not only suggest changes but give them solutions that are correlated with their use cases. So if you need to have logs frozen or stored somewhere but you don’t need to access them in real time, have a SKU that lets them do that and not pay the real time price.

So we’ve been doing that in the platform. We’ve also I think gotten better about sort of value selling, trying to work on consolidation. Those involve things like migration credits, longer term deals, and figuring out how to get a client to find the right value with us, whether it be discounting on volume and increased commitment, having migration credits, having technical account managers attached to them for usage, etc. So I think those are some of the ways we’ve been working with our clients to try to evidence value in longer term client relationships.

Unidentified speaker: Yeah. And are you so flex logs seems to be a meaningful way to get customers to control their spend on logs.

David, Datadog: Or add use cases. It could be like for instance, okay, you need I think it’s more the assignment or matching, so you need real time, you need access. So for this amount of logs steer it this way and the price, but find other use cases like use cases to store your logs for compliance. And we’re trying to match up the price and the technology for the different use cases and in fact expand use cases.

Unidentified speaker: Yeah, has this ended up being a net headwind or a net positive?

David, Datadog: It’s been a net positive. Finding that those clients are essentially curating and dividing up and we’re getting our hands on other use cases that we were not maybe able to get our hands on earlier.

Unidentified speaker: Yeah, interesting. Let’s talk about security a little bit. We talked about this at dinner last night. And my observation on the call, it seemed like there was a tone change

David, Datadog: from

Unidentified speaker: you and Ali on security. And I said this last night as well, it’s like from a product perspective I think you’re punching above your weight of where your recent milestone was. Great milestone, 100,000,000. But your product capability I think suggests you do a lot more. But so to get to the question, it sounds like you’re willing to invest much more in go to market on security at this point.

So high level, what’s the rationale for why now invest in the go to market and what’s the opportunity you see?

David, Datadog: Yeah, think that why now is the product in certain areas has matured enough to be able to win use cases. So it doesn’t really do any good until the product’s at a certain level to expand your go to market and have those sales people or channel partners or whatever get there and not have the product to succeed. But we think in Cloud SIEM, for instance, that we’re there. The environment with the Splunk acquisition and the product capabilities and flex logs and frozen logs and all of that is enabling us to have a really credible offering. So what we know from how trying to sell security is that it’s not good enough to have the best product or a competitive product.

You have to reach the buyer who’s maybe a different buyer. You have to go more through the channels because that’s where security is. You have to develop a brand and you have to help them implement and migrate. And so those are all the types of investments that I think you heard on the call that we’re thinking about making. It’s not going to be a, know, day one we’re going to have another data dog for security.

We’re going to try to layer these on in a programmatic way and monitor the success as we go along.

Unidentified speaker: Yes, makes sense. And then M and A kind of relative to security but you can make it broader too. You’ve had a nice steady cadence of small tuck in M and A. How do you think about that strategy going forward and as it relates to security?

David, Datadog: Yeah, definitely. So I think security is one example. It’s really our product roadmap. We just did, for instance, we did an acquisition in product analytics in Epo and we did Meta Plane and data monitoring. And so I think we’re basically looking at how we can enhance the velocity of the product introductions through mainly technology based acquisitions.

And you know we’ve gotten good at that. That could be in security. I think we are not adverse to doing a larger acquisition. We’re not really making acquisitions for just to consolidate customer bases. We’d rather win the customer bases within our platform.

But, you know, if it’s the right price, the right team, the right acceleration, we haven’t rolled out. We’ve tended to stick to more of these acqui hires All about our product. It’s all off the product roadmap.

Unidentified speaker: Right. And it’s a well oiled machine I think in that process think at this

David, Datadog: we’re getting we’ve gotten good at it. Yes. Yes.

Unidentified speaker: I want to talk about go to market a little bit since that is an area that you’re talking about investing. Maybe just on the enterprise side to start, growth has been stable there from a consumption standpoint. You started down market and you’ve come up and you had a lot of success in transactions this quarter you called out, there was $60,000,000 TCV in the enterprise space. And my observation at Dash is a lot more big logos are consolidated. So how do you think about Datadog’s penetration from a logo perspective in the enterprise and for the existing customer base your penetration within that?

David, Datadog: We’re still if you look at our penetration, it’s still quite low. So when you look at the number of enterprise, our penetration might be somewhere in the double digits. And the increased penetration will come from two things. One, we’re pretty early in the cloud migration. So we still have tons of enterprises that are pretty immature, and the vast majority of their infrastructure is legacy infrastructure.

