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

Let's do this. My name is Matt Hedberg. Thank you. By the way, I just got to pause for a second. You guys make this -- this conference is fantastic. I was talking to Matt Finnigan who runs sales, like you can't even hardly walk in the hallways, it's just -- it's great attendance. And so you guys make this all possible. So I really do appreciate everybody coming and supporting this conference in these great companies. So let's get into it. And by the way, this is a big room. I'm intentionally going to be leaving some time at the end. Save some of your questions for the end. There's a microphone that will pass here and David will kind of -- yes. So David Obstler, CFO of Datadog. I'm sure you guys doesn't need much of an introduction. You guys are coming off of strong Q3 results. It seemed like a lot of stability in end markets. Can you just sort of like double-click on what you saw during 3Q and kind of up until when you guided relative to maybe the first half of the year?

Yes. So it was, I think, a continuation of the trends that we talked about in the previous quarters in that -- in our enterprise segment, we had sort of normal activity, meaning that larger enterprises were back to their digital projects and adopting solutions like Datadog, that resulted in a number of really large and nice deals that were featured on the earnings call and a continued improvement in the net retention rate there. And that's good news. That's sort of what we've been saying over the last 2 or 3 quarters. Continuation of an SMB of a stable environment, meaning it's continuing to grow, nice net retentions but not accelerating likely due to the economic concerns of the venture capital environment, et cetera. And we also said we continued our motions of cross-sell and development of a multiproduct sale. And evidence of that were the metrics we gave around the 3 pillars being now over $2.5 billion of ARR and quite a number of products getting past $10 million. So I would say it was a continuation of some of the trends that we've seen since what looks like the bottom in the historical time series of Q3 last year.

Q3 last year was sort of important for some of the key headwinds. I know I've asked you on the call. What percentage of your business would you classify as SMB.

It's about 1/3 of our business, 1/3 to 35%. Our largest segment now is tied maybe 35%, 40% in enterprise and SMB with the rest of being mid-market. Mid-market is 1,000 employees to 5,000 employees. Datadog was a mid-market up until very recently.

Yes. You've said that sort of some of the cloud optimization trends that you were seeing last year sort of stabilized. I'm curious, it feels like optimization is part of the new lingo that all companies should be optimizing all the time. What does that mean from like an ROI go-to-market perspective? Has selling changed sort of in a post-COVID world where it's just not -- you're not an order taker, but you're an ROI -- you have to deliver ROI. Has that conversation change relative to the optimization trend that probably continues.

And cloud application and cloud-related purchases, it's always been the case. So since we went public, we've said that our customers go through cycles of investment and deployment of applications and then optimization. And whether we're pointing it out has to do with whether it's become more or less in PAT. So like that is more about cloud applications. Yes, in environments that are more careful, you have more focus on the cost side and potentially the trade-off between growth and optimization. But I think it's always has been there and is going to be there in cloud applications.

I talked to you about this and you and I were talking about this yesterday. You've been saying since IPO billings isn't a great proxy. And reported billings wasn't great in Q3, you normalized and it would see more sort of mid-20s, I believe [indiscernible] be normalized for duration. Why shouldn't that be a concern for investors, especially when we think to next year, to next year's revenue growth.

Because billings will be higher or lower than revenues or ARR growth, which is what drives the business based on the timing of sending bills out that we don't manage for -- I mean, we're not a company that is strapped for cash and is trying to -- I want this bill to go out this minute rather than that minute. So -- and when we look at it over a weighted average basis, it goes back to revenue. So in a quarter, long term, it's correlated with revenues. And I think we said in the earnings that if you do the math, you'll see it's all these metrics go back to revenue growth. But in many periods, it's been higher and in many periods have been lower, and that has to do with the timing of billings going out versus the comparable period. Don't forget, we don't have a renewal cycle like everything renews on this day. We have a commitment cycle. So it depends when the client is going to redo their commitment versus last year. And when it's been higher, we pro forma it back to revenues and when it's slower, we pro forma it back to revenue. So I think we've been pretty fair in providing evidence.

Yes. No, it's a great perspective -- it's -- I think we all -- what are some words of advice you give to investors. And I think the question -- the answer was like, people get a little bit detailed in their modeling of Datadog and what ultimately revenues what matters most over sort of longer duration...

But trying to help everybody. Hopefully, it's appreciated. We're basically -- we're basically saying here, pro forma for this bill that went out at different times and there are slight duration differences is that -- so we're trying to help everybody, if you look -- I think and if you look through all the scripts, you'll see that -- there's a lot of analysis in that or pro forming that all gets back to this truth.

One of the real standouts from last quarter was AI natives. Really -- I believe it jumped from 4% ARR to 6% this last quarter. You talk about what drove that? Because that's a pretty remarkable...

