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

What a delight to host 2 companies from the great New York City headquartered in New York City and my favorite outside of San Francisco to ever -- I spent -- I think I spent the most amount of time outside of San Francisco in New York. So I love New York, and welcome Dave Obstler, the CFO of Datadog, which is based in New York. MongoDB had Mongo based in New York and Datadog.

It's very appropriate that we're back to back. Dev is on our Board, a longtime Director of Datadog, and we learn a lot from each other. So it's a very appropriate pairing.

Kindred spirits, kindred spirits. Two great companies back to back. So welcome back to the Goldman Sachs conference here.

Thank you. It's great to be here again.

Now we're doing this fireside chat. We've done this at multiple ballrooms whatnot. Delight to have you back. And I keep asking the same question. What is the vision for Datadog 5 years out? And if we are to come back Communacopia, I think it will be 2031. What does your company Datadog look like? Just as Dev was asked the same question about MongoDB, what does Datadog look like 5 years from now?

Yes. I think we want to look at our customer, which was sort of the production engineer the reliability engineer, the DevOps. And we want to be the platform they turn on in the morning and never turn off or perhaps never turn off. And when you think about how the world is moving, we'll talk about it towards more and more complexity of applications, more and more migration. There are many more sort of use cases or breadth of use case that we can satisfy with that customer base. We've already, I think, had a strategy well articulated in our platform in metro traces, logs, observability. But anything that touches the function of that application when it comes to -- and we'll talk about a database, network, LLMs, service management, we want to own. And then we want to spread out our use cases to things like security or DevSecOps and sort of coding tools. So that's the vision. That's what Oli, our Founder and CEO, and his partner, Alexis, have been doing relentlessly since the founding of the company and want to continue doing.

And how do you operationalize division? What are the things you're doing to put this action -- put this in action and help actualize the vision of the company?

Yes. That's right. And that's where I come in. There's many, many types of product enhancements and go-to-market enhancements. I think we're in a very good position given the size of our customer base and platform and the fact that we get real-time feedback back from customers, we, as many of you know, our consumption model with underlying subscriptions or credits. So we actually can see what our clients are doing. And the philosophy has been to look at what they're doing in their day-to-day operations and have a list of things where we can enhance value or develop the platform and then get that feedback from customers. We've been -- and I know it's one of your questions, we've been announcing various milestones, $50 million, $100 million, $750 million of parts of the platform that are going to be adopted. What we do day to day is think about how important a use case is that and can that be evolving from that $500 to $750 million and beyond. So that's how we do it. It's mainly from the customers.

The compounding S curves. You've got a product there are disclosure for logs was a whopping number a few years ago.

Yes.

I want to go back to the vision question a little bit. You throw an LLM networking, et cetera. In the cloud world, was easy to understand how the network topology and the infrastructure layer got to be much more complicated, much more massive scale and how Datadog kind of rode that way right. As you think about AI and what's ahead, what is the relevance of Datadog's core technology in an AI world?

Yes. And sort of stepping back, what we saw in history is as the world got containerized Kubernetes serverless, as it became impossible to monitor these applications using legacy observability platforms that enhance Datadog, and we see that happening again when it comes to GPUs, LLMs. So in terms of AI, there is a number of things we're doing. I'll start with our platform itself, our product. One is our goal through our integrations is to monitor wherever the workloads and data goes, it's Datadog. So we're essentially developing integrations into the AI tools. We have 4,500 of our customers now sending us data from AI tools. We want to be able to monitor and we can GPUs and CPUs, and we want to refine that GPU. And then we also want to be able to monitor the LLM in the application, and we announced our LLM monitoring product -- so we want to do is monitor all the content. That's one part of it. Then we want to AI enable our platform. So we believe that to continue to be the leading observability platform, we have to inject AI into our platform, and we've been calling it blank Bits. An example of that is our service management, where we've always had machine learning. We've always had Watchdog. We've always had analysis of correlations in what might be happening. But we're using our LLM and outside LLMs and training them to get quicker in the diagnosis of problems and therefore, be able to become more reliable and speed up the work of our clients. So that's a good example of a platform feature. Then we have the customer base, which we'll talk about. So we've always been a company because of the innovation, the R&D that has been the choice of platform for what we used to call cloud natives, but now we've created a new segment called AI natives. They're essentially cloud natives. And if you look at some of the disclosure we've made and we can talk about this further, we've been gaining quite a bit of traction in that market. That seems to be where a lot of investment is going. So we want to monetize that in our customer base. It's an accelerant, as you just heard from Mongo as you've heard from a lot of companies. And lastly, what about internal to Datadog, what are we doing? And we're trying to dog through our own uses, and we're trying to use AI, whether it be coding tools or the service management more proactively in order to accelerate our product development as well as eventually, we may become more efficient in spending.

