DigitalOcean Holdings, Inc. (DOCN) Earnings Call Transcript & Summary

All right. Good morning. Thank you so much for joining us at the DigitalOcean Session Conference, and special thanks to Paddy. Thank you for joining us.

Well, thank you for having us.

Can we get the door closed, please? If someone please. Thank you. So Paddy, I wanted to ask you about a topic that's been very much in the news [indiscernible] post Oracle's earnings report. I think there's a fair amount of industry discussion on whether investing in inference and training, training specifically is actually a good. All right. Let's try that again. So Paddy, I think the news flow this week has been very much focused on the unit economics of training and inference. Now the beauty of the DigitalOcean business is you do a little bit of both and increasingly inference. So how do you think about as a CEO for DigitalOcean, how do you think about whether investing in training and inference is a durable, healthy business for the long-term, and maybe take training and inference separately?

Yes. So first of all, thank you for having us here. It's wonderful to be here as always. It's a great leadoff question in the sense that this has been a dominant theme for us over the last several quarters, but also this week where I've spent a lot of time with AI native companies that are ramping up their footprint on us. And I would say we made a bet a couple of quarters ago that we're going to be focused on inferencing for a number of strategic reasons. Number one, that's very close to the DNA that we have had over the years. And a couple of really interesting data points from a unit economics point of view. For training, it is all about GPU dollars per hour. But for inferencing, there are a lot of interesting patterns that are emerging where if you think about inferencing, it's all about the throughput that you can get, like the flops measured, right? And within reason, like within a family of GPUs, increasingly, our customers really don't care about whether we are servicing it with -- like I'll take an older example of H100s versus H200s, like they're like, okay, as long as I can get this kind of throughput, I really don't care how you service us. And so it's all about the dollar per flops versus GPU dollars per hour, so which is a pretty big shift. And interestingly, there are a couple of other types of inference use cases that are emerging where I spent time with 2 startups -- 2 different start-ups this week, both of whom have freemium business models. So for them, the free tier, they're okay or they want us to serve the free tier customers using an open source model that we offer through our serverless inferencing fleet. And for their more "premium customers," they want to have a closed source model that they have fine-tuned and they're hosting on raw GPUs on our infrastructure. And they want us to do the load balancing, and the routing dynamically based on whose request is coming in. So if you look at the price performance on these 2 types of requests that we are getting, very different, right? One is about 1/4 or 1/5 the cost profile of the other. So from an inferencing point of view, it's a totally different ball game in terms of how our customers perceive the unit economics. And that's why it is really important for them to not only have a provider that is just racking and stacking GPUs, but have a full stack agentic cloud that can do all of these things dynamically.

And level set us on the mix that you have today, are there exceptions to the rule way, will accept to train workloads as the part of [indiscernible] on the customer journey?

It's getting smaller and smaller part of our fleet. And part of our resource allocation philosophy, and that's one of the big things we're looking at for next year's capacity, how should we allocate it across our customer base. It's going to be predominantly inference-based workloads for a number of reasons. One, for me personally, as a CEO, I look at who's actually paying the bill, not the start-up, but who's eventually paying the bill? Is it a venture capitalist? Or is it actual real customers? And I get super excited, obviously, by -- like whether it is a consumer or an enterprise customer that is paying the bill because then there's more durability of revenue, both for the company that we're doing business with and for us eventually.

This is a great observation. So out of the companies that you have on DigitalOcean today, I know your visibility is not perfect. What percentage of them have customers paying the bill versus these?

So increasingly, a lot more. So we have companies that are doing, for example, generative media. And they're selling to B2B customers who may have consumers at the other end of this spectrum, but these B2B customers are looking at generative media as a way to increase their conversions or increase their engagement and the depth of product usage for them. So these are great use cases for us because we know that this use case, if the customer is -- or if the start-up is able to prove the workload, it is only going to go up in usage.

The other interesting comment you made there, inference is closer to DigitalOcean's DNA. I think you expanded on that a little bit already. But what do you mean by that?