Yes. So those are customers that are digital native or cloud native customers. They are not customers that are going through a replatforming, they're cloud native. And what really drove that was their demand cycle and that's as more companies are investing in the infrastructure. And you see that in the hyperscale as you see that in the NVIDIAs et cetera that will increase their workloads, and we monetize based on workload. So it's already a customer base that has a good fit because it's cloud native to Datadog. And we wouldn't have that acceleration within that customer base, unless that customer base is workloads accelerated. We're following that.

Yes. You also -- I think you mentioned that it could cause some variability in second class numbers. Talk to us about why that dynamic could actually show up?

Yes. So this is similar to what you saw before, which is that when our customers -- when customers ramp quite rapidly and they're above their commitment, they haven't repriced their contract to their volume where we basically price based on volume and, in some cases, based on term. So they -- when they do that, and this applies to all customers, you generally see the ARR go down for a period of time and then go back up as they grow. And what we pointed out was there are some very strong rampers in here that should and will most likely adjust their contracts and review their commitments. And that always creates volatility in the ARRs revenue. It's just -- this is a small segment of our customer base that you mentioned now, rapidly it had increased. And so therefore, the effect may be more pronounced in short-term.

And then just in terms of like what some of these AI natives are leveraging I assume it's a whole platform, but is there anything that's unique with that cohort of customers versus cloud native sort of general Datadog population?

No. Those are, that's the variability platform that's metrics, traces and logs that is essentially the same workloads that we are monetizing in our other customers.

I guess the other question that a lot of us are wrestling with when it comes to AI, and it's a broader AI question that maybe Datadog specifically, but it does seem like customers are moving into more of the inferencing phase of generative AI. Are you seeing any customers shift monitoring towards more of that phase of generative AI? Or is that hard to ascertain what they're actually...

Yes, we have around 30,000 customers. And what we always do is we build out the integrations to allow them to access those AI tools companies. And we said we have 10% of the customer base with 3,000 [indiscernible] and meaning they're sending us data. And we said we have hundreds -- in the low hundreds of customers using the LLM monitoring, which would equate to around 1% of their customer base. So we're seeing some traction, and we monetize when applications are enabled with -- in this case, enabled by LLM and in production. So we're seeing green shoots, but again, it's a very, very small percentage of our customer base that are doing it. In production, they're doing it. They're doing it, but where I'm talking about in production. We're so early in that, very early.

Are you seeing any -- outside of the AI natives, are you -- like say a bank are you seeing usage of Datadog within sort of like gen AI usage in the sort of a non-AI native cohort?

Yes, that's what I just mentioned. Of the 30,000 customers, 3,000 of them are sending us data. This is not about the AI tools separating that out. This is about our RBCs or whatever, 3,000 of them sending us. And then a few hundreds, low hundreds are using the LLM monitoring module. That would mean those are companies that are not AI tool companies, but they have LLM data flowing. So those would be -- we're giving out 2 types of metrics. We're giving out metrics about a rapidly growing cloud-native customer segment, you talked about 6%. Now, I'm speaking about our regular customers or non-AI-native customers. And those are the 2 metrics that we gave out indicating activity. But again, not activity that is turning into monetization at a large scale yet.

Let's talk about pricing a little bit. You guys said you started out more as an SMB tool, you've obviously moved up to the large enterprise quite well. How do you think about pricing and the evolution of pricing longer term, especially when you talk about some of these customers spending in figures.

Yes. I mean we basically price very much the same way as the hyperscaler and we try to, which is on commits. And we basically give them access to a platform and then they can choose what they commit to and whether they reserve or within the commitment to use any of the products. And it's all in a calculator and they can see it. It's very much the same way that it's the same way AWS prices. So that's the way we price. It's been -- I think it's been a very significant factor in the evolution of Datadog in that we price based on use and value not based on storage. I mean, there have been other models where there's been some concern, but we've tried to sort of lower, remove the impediments of adoption other than by consumption. And we've basically, as clients have scaled, they've gotten a better price point similar to what hyperscalers do. We tend to be -- for large enterprises, we tend to be a BU or a business activity type of spend. We tend not to be like Microsoft Office or their core cloud spend in that they're buying it centrally and using it across. So we tend to be based on use case and tried to price so that it is completely correlated with the usage in that new skills.

Got it. Yes. And do you -- like as you scale -- continue to scale the business, $10 billion more in revenue -- I mean is this the sort of the pricing mechanism into the future?

Yes, I mean, we -- that -- it's worked very well for us. We continue to evolve it. Over the years, like the last couple of years, we understand that clients sometimes need to ramp into their usage, often getting offers -- a competitive product. So we try to give them, let's say, ramp pricing so that they can have periods where they're bringing that on and don't have to pay for that workload if they're not using it. But there's been evolutions, but the basic model has been consistent and has been a model that's helped us succeed with our clients.