Got it. Got you.

It's a long answer, but it's a big topic.

It's a good -- that's pretty technical for somebody with the finance side. I think I've mentioned this a couple of times before. Are you sure you're the CFO or you're the Chief Product Officer?

I look at all the great work being done by our engineers and product and try to understand it.

Thank you. You've done really well absorbing it. Now to dig into consumption trends. We talked about SMB improving, margin enterprise stable-ish coming out of Q2 results. Can you just give us an update on the broader spending trends across these cohorts enterprise versus SMB?

Yes. I think in SMB, when the bubble burst we had, as you all know, we had funding pull back. We had a change from growth at all costs to the combination of growth and efficiency and that hit the SMB. Now for us, because you have to have a cloud deployment, we're not talking about what some of you might think of as SMB. It's not your corner dry cleaners. Essentially, many of these companies have significant revenues and 500 to 1,000 employees. But they had to change what they were doing and funding got constrained. And so we went through an adjustment there. And what we saw in the last 2 or 3 quarters is -- this is excluding the AI. If you add the AI in, you would see because most of them are SMBs as we define it, less than 1,000 employees, you'd see a material increase. But -- in the non-AI, what we've seen is things return a little more back to normal. And in the last quarter, we saw a pickup of our net retention there, meaning what they're doing is getting back to -- maybe they've calibrated, they've optimized and they're getting back to the appropriate balance between growth and costs. So we're seeing that. In enterprise, this is where we have a very, very long opportunity, meaning if you look at the percentage of workloads that are in the cloud and then modernize, not lift and shift, but modernize. You see we got a long way to go. That might be in the 20s or 30%. And there are many enterprises that are right at the beginning of this. So what we've seen is a return to, I would say, the priority projects, some of them are AI related and we've seen steady growth and consolidation, meaning we've seen similar growth rates as we've had in previous quarters. We still have a careful environment, a balanced environment, but what we're trying to do there is expand our enterprise sales team. I think we got a little behind in that. Maybe we risk managed a little too much. There are a lot of geographies we can talk about, and we're trying through the combination of product innovation and go-to-market to accelerate that. So we're in a good place. We're not in an ebullient place.

Yes. That's good to know. You've seen good growth from -- actually tremendous growth from AI native to your point, which is not included in the SMB consumption. Over the last 12 months or so, how do you think about the potential for sustained growth for Datadog in this cohort? And why is Datadog so well positioned in this AI cohort?

It's a great question. So really, we follow the workloads, and we follow where revenues are being gotten. And you can see a lot of these companies are publicly announcing their progress. They're giving you revenues, they're giving you funding rounds. And we have a business here that has hundreds of customers indicative of the demand environment. We have 8 of the 10 largest companies by valuation in the cohort. We have -- we said over a dozen million-dollar customers and maybe even more importantly, long term, over $80,000, $100,000 customers. So the signs are there, like other companies are discussing that we're attaching ourselves. Now why? Datadog when these are -- we can call them AI native, but what are they? They're modern software companies whose whole business was invented in the last 5 years or so. They have a modernized tech stack and their whole business is delivering to APIs and others to their clients. That makes the delivery of the product mission-critical. And because Datadog has invested most of its dollars in servicing the modern side of this, the cloud side, the reliability side, the breadth side and the speed side it's a perfect solution. And we've always been the leader in the choice, I'd say, in cloud native. So if you want to call these cloud AI natives, it's an extension of that.