Yes. So there are multiple reasons why we feel inferencing is a place where we have a big right to win. For a number of reasons. I'll start with the most obvious one, which is inferencing is a lot more than just GPUs. Yes, GPUs are a big part of inferencing. But when you talk about inferencing, you need the raw horsepower to leverage an LLM, whether it is a closed source model or an open source model or a serverless inferencing of either of these 2 models. But more important than that is in an inferencing mode, you need to pump in some custom data. You need to process -- preprocess the data and post-process the data. You need a way by which you can build some of the other higher order services around it, like you need to have guardrails. You need to evaluate both in real time and also offline, which model is the best suited to serve the needs of your customers. I gave the example of a free customer and a freemium customer, but also different types of use cases might require different parts of the same LLM even, right? We are now starting to see from a same LLM provider, different models are great at doing different things. So how do you do that in real time? For many companies which are not super sophisticated AI native, they also want the ability to start building agentic workflows from a template. They also want the ability to have multi-agent orchestration. So you need -- you need a way to have sophisticated routing agents and traceability and observability. So all of the things that we have done on the traditional cloud are very important when you're running inferencing at scale. So for all those reasons, a lot of our customers have started -- they come for the inferencing needs, but they stay because we are a full stack cloud because they start leveraging the other stuff. They don't have to go to multiple cloud providers. And the fact that in our new data center, these 2 stacks live side-by-side in an integrated fashion is a big, big deal for them.

Turning to the broader business. You've had a lot of success growing your Scalers+, your largest end cohort, now 25% of the portfolio and growing 35%. What are the specific product features and enhancements you've invested behind to accelerate this momentum? And where do you see the gaps that you need to fill to continue?

I'll start with the boring answer. There's not 1 or 2 features. It's a collection literally of about 250 features we have released over the last 4 quarters or so, right? And if you look at the number of business days in any given quarter, we are releasing a major product update almost every business day. And we can categorize some of these things to say -- and for those of you who are new to the DigitalOcean story, when I came on board about 20 months ago, one of the biggest themes that was highlighted was the fact that customers grow to a certain size of footprint on the DigitalOcean platform, and they're forced to graduate because we didn't have certain types of functionalities, right? So you can group those functionalities into core compute. We had a couple of types of "droplets" but we didn't have a lot of different flavors of them in terms of some use cases need memory-optimized droplets. Some use cases need compute-heavy droplets or storage-heavy droplets. We have fixed a lot of those things. We have a variety of different droplet options, even to the extent now we have an inference optimized droplet powered by GPUs. So that's on the fundamental level. Our storage was also lacking a lot of high-throughput storage, input output, different types of network attach storage and things like that. So that was another big hole. Our networking stack was fairly basic, and that was a big area of focus for us over the last 6 months. We have added several features, including virtual private cloud and also direct connect between our data centers and the hyperscaler data centers. And that's been a big hit because we don't charge extra for that, but that has been a big hit with our large customers, especially Scalers+ because now we can go make a pitch to migrate a part of their existing workload, like it doesn't have to be all or nothing. And our big customers absolutely love it because they love many parts of the DO platform, but not all parts of it. They want to have a multi-cloud strategy even within a given workload. Now they can tastefully pair us with an existing workload running on GCP or AWS. So that's another one. And final thing is it's an evergreen area where we are investing a lot of bandwidth, which is our database offering. And I think that's going to continue over the next year or so. In a couple of weeks, we have our product conference in London. We're going to make a series of big announcements, and it will be in compute, storage, networking, database and everything in between.

And you've traditionally relied on a PLG motion. As you're expanding to these larger customers, how are you leveraging partnerships like with Hugging Face or channel partnerships to grab a larger portion of the market there?

Yes, it's a good question. So our product-led growth has been a major driving force in -- from the founding days to now, right? But over the last couple of years, it is -- it was showing signs of fatigue, if you will. But last quarter, we talked about how we had one of the best quarters ever in terms of our month 1 to month 12 cohort. The reason why I get super excited by that is today's M1 to M12 is tomorrow's NDR. And I obsessively look at the quality of customers coming in, in terms of their ARPU and their velocity of progression through the M1 to M12 and how quickly they're attaching other DigitalOcean products. So on top of that, we are now -- we have never had a proper sales-led growth motion. When we had good sales, we didn't have great products. And when we had good products, we didn't have good sales. But now we have a good 1-2 punch, and we are now packing a good amount of wood behind the sales-led growth arrow. So that will be a big theme for us next year. On top of it, we are starting to open new front doors through which customers can come in. One obvious one is our AI front door has been great in getting more customers attach our traditional DO stuff. But you also mentioned a couple of other partnerships. One partnership that hasn't gotten a lot of publicity is because we haven't really launched it yet, but we talked about it at the conference, a company called Laravel, which is the most popular PHP framework in the world. Their founder talked about how they're launching their BPS offering exclusively on DigitalOcean. We have, I don't know, how many thousand people in the waitlist for that. We are going to be releasing that in the next couple of weeks. So we expect that to be a massive front door. So our partnership is not restricted to 1 or 2 companies, but we are looking at the open source community at large to drive a lot of traction to us. So that will be an evergreen motion in terms of our partnership, both on the core cloud as well as on the AI side.