Some noncore modules, if you said $2.5 billion for the -- for the free. Staggered numbers. When you look at the competitive landscape, obviously, log is the big part of that growth [indiscernible] right now. How do you think about that opportunity? Because it certainly seems like a lot of that could be...

Well, cloud logs or actually the use of logs to investigate the health of digital applications by reliability and production engineers. As you know, we said a few quarters ago that, that business had gone over $500 million and has grown from there. And so that was the business that Splunk had tried to get over the years. And I would say, over the last 3 or 4 years, we've been winning a good market share in that. And then we recently commented that for cloud SIEM, and that would be SIEM, again, in cloud workloads that we feel good about where we evolve the product and we see product momentum. That is not about the replacement of the centralized on-premise Splunk cloud SIEM, but just similar to cloud logs, competing around that cloud side of it and we are going to have some success. I think we get asked a lot about is that going to change now that Splunk has been acquired. We don't know yet. It's too early, but when other companies have been acquired and put into the types of situations, we have tended to innovate and tended to get market share. So we'll see what happens there.

Splunk is one and there's obviously some companies that have been acquired -- private equity as well. How often do you see Dynatrace competitively because obviously you're pretty well in the enterprise as well. How often competitively do they show up.

Yes. I think Dynatrace has basically from their APM on-premise log is doing a good job. They don't have the complete observability platform functionality that we have. So buyers are smart if it's for cloud workloads with some on-premise, and they want the complete platform, the choice generally has been Datadog. And then if it tends to be a more of a on-premise, either monitoring on-premise workloads or an on-premise delivery, Dynatrace has a strong competitive position. In many cases, it's a choice of clients, whether they want to port one way or the other or have both and have certain workloads monitored by Dynatrace and certain workloads monitored by Datadog.

When you look to the future, obviously, [indiscernible] expansion and innovation of [indiscernible] is incredible, where are we in that evolution of products? Like I don't know how many you technically have today -- what are the next 5, 10, 15 look like?

Yes, I think we have, I think, talked about that in our Investor Day and we sketch up the service management. So I think some really interesting ones that we talked about are we have tended to be a company that is a real-time analytics company. But in terms of workflow, once you identify a problem, how do you create the workflow to route it and resolve it we really haven't enough with service management. And I think Oli talked a lot about Hongkong. And that means essentially not just paging but also the whole operational fabric of getting to the documentation and routing of a problem. And this is something that we have early signs of clients that they really want to pull us in this direction we've been investing. We don't even have a GA product or a product that's out there. And I would say, sold to everybody yet. We have a lot of clients who are asking for commitments on that so that could be an area of vector of growth. We have the security that we talked a little bit about, which is -- there are 3 products in that. We talked about the cloud prem but there's container and infrastructure security and application security and in terms of DevOps environments, we've begun to see traction in attaching them to APM infrastructure, that could be a growth factor. We have a product that's in beta and working on in business analytics that we think has a high degree of correlation to RUM. And what that means is that, that allows you to see more of how the application is affecting client buying behavior. And we do that in terms of how the application is working in the mobile device say, but this was business analytics. So these are the other vectors of potential growth. And then there's the whole AI set of things, whether it be bits or the LLM durability. So we think the reason why we're rapidly innovating is that -- or 2 things. One is our clients want more and more functionality in a single platform and the world is changing very fast. And so that's the 2 reasons why we're continuing to be a product-driven company and innovate quickly.

You have $3.2 billion in cash, I believe that's the number roughly, how do you think about broad capital allocation? You really haven't relied heavily on large-scale M&A. Does that change in the future? Is that cash balance continues to build as a company continues to scale and I guess what would be a material acquisition for you guys?

Yes. I mean I think that when we look at our -- where our market cap and our amount of cash, we do not feel we're over capitalized. We feel that it's an appropriate capitalization and gives us flexibility. And so far, we've tended to invest more in technology teams that have maybe monetized their opportunity a little bit, but not a lot. And so what could be a larger acquisition, we look at it. I don't know if it will -- if these things will happen, but it could be something that is on our product road map because everything we do is about the product roadmap and the product vision, but it's gotten farther ahead in monetizing the opportunity. And therefore, we would have to pay a higher price for it. We would only do it if the R&D teams of quality would stay the product architecture is compatible with our platform. And very importantly, we got strong signals that we could retain the people that were important in the technology side.

Yes. Yes, there was some news earlier this summer. I think [Technical Difficulty] I don't know if I missed the call or but you're not looking at anything like some material right now... In terms of balancing growth and profitability business, you guys really standout can deliver in really both. As we think towards the future and maybe the economic environment starting to improve a little bit. How do you think about that balance of growth and margin.