So that’s a wave we’re riding, and we think it’s a very, very long wave. Second, what we’re doing is we’re trying to consolidate. We think we have very, very compelling value proposition to consolidate the different parts of the observability stack onto data. Don’t forget, we didn’t have APM or logs when some of these other vendors established their position, so by definition we couldn’t have had those businesses. So we’re going through, you know, a consolidation cycle.

And I think the third aspect of it is to expand our enterprise motion in a number of ways. First of all, slicing and dicing into key accounts, large customers, that might be many years of working on that customer. Then majors which would be largest customers and we’re working on cross product adoption and cross business adoption and then a hunting crew. So we’ve gotten I think smarter about that and there’s a geographical expansion that we’re pursuing. There have been some markets where we arrived relatively later in the game and we did things in a more centralized way and we’re establishing presence in some of those markets.

Unidentified speaker: Yeah, should I think of it relatively closely associated to go to market investment priority this year to being enterprise?

David, Datadog: Yes, would say it’s prioritized towards enterprise including channels and things like that. I would say the non enterprise or SMB is focused on new markets where we didn’t have a presence. I think Brazil, India, non Japan, Asia, but our sort of our EMEA and our Americas presence is more mature in commercial SMB.

Unidentified speaker: Yeah. And a couple of your competitors support self managed on prem deployments. Is that a barrier or something that you would potentially address longer term to really drive full consolidation for some enterprises?

David, Datadog: Yeah, I think you’re going to have you’re always going to have in the very largest enterprise matching up of the choices for the tools to the business activities. So I don’t think you’re gonna have the monitoring of on premise workloads go away. But you know I think we are thinking about either how to slice or dice the packaging pricing of the monitoring of on premise workloads which may not be as storage or computational intense and maybe should be priced differently. And the possibility of having more dedicated instances. We’re looking at it.

I don’t think it’s where the center of the company is going, but we’re looking at it as a possibility.

Unidentified speaker: Right. We have a couple minutes left. Want to survey the room, see if there’s any questions out there. All right. Here’s one.

How do you look at the competitive environment on open source?

David, Datadog: It’s been the same as it’s been for a while. There’s been an impetus towards buying a system like Datadog, meaning the revenues in open source have not been growing as fast as Datadog. But there are always some places that want to combine open source with the system or want to try to in source. I would say it hasn’t become more intense in the last few years.

Unidentified speaker: Okay. Maybe last one, David, financial question. Just margins are compressed a little bit because you said you wanted to invest in capturing the opportunity. How do you think about the longer term growth versus margin trade off

David, Datadog: I mean we stick to what we have said in our Investor Day which is our long term target is 25% plus and with free cash flow 200 to 300 basis points higher. We’ve already proven we can get there. I think this is really a situation where some companies are still proving their economic, but we’ve proven we can become very profitable. So really for us it’s identifying good investments that we think can compound the top line. There are a lot of them out there and the way we’re looking at it is being disciplined, prioritizing them with trying to meet our obligations in profitability and continuing to improve.

We have a profitable business but not leaving on the cutting room floor growth opportunities. And that’s how we do it. It’s a balance. I think we’ve been good at it and we’ll continue to, you know, balance those two things.

Unidentified speaker: And two quick ones. Yeah. As a CFO of a software company whose job it is to create software, how much software in three to five years, bigger time frame, is written by AI? Question one. Question two, if you see productivity gains from cursor tools and stuff like that, do you ring the register on those savings?

Or do you just deliver more product?

David, Datadog: Well, we’re going to we think we’ve been clear we’re going to deliver more product right now. I think that might have to do with the fact that you know the productivity gains are there but not proven out completely. I wouldn’t be a CFO unless I wanted to have metrics on productivity and I wanted to match that up with the demand for product and product enhancements. So I think we’ll eventually get to some potential savings but I think we’ll in the near term we’ll try to use that to sort of get our products out the door faster. I’d like to see, you know, once everyone gets comfortable, I’d like to see metrics and proof, my kind of person, and see where we go from there.

I think there’s opportunity but the opportunity still needs to be realized.

Unidentified speaker: And how much code written by AI? Best guess.

David, Datadog: That’s a good I don’t know that. That’s a good question. I don’t know.

Unidentified speaker: All right. We’ll come back next year and have

David, Datadog: a better answer. All

Unidentified speaker: right. We’ll give a round of applause for David. Thank you so much.

David, Datadog: Thanks a lot.

Unidentified speaker: You. David, appreciate

David, Datadog: Thanks it so a lot. Thanks for inviting us.

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