And on the AI side, can you talk to us a bit about the breakdown of the platform currently? What percent of your customers are leveraging the infrastructure versus the platform and eventually the agentic? Where do you see this going over the medium term?

Yes, absolutely. And I'll maybe just do a continuation of the previous answer, which is on the AI side, the AMD Developer Cloud is another big front door, which is powered by DigitalOcean. So we continue to expand the footprint of how companies and developers can enter the DigitalOcean family. Specifically talking about the AI stack, I think we have talked about -- and we largely borrowed the inspiration from the Goldman Sachs framework of IPA, infrastructure platform and agents or applications. And from our infrastructure point of view, it is both NVIDIA and AMD are the big GPU offerings. But on top of these GPU offerings, we have a couple of layers of abstraction. Of course, we offer bare metal compute, but increasingly, a large percentage of our customers have started using our droplet architecture. And our droplets are very sophisticated in what they provide in terms of taking away all the brain damaging work of putting the right frameworks and making sure that they are working and stuff like that, but also more sophisticated orchestration and life cycle management of these instances, which if you worked with GPUs, it's not very sophisticated, right? Then there's a lot of breakage and you have to do a lot of baby sitting in terms of the life cycle management. In addition to all of this in the infrastructure layer, we are also building some inference optimization logic, both ourselves as well as working with some partners. We are building inference optimization, including the GPU inference droplet that we created. So that is an ongoing R&D effort. So we are building a lot of IP on that. The next layer is our Gradient AI platform, which is -- starts with serverless inferencing of both closed source as well as open source models and also all of the other building blocks that I rattled off all the way from a model playground to TCO calculations on different LLM throughputs to agentic building blocks of multi-agent workflows or agent evaluation, agent traceability and so forth. So all of these building blocks is what we call as Gradient AI platform. So typically, the users of these 2 layers are very different. AI native start-ups typically want access to GPUs. SaaS applications and traditional software companies want access to serverless endpoints or the agentic framework directly because they're not trying to take GPUs and start building from scratch, but they are introducing AI as a feature into their platform. So to finish my answer, most of the revenue today comes from AI infrastructure layer, but most of the mind share adoption and thought leadership is in the middle layer. Will that invert sometime in the future? Yes. When? I don't know. But we already have 6,000 unique customers using our platform, more than 15,000 agents deployed at this point. But a lot of them are in proof-of-concept mode. But at some point, over the next few quarters, that will invert, and we are really looking forward to that.

And you announced the general availability of your Cloudways Copilot. Any early feedback from customers, what you're hearing on adoption and...?

Yes, it's been a big hit. It's been a big hit. It's just amazing. See, our -- you have to realize our Cloudways customers are -- some of them are technical, but generally speaking, are not very technical, right? They're hosting websites or their digital agencies and things like that. And they typically have shared IT resources. They're not babysitting websites day in and day out. So for them, having an agent that is doing the job of a human is a welcome addition to their fleet because they're not there looking at the varnish cache of their WordPress deployment all day long. So the more automation we can provide in terms of just the observability and monitoring of the health of their website is super welcome. But the next step of actually taking remediation on stuff before it actually goes wrong is an absolute winner for them. And we are getting more than 95% accuracy in terms of our ability to predict that something is about to happen. And in addition to the cloud-based Copilot, we're using the same technology internally because I mean, obviously, we have a massive cloud footprint and with any massive infrastructure at this scale, things go wrong all the time. And it has reduced our time to respond and mean time to respond and mean time to remediate by like 30% to 40% in most cases. And we've still only opened up 3 or 4 use cases internally and the productivity gains are just staggering.

So you made a really interesting comment there. AI native on GPU access, traditional SaaS companies on servers and more edge compute. The question for you is there's a debate in the market today on whether AI native companies can disrupt traditional SaaS [indiscernible]. And one of the parts of the argument as well, the tech stack is fundamentally different for an AI native company [indiscernible]. Would you agree with that? Or is there a valuability here?