Yes. And as you look at that cohort ahead, this question came to me like midstream. What are the lessons learned from servicing the cloud-native co-marts during the big cycle that we had? And how do you apply those learnings to monitoring the native cohort? So what are some of the telltale signs you're looking for as a CFO to make sure that a balanced business, not overindexed too much?

No question. So we learned a lot of lessons. We have pretty good transparency because we have the meter on it, it's consumption. We can see the level of usage and the type of usage. So I think what we learned in the cloud --

At what level you could say there is an AI bubble happening in venture like you had the cloud bubble happen in 2021.

I think, yes.

We should be smarter now in this cycle.

Definitely, I think you can see it's a much smaller part of our customer base. So essentially, the impact of whatever may happen for better or worse positive and negative is going to be smaller, but you see a workload growth. And what probably will happen will be -- there'll be some winners and losers. So you're going to have some consolidation. You're going to have some companies that are really mission-critical and their workloads are going to continue to grow. And you're going to see more AI activity in all applications. So what we -- I think what we learned was we're here for the long-time relationship with customers, meaning our application for the good part is frictionless. But that doesn't mean we can let there be no friction. Sometimes we have to be the friction. We see what's coming in. And so we are proactive in helping the client use it. They may be sending us too many logs. They may be sending us the wrong logs. What we've always done and learned in the cloud native, it's really important to have long-term relationships. So we're focusing on the length of contract. We're focusing on initiating the increase of commitment where they can get a better price and what it means in the trade-off of commitment and size. We're focusing on our own platform. I think we talked last night that when you think about logs, it's not just logs, it's what are you doing with the logs, which is why we've created flex logs, frozen logs, a number of different things to try to match up the use cases with the SKU and that doesn't mean we're cutting price. That means the intensity of the cloud use of that application is less than real time. And therefore, what we're going to try to do is instead of pricing and we've already done it, sort of unilaterally, we're going to try to match up on a gross margin basis, the costs and the SKU price. And that is creating, I would say, more stickiness, and it's also creating a broader market use cases and logs that are beyond real-time reservability. So those are some of the things I think we learned in the bursting of the bubble that we're applying in this generation. There may be more volatility, but we're going to try to -- in my seat as CFO try to manage that volatility in a more proactive way.

I know people try to sort of get at, "Oh, why do this large AI native customer not grow the business or there's all kinds of specificities." I want to flip that the other way and say the biggest native AI customer, Datadog. What are you doing right for them? And what can we learn from that success? And why could that not be a template for other AI natives that may be on the fence? Should we do it on our own, but look at this big case study, a shining example?

Yes. I think that's a very important lesson. When you look at how --

The glass half full version.

The glass half full. So yes, think about it. it's a very good thing that all of these companies are choosing Datadog, and they're choosing Datadog because for their use case, it's the best product their DevOps teams love it. They -- if you try to take away their Datadog, they protest. It makes their job easier. The time and cost of remediation is dramatically reduced. We've been able to prove over the years and with this cohort that economically, it makes more sense to use the platform than to build it yourself. You have huge investments you have to do yourself. And that's why with so many of these cloud natives, we've been able to grow the business and why our gross retention is so high because the vast, vast majority of customers choose to stay with Datadog and grow their use. So I think we have a whole team, business value team, that does nothing other than relentlessly prove this to customers. And you can look at it both on the cost side, but you can also look at on the revenue side. If you having -- and a lot of these, as you build companies, you have certain accidents or things happen. If that happens, you lose a lot of revenue. So we've been able to prove that it's a good decision net-net to use the Datadog product.

