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

Perfect. Thank you. So thank you, guys. Thanks, everyone, for attending. Really excited. My name is Mark Zhang. I cover cybersecurity and infrastructure for Citi Equity Research. Today, we have the pleasure of having Paddy Srinivasan, CEO of DigitalOcean. Thank you so much for attending, Paddy.

Thank you, Mark.

Really appreciate the time and insights.

So maybe just to kick it off, Paddy, you've been with the company for, call it, 2 years and change. It's been really just very focused on turning the company around in terms of profit portfolio go-to-market. Just want to give us a sense of what's changed over the past 2.5 years? What stayed the same? And how you're thinking about like going after this AI [Technical Difficulty]...

Yes. Thank you, Mark. First of all, it's wonderful to be here. This is turning out to be quite the conference, a lot of energy, a lot of familiar names. It's just amazing. Truly amazing the times we are living in, and it's an honor to be here. Yes, it's a little over 2 years that I've been at DigitalOcean. It's been a lot of different things, some planned, some unplanned. But when I joined the company, I've been in the developer ecosystem for 30 years. I started my career at Microsoft and did a few different things, including a start-up also in developer ecosystem. So when I got the opportunity to engage with DigitalOcean, it was -- I was super excited because we are a relatively young company. We're only like 12, 13 years old, 5 of which has been in the public market, relatively young. And over the first 10 years of the company's existence, we built an iconic developer cloud. Like developers absolutely love us even today [Technical Difficulty] with developers. We have 650,000 paying customers, and it just keeps going, and we get tens of thousands of customers signing up on a monthly basis. So that machine and that goodwill and the credibility we have with developers is at constant. What has really changed? Number one is when I joined the company, we were at approximately $600 million of revenue. And there was one big problem that the company was facing, which is our top customers were unable to scale with us to go take their workloads to other hyperscalers to be able to keep growing and scaling their more sophisticated workloads. And that was my #1 focus to fix because at the scale that we were in at $600 million, it was really impossible for us to keep our growth rates accelerating [Technical Difficulty] customer acquisition energy to [Technical Difficulty]. So that was the first thing that I wanted to fix, and we fixed it by really focusing on the fundamentals of our product. And for the first 4 quarters, I was repeating the same theme. This quarter we shipped 40 features, 50 features, 60 features. And here's the impact, here's the adoption. And then slowly that turned into business outcomes. Over the last -- in the last earnings call, I talked about how we've taken what was a questionable even a weakness of the company, and we have turned it into an absolute strength of ours. Our top customers are our growth engine now. Our $1 million customers -- $1 million-plus customers, $500,000 plus customers, $100,000 plus customers, they're all growing significantly faster than the market growth rate. Our $1 million customers are growing in triple digits. Our $500,000 and $1 million customers had 0 churn over the last 4 quarters. And by any measure, they have become a big reason why our growth is reaccelerating. And that was purely a function of [Technical Difficulty] mention, Mark. We made a bet on AI. And when I was getting started 2 years [Technical Difficulty] generation ago in terms of AI, it was just getting started [Technical Difficulty] large training clusters. We made a conscious decision to not go after that primarily because it was really not aligned with who we are as a company. What we know how to do is build great software, take very complex concepts, make it simple, accessible, affordable, predictable for our customers. And inferencing -- when inferencing started emerging, we felt like it was really in our wheelhouse. It played to our strength, and that's why we decided to double down on inferencing over the last 6 months or so. And the results are here to see. In Q4, I announced $20 million of AI customer revenue, growing at 150% plus year-over-year and just getting [Technical Difficulty]. And 70% of our AI revenue is non-bare metal, which is the exact opposite of all the other Neoclouds. You see it's mostly bare metal services, whether it is mostly training, but also inferencing is all bare metal. For us, it's the exact opposite. And it really enables us to build a very different financial [ profile ] as we scale our AI track.

Great that you brought in the AI interest and opportunity. I think the AI [Technical Difficulty] around the industry quite [Technical Difficulty] -- I think if we really parse through the various levels of inferencing, right, there's various opportunities. How do you see sort of DigitalOcean's opportunity in inferencing? How do you differentiate yourself from, let's say, the other providers, even outside the Neocloud. The ones that you're trying to put in more managed services and [indiscernible] AI inferencing. How does DigitalOcean play? And how do you sort of see the longevity of your position within just AI inferencing?

Yes. Great question, Mark. So I will answer this question purely [Technical Difficulty] perspective as a platform provider. So I mean, obviously, there are different types of customers, starting with individual users, we all use [Technical Difficulty] for OpenAI and things like that in our daily lives or open clock. So that's one set of users. The second set of users are enterprise companies like a Citibank or Walmart or Eli Lilly, those kinds of -- or a restaurant chain. Those are end user enterprise companies. The third category, which is our ideal customer profile, our target customer profile are cloud-native companies or AI native companies. In the old world, they used to be called independent software vendors or ISVs [Technical Difficulty] building and monetizing technology. That is their business model. So we are [Technical Difficulty] only on the last segment, which is our [Technical Difficulty] majority of them digital native enterprises or cloud-native companies. And now [Technical Difficulty] from AI native companies that are using our technologies [Technical Difficulty] -- it's really important to understand that. So in this category, there are 3 types of [Technical Difficulty] business, and they're trying to introduce or they're trying to have a heavy dose of intelligence in their existing application, whether it is a SaaS application or something else. That's Category 1. Category 2, our AI-native companies that are reinventing a category with an AI-centric approach. So this could be a new travel management application, not Concur, but the next generation of Concur that is built using an AI-native approach, right? So that's the second type of company -- companies that says. And the second type is still having humans as the primary persona with a heavy use of intelligence to support the human workflows. The third type of companies is what I call as agent-native companies. These agent-native companies are just emerging, and they're taking a very different approach to say, why do humans need to be involved for a travel management application as an example. Agents can do 90% of the work and 10% of the work will require a human in the loop. So for all those 3 types of customers, they're all intelligence hungry. They need a lot of inferencing, and that's who we are. We are an inference factory, and I'll get into the details and you ask me how we differentiate and things like that. I'm happy to get into the details, but we are an inference factory. Can you get inference tokens by renting out a GPU and building everything? Sure, you can. You can also visit a lumber yard, buy timber and build your own cabinet. You can do that, too. But you come to somebody like us to get smart intelligent inference tokens so that you can focus on building your travel management application rather than having to go manage the life cycle of a GPU and things like that. So we are a platform provider that produces intelligent inference tokens compared to a Neocloud that is still in the business of renting hardware.

That's a great overview. I think like certainly, the space is evolving and even the Neoclouds are providing or creating their own managed services and layering on top of the GPUs. So how do you sort of like think of the competitive landscape in terms of -- how do I protect my mode? How do I sort of like continue on with providing this platform as [Technical Difficulty]...

So before I answer that question, Mark, let me explain to you with these kinds of new generation of applications, how is that translating to a new stack, right? Because when I look at the stack that we have built over the last dozen years, a lot of it is applicable in the new world, but the new type of applications are also demanding a complete relook at the stack that we are providing, right, as these companies scale. One very, very important thing we have to internalize is for an AI native or a cloud-native company that is building an intelligent application, they are carrying the cost of inference in their cost of goods sold. It is a COGS line item for them. And if they are not mindful, they can very quickly get into a negative gross margin territory. Like there's no business to be scaled, right? I think that's a really important distinction compared to a personal use case where you can put some guardrails on how much you want to spend and things like that or even an enterprise that is spending, like, let's say, I have a $100,000 employee, you can say, I want to spend $20,000 to augment that human employee with tokens and things like that. But for an AI native company that is building and scaling an intelligent application, this actually sits and drains your gross margins. And if they're not careful, it just -- there's -- you cannot build a business, right? And we have a lot of companies coming to us saying, "Hey, we started, we wanted to nail our product market fit. We started with a closed model, and we have figured that out. Now I'm scaling. And if I don't do this, I'm going to be -- I don't have a business to scale. And we have this combination of open source models and open source harnesses, and we're trying to stitch all of these things by hand. Can you help us automate some of these things?" This is how -- 6 to 9 months ago, this was the starting point of our new inference cloud is. We started observing and working with companies that are trying to do this at scale with -- in a post-product market fit stage. And that's when we said we have to really rethink how we are doing this. And we started investing very heavily in a couple of different layers. So at the bottom most layer we have infrastructure just like any other Neocloud