I think I'm more on the side of over time, the AI natives are going to disrupt the traditional software companies, and there is a parallel stack emerging. And even in the AI native companies, and I should probably qualify this a little bit more. AI native companies that are more infrastructure-oriented need raw access to GPUs. But I was talking to an AI native company that is building contact center software. They don't want access to raw GPUs. They want serverless endpoints, because they're like -- yes, but they want cheaper serverless endpoints, but they don't need access to raw GPUs. What they need is our high-quality tokens and tokens out because they're doing voice to text and things like that. They want us to do the heavy lifting of, hey, I'll give you the model or I'll point you to the model, you host it, you manage the life cycle and just give me API access to it. So I feel there is a parallel like observability, for example, right? Age-old problem. We've been doing it since the mainframe days. But the way you do observability for a pure end-to-end agentic stack is very different. Like what you observe and what you take remediation on is very different from what you observe and take action on for a traditional cloud stack. So I think there is a parallel stack emerging. And it is also nuanced in the sense that the more sophisticated AI natives that are building raw infrastructure or doing media manipulation and those kinds of things need access to raw GPUs, but AI natives that are more in the business realm or building business workflow software are tilting more towards getting access to endpoints in a serverless manner.

Yes, fascinating. And you talked about this at the beginning how more of the mix is now customers that are actually paying for the business model [indiscernible]. This is a question about the quality of the revenue of some of the AI in the startup. To what extent are you still seeing a lot of stopping and starting a lot of experimentation such that it's not -- you can't measure NRR the way you typically measure, or are you starting to see the quality of that revenue is not [indiscernible]?

Yes. So I would say maybe about half of our revenue is very -- it's getting to be predictable because of this inference workloads.

Of the AI native companies?

Of the AI -- yes. So the AI native companies, and we are trying to get slightly longer-term commitments from these customers because we have limited capacity in terms of our fleet -- inference fleet. And part of my business development activity with these companies is, hey, give us more visibility. Can you give us 6 months? Can you give us 12 months or 18 months? And the more mature these AI native companies are with their inference workloads; they are willing to now give us visibility into their 12-month run rate because they know what the price performance or number of tokens required are to serve their customers. And they have -- they have a prediction in terms of how that is going to look like in terms of their end user adoption and the number of tokens required to service and are able to give us some visibility in terms of what their needs are. So I think it's still early days, but we are starting to see that for sure.

And I know we debate this with Matt every quarter. The question on the demand environment, the visibility that you have in the company because you do tend to service more of an SMB developer around the customer. Would love to hear your thoughts, how do you feel about the health of the demand environment for both that -- look, you [indiscernible] what about the cloud?

I think they have been more resilient than I initially thought during the very turbulent April time frame. It has been fairly resilient. And I would also say that it's less about some of the macroeconomics going on globally, but there's a lot of microeconomics from a country-by-country perspective that we see. But in terms of the demand environment, we are not seeing anything unusual at this point.

How about from a competition standpoint? I really appreciate that at the beginning of this conversation, you were talking about the inference workloads being close to your DNA as a company and the unique value proposition that you have. As investors, we spend a lot of time hearing from new cloud providers that are addressing inference workload. So maybe drive that for us, are you seeing a change in competition for that AI native cohort versus the cloud [indiscernible]?

I think we're -- I don't know if it has really changed that much in the last 6 months. It's the same names that we keep seeing. So it hasn't really changed, but these are the same NEO clouds that you're probably hearing from as well. But I think there is definitely a more nuanced appreciation from our customers in terms of some of the other building blocks that they need, whether they -- we are seeing a lot of companies approach us. So there's -- the concept of multi-cloud inferencing is also picking up. So it is not -- so we have many customers for whom we are not the only cloud. So they are -- they may start their journey from a hyperscaler for whatever reason, they don't have capacity or they don't have certain things available and they come to different NEO cloud, they come to us. So I think the -- the concept of going from a single cloud to multi-cloud, probably in classic cloud took 10 years for multi-cloud to really come to fruition. But in AI, we -- it feels like it's already there from -- for inferencing.

Turning to the balance sheet. You've historically invested around 20% of revenue in CapEx and 15% of that towards growth and 5% towards maintenance. How are you thinking in the medium term as you look to your Investor Day targets to reaccelerate revenue? Is there any near-term influx that's needed on the growth side of things to support this investment? And how are you thinking about additional funding tools to do so?

Yes. It's a really pertinent question given what we're seeing. So in the -- on the Investor Day, we said this is -- this has been our historical run rate, and this is the split that we are used to. And I think that's largely still true. But we're getting more and more confidence that we will not be afraid to invest behind durable growth backed by companies that are seeing real customer traction. Now we also talked about the fact that just like other companies in the market, even some that came out earlier this week, there are multiple tools that we will leverage as part of our tool belt to support our growth aspirations. And the key thing here is to say it needs to support our growth aspirations. If there is a way to accelerate our growth or get to our growth aspirations faster, or any combination of those 2 things, we will absolutely invest behind it, and we are starting to look at some of those things as we are picking up momentum and traction with these AI native companies. And not to sound like a broken record, but the more true inferencing workloads that we can see with durability attached to them, the more conviction we will have to invest behind those workloads and those companies. And part of our mandate is to go allocate our resources from a compute perspective behind these companies that have real enterprise and consumer use cases behind them. So just by the nature of inferencing, it just takes out a huge piece of uncertainty behind like are they going to be viable in 6 months? Because if they're doing inferencing at scale with thousands of GPUs today, somebody is paying money in exchange for value. And that is a big validation for us, and that gives us more conviction to invest behind this.

What does the pipeline look like for the $20 million plus multiyear type deals like the one that you announced recently?

The pipeline looks healthy. Pipeline looks healthy. Part of it is with companies that we seeded and are finding traction. We are getting very active in the start-up community with the concept of, hey, this was something that DigitalOcean was great at. And you won't believe the number of people who come up to me regularly when I'm walking around with the DO T-shirt in an airport saying, hey, I'll learn how to code on Ruby on wing from DO. Yes. So we are trying to get back to that DNA with AI companies. Even last night here, we sponsored an event called Founders You Should Know, which is a very small, curated set of founders. And these are really successful serial entrepreneurs. And we are getting back to that route of seeding DigitalOcean as the place to start their AI journey, not just their cloud journey. So I feel really good about that.

And in August, you completed an offering of the $625 million convertible notes to -- in part retire the 2026 notes. Can you just bridge the gap between the remaining 20% to retire and how this is going to impact DigitalOcean's funding structure in the...?

Yes. I think we are in a really good place. I was just joking that I don't -- I'm so glad I don't have to take more calls on converts. So glad to have that. So I think we have a little bit of stuff left over, but I mean we are in excess of 40% EBITDA. So we throw out a lot of cash, and it gives us a lot of optionality to do all of the things that we just talked about, and we have a significant amount of runway between now and end of the next year to take care of this. So we have a very high degree of conviction that this is behind us, and we have multiple degrees of freedom to pursue.

I wanted to ask you a little bit more about product-led growth in [indiscernible] we had Canada, we have Versal, we have HubSpot conversations recently, where they both said that you sort of evolved from search engine optimization to AI engine optimization. I'm curious what you're seeing in terms of lead generation from LLM inferences and how you think about what positive growth looks like under that pyramid?

Yes. That's a great question. So we are -- obviously, we spend a lot of cycles tracking this. So the movement from SEO to GEO is real, and we are seeing it every day with tweaks to the Google algorithm and things like that. It makes a huge difference for us. I'll start by saying our M1 to M12 has never looked healthier. It's an amazing engine for us. And SEO and our SEM spend is fairly miniscule, really small, like single-digit millions is what we spend. So it's not a big driver of our PLG motion. Our PLG motion is driven by community, our open source involvement, organic search, branded search is a very small part of our overall strategy. We're also starting to get a disproportionate amount of our sign-ups from LLMs. But it's still early stages. We are getting a disproportionate amount of our sign-ups from LLMs, but their conversion rate and their ARPU is something that we are monitoring and tracking. Like are they coming here to do something serious? Or are they kids and students that are here -- yes. Sorry?

Experimenting.

Experimenting, yes. So we're looking at all of that. It's still very early days. And even for our product-led growth motion, we have multiple front doors, right? And the open source community is a great example where we get customers coming in from different open source frameworks into our PLG motion and then they become super entrenched customers of ours. So unlike some other companies where SEM -- like even in my previous job, we used to spend like tens of millions of dollars to acquire customers through Google. That's not the case with us here at DigitalOcean. So I feel it is an important part, but it's not the most important part of our PLG motion. We have multiple feeding points into our PLG motion. But it's a really fascinating place where we're starting to see a significant impact of the Google Search algorithm and how we are bringing in customers into the funnel. Luckily, we have multiple bites of the apple, like we don't rely on search engine marketing to drive the top of our funnel.

Really fantastic color. Thank you for your time. [indiscernible].

Thank you very much. Appreciate it.