So this is like value engineering going and say, okay.

There's prioritization, there's costs, all of those things for the vast majority of our customers have chosen that way. I don't know if we can get into the large customer, if you'd like to and talk about that. But I don't know if that's what you want to turn next. But the largest -- the most customers are not building a Datadog internally. And so we can't tell what happens, and we certainly don't retain every customer but we have a very long track record of keeping upper 90s of customers, and we think it makes sense for them to use the platform.

David, did you know that I can by code my way into a Datadog competitor?

I mean I did not. I can't.

But problem is it doesn't scale that well. I mean it does not integrate. It does not have governance. It does not have security. It does not have authentication, that's not ...

I think you just heard that. I was -- when I walked in, Dev was talking about that I mean when you think about the difference between a consumer going into a model or chat and all the things that happened in the enterprise, where these are your mission-critical applications, you have to balance up time, putting new functionality and security, privacy, speed, the platform being used by everybody. It's not at all trivial, which is what's made Datadog what it is.

I want to get into some of the growth businesses. So it's an amazing since you started disclosing -- since you started disclosing logs, APM, that business is those businesses have grown pretty massively there approaching $1 billion in revenue. Can you talk about what's going on in the APM market and logs and I want to get into security just a little bit?

Yes. Definitely. So you have the observability where we repeat this a lot. We call it all these products, but our clients call it problem solving in the platform. And what they are speaking loud and clear is they don't want to go to different point solutions given the real-time nature of it. So I think as you just mentioned, we've done a really good job of creating a platform that covers metric traces and logs well. And then we've been able to extend it into a number of the things that affect the application network database. Now these we've announced that these products are growing very fast. Synthetics and RUM, what does this mean? You're taking it from the back of the infrastructure all the way out to the device product analytics, things like that. So in the platform itself, service management, we've been able to create additional SKUs that have become significant. Then on top of that, you have some growth vectors that are tangential, somewhat related and security is one of them. So we have a lot of the data. We have a pretty good real estate of customers and -- but we didn't come to security. So what we've been doing is investing in the 3 areas of security, which would be Cloud SIEM, cloud security, which is posture management and vulnerability and application security. And we've been, I would say, in the DevSecOps world where they but very closely, we can attach, and then that happens a lot in cloud nativity. But what we're doing is moving to the next level, which is essentially how can we use our infrastructure and our for instance, logs and create a Cloud SIEM product that is able to address the nature of compliance and other use cases besides observability where we've been very successful and we're starting to see success there. So I think we announced that security had gotten over $100 million, which is an achievement, and we have some game plans in product in marketing and creating channel relationships and expertise in sales teams to try to push that. I think we have a great opportunity in Cloud SIEM, given where we already are in logs and some of the other things that have been happening with some of our competitors. Also, we have the AI, which we mentioned, the LLM and the GPU and then service management. I want to address this as a kind of a combo of an observability platform, but extending it because we generally have been a company that analyzes data produces clues of where things might be wrong, but we haven't been a workflow company. And what we're doing, I think we think AI accelerates our opportunity to reinvigorate, reinvest in this and essentially go from what's wrong to who's going to fix it and way out there maybe auto remediation. So these are some of the areas we're most excited about in sort of growing on top of the observability.

That prompts the obvious question, ServiceNow. Is that who you're trying to -- I'm not suggesting that you go up against, but have you uncovered a niche in the market that they're not addressing so well that the deal product is naturally suited to address because of the adjacency. That is -- what do you see in the ITSM market? We had Mark Benioff from Salesforce also talked about we're getting into the ITSM market, right? So what is Datadog?

Yes. So ITSM, you have to then go below that and figure out who is it? So IT or when you call your corporate IT group, that's not our customer base. So what we're doing is the whole thing's platform is basically we have a real-time use case that emphasizes speed and so what we're trying to do, I think you might look at Opsgenie and things like PagerDuty. We're trying to do it for DevOps and security reliability engineers. I think there are -- in this case, you have to look at who the end market is. I think ServiceNow obviously has done a fantastic job in a number of markets, but we're not trying to boil the ocean. We're trying to have this be tightly aligned with our platform to create more value to our customer base. So I think in the end, if we're successful, will sit a lot. Those customers will have ServiceNow for what they do, and they'll have Datadog for what we do.

Got it. Got it. Got it. Want to talk about some of the newer products, and you did touch upon this a little bit, AI observability, LLM observability, database monitoring. What do you see in the opportunity set? And what is your investment philosophy to nurture growth in these nascent markets?

Yes, definitely. When it comes to looking at sort of prioritization, since we put everything on a common platform and about 50% of our investment in R&D is platform, that is a huge birthright, meaning we're more efficient than others in putting out new products because that's sharing in a very large investment in platform. So some of the things that we've been able to do is, as in database, for instance, as the data that you just heard you just heard from Dev and Mongo as the database world and the data world has innovated, there's more and more connectivity into the applications and more variety. And in that case, it's been really you cover another database, your revenues increase because we need to see our customers need to see everything that affect. So I think database has been a really good opportunity for us, take RUM.

Particularly the MongoDB database, what's that MongoDB thing?

I don't know what that is, but people are using it. And I just heard that it can't post graph -- I don't know. I don't know the database world. I know about monitoring it. But when you think about how this is evolving, and this is the same thing as our other integrations. We need to be comprehensive. We need to -- and as it gets more complex, as customers have more choices, that's when you need Datadog.

I think at dinner last night, you made this point, just thought about it. 50% of the research development dollars are for the platform. So -- and everything is an extension to the platform. And I think it's so underappreciated because you can think of building an APM company and then another product that then you got to extend the breadth of the platform. But when I first met Oli, just blew my mind, have we got the idea for this company in San Diego decades I mean -- so -- and the view back then when you find out the company had this idea, it was a wide-spanning view. And I think the pieces are falling in place into that view.

Yes. Yes. It turned out that infrastructure was the ideal place to start because everybody needed Ubiquiti, so you got a large canvas. And then data, when you think of what others maybe didn't do first, Oli thought of the underlying architecture and the sort of -- coding of data. And if you sit in meetings with him with our product meetings, you see that he is obsessed with UIs and customer activity. So a lot of companies have a great product. It's so complex to use, and you can't see how to use it. every time Ally sees that it's not very intuitive and native that somebody can't pick it up, he challenges. So I think he also created a very, very customer-friendly UI with workflows in the platform that could attach really quickly. And those are some of the things when you get the platform that made Datadog.

I don't know if it was for me, when I first saw Datadog was 2011 or 2012, AWS reinvent. I went to a demo booth. I don't know if was Datadog. It had massive monitor with flashing signals and all. What is this? It's so animated and so expressive and so full of data almost 12 years ago. Subsequently, my other wake-up moment was 2023 DASH in San Francisco. I would love to have DASH in San Francisco. You launched your refreshed version of the logs product. It just blew my mind.

Definitely.

The live demo, the LLM monitoring, it's just -- of course, Selina and my team went to the DASH conference in New York, and she said, "Gosh, this -- you look at all the stuff that I picked up, I just spoke to all these partners and these customers, it's a company that's got a lot of buzz."

Yes. I think when you think about -- you want to look just methodically about what's going to affect the application, and of course, LLMs will be in applications. You can't cover everything else and not cover the LLMs. So the goal is to be comprehensive in a single unified platform.

Dave, what's happening -- by the way, the 5-minute mark, anybody has questions, just raise your hand. We'll get to you. Doing a pulse check. Okay. What's happening on the GTM side? I know that -- last year, when you gave guidance for calendar '25, we had built in some expense buffer to ramp up the go-to-market engine. What is happening there? What is -- I know you said you were a little bit behind in hiring. But how much of this is actually proactive? And should we read this as a sign that you're actually -- I always am an all-school software guys when companies ramp up hiring and sales, that is a bullish sign and when companies ramp up CapEx, that is a bullish sign in software. So what am I to make of your signal that you intent to step up your sales and marketing?

Definitely, I think your old time-ness, is right on. We have a reading of significant uncovered white space and believe that there's a correlation between ramp quota capacity and top line. And so I think we -- on the back end of COVID, we got a little conservative. Some of it couldn't travel. We couldn't develop the international markets as well. But we saw a lot of white space we weren't covering and we have a lot of proof points. So this might surprise you, but we had no one on the ground in India or Brazil when COVID ended, we were covering them centrally. And so I think what we learned is in looking at the white space and looking at the competitive environment, there were huge opportunities and a large number, I would say the Middle East would be an example. That we just had nobody, and we came to understand that we need the combo of centralized sort of SMB and commercial sales and marketing and feet on the ground. So we've been developing those markets and it's paying off. We're seeing a great growth. So I think, yes, you're right. It is a bullish signal that we think we can get really good return from increased investment in sales and marketing.

And the time to productivity, is there any trends and changes in how quickly people get productive? Because Datadog is now institutionalized today versus 2, 3 years ago. So a rep joining the company today ought to be -- it's got to be easy. Not easy, but less complicated.

Yes. We definitely are focused on enablement, but still takes in enterprise. It still takes a year for a rep to get ramped. And that's because they have to get educated, but then they have to make their champions go and make their champions and companies. And then after that, since we are still somewhat land and expand, we have to get our landing spots and then grow them. So I think potentially, we have a longer ramp but a ramp that you can monitor along the way. So I still think you should think about a year to ramp.

Just to finish up here, by the way, any last question to -- last chance. Okay. Maybe what -- I'll do it slightly differently. Do you have a question for me?

Yes, what do you think of the opportunity or risk factor of AI on in one case, application or a SaaS software and the other case infrastructure?

Infrastructure, obviously, more insulated because at the end of the day, it's about compute, networking, storage and bottlenecks. These are things that are homogenous across tech cycles. But as far as SaaS is concerned, I liken it to the web browser, late '90s, the web browser became the front end for most things and enterprise software lagged pretty badly. And Mark Benioff was going to be speaking later today. I had this idea that we need to put our web browser front end to the boring drab world of enterprise software. And that not only just became the UI for software, but it replaced the front-end application layer, right? So one thing led to another. So the back-end logic of our business is business does not change. So I think what we saw was the catalysis of the enterprise software industry, the web browser front end, everybody said, well, Amazon, Netscape, all these companies are going to destroy enterprise software. I know actually, there was a birth of companies that changed up the user interaction model, the application code of the front end and we had a 20- to 25-year run, right, as a result. When I look at AI, maybe I'm being completely wrong headed about this, but AI is the new UI, and it's going to change the front end of the enterprise software industry, the application industry. But I see a graceful world where you interact with the software through AI, whatever your engine is, whether it's a foundation model, XYZ? And then people, I think, are always ultimately very curious when you enter a prompt, you get an answer back and you want to find out more, you want to dig and so I want to go to the source. The transition from UI, which is AI, into the back end of software, the back end of software will also change to accommodate the graceful transition from AI into the software and companies that make the transition graceful and are able to accommodate that business model aspect to their -- I think one of the panelists on the VC panel yesterday said it best. I think Byron, he said some of the SaaS companies are trading at 5x multiple today, we'll go to 3, and some will go to 10x. And that's what keeps me super excited. There's going to be some massive transformation. It's not going to be the same. But there's a lot of money to be made. And I want to thank you once again for your partnership. You've been tremendous. I really love these discussions and --

Thank you very much. Thanks for having us. Thank you. Thanks, everybody.

Thank you.