I think we've always -- we risk manage this well, maybe too well when things -- when there were more risk factors. But we are really always thinking about this as a long and large opportunity and always looking for ways to invest in the product and the go-to-market. So I think we said -- I think it was third quarter last year that things were not as bad as they could have been on the top line. And we had been really good operators in optimization of everything. And so we got ahead of ourselves on margin, and we were now next going to go and work on investing against this opportunity at a higher rate. And it takes a while, and I think we're doing that. I think we talked about on the call, having some evidence of success of ramping quota capacity significantly in the second half of this year and then into next year. It takes a while to get the people in and get them ramped. So I think that's a good example of how we are trying to push the investment forward, which, hopefully, on a weighted average basis, as we have shown we'll be quite disciplined, but it won't be in a straight line meaning the revenue growth and the expense growth won't match in every period on a weighted average basis over the long period, if they will, but we're probably looking at trying to expand our investment, as you said, market better winning in the market, big opportunity, investing in both the go-to-market organization and in R&D.

Is there a framework which you think about long-term margin expansion like sort of as a rule of thumb?

Yes, we gave a long-term goal of 25% plus. So we really aren't -- we understand that it's important to the investor world, what happens in these micro time periods, but we're really sketching out the company to be a compounding grower with a 25% plus margin over the long term. And as I said, that will result in periods where the investment exceeds the top line growth rate in periods where it might not -- as you know, because we're a consumption model, we've always said we have to plan our investments in a certain way. And then the top line because of consumption could change versus that but that's how kind of we think about it long-term opportunity, emphasis on growth, compounding top line growth rate in a strong economic model, and we gave the guidance of 25% plus EBIT.

When we think towards next year, obviously, it's too early to guide to '25. But what would you say is -- for us, as investors sort of a framework? Are there things that you think about like, well, I would consider growth in logs or I would consider AI natives? What are some of the key tenets that we should think about when we consider '25?

We consider 2 things. We consider the white space, which is the customers that are out there that we don't have or don't have a material way. We plan bottoms up. So we go and look at the accounts and then the growth -- product-led growth in the customers that we have. So far, we've had new logos growth contribution being around 25% and existing 75%. And then we know against that, that there's 2 things we can control. One is the ramp quota capacity and doing that in an intelligent way. That's a gross term. But underneath of that are many, many micro decisions and then the product investment. So we really think that way.

Yes. I'm going to pause here for a second [Technical Difficulty].

How much exposure or government spending? And can you help us give a little color on this doji threat we are all facing.

Sorry, government efficiency.

Yes, right. The Department of Government Efficiency.

So we -- for better or worse, we have a very small government business. We came to it later. We have to invest in a separate instance and then bill the team. So we've seen it as an opportunity. We're really, really have a very small government business. So to the extent that there is no more government. Then -- yes, I don't know, but let's just say it wouldn't affect us as much as other people. We don't have a very large government business. It could cut -- it could hurt our ability to grow into that, invest into that. But I don't think we're very -- we're not very exposed to government today.

You do a great deal of business with small business, how do you characterize the strength of the SMB as an industry from your point of view.

Yes. We tend to be, I would say, more towards them, the middle. So we don't really sell to mom and pops. The reason why you have to have a product that's -- you have to be buying cloud. So you have to be in AWS or GCP customer that kind of thing. So essentially, I would say that we have a larger -- the larger side of that -- and we have strong growth in both customers and net retention, but not where it was in the bubble. So I would say it's been stable and good, but it hasn't been reaccelerating back to the glory days.

Maybe to wrap it up. We're sitting here next November. Looking to crystal ball, what are you going to be most positively surprised with -- the enterprise business is really accelerated because the economy is in a better place, AI natives or -- I mean what do you think some of the surprises [indiscernible]

No. I think something we're all watching is the effect of AI in production environments since we're so nascent in that. So things are moving fast. And if that's the case, that could accelerate the replatforming because essentially, most of the applications out there are legacy applications. Most of the infrastructure, as you well know, is legacy. So those things that accelerate the migration and replatforming are positive. So that's something I think we really are watching out for. I think some of the new products that we have talked about that are exciting like OnCall or cloud SIEM, I'm very anxious to see what happens with them over the next year and see if they become meaningful parts of the revenue and then, yes, the enterprise buying cycles and enterprises all over the world, what they are doing with their cloud migration and how that's moving, which is related -- a little bit related to AI is another thing that we watch very carefully.

And what is -- on that enterprise piece, what do you think if we're on the 4Q call and some enterprise spending is -- like what are some of the KPIs you're looking for that give you confidence in saying, this is actually getting better.

Yes, it would be the net retention. The composition of net retention between existing products and new products. The number of new logos in enterprise. The geographical distribution of them, which could inform us on where to place our investments as we go forward. And that would all go back to the revenue, the enterprise revenue and their growth rate within Datadog.

Yes. Yes, we're out of time unfortunately. Thanks a lot. Thanks from all of us here today, David. Thanks for coming everybody.

Thanks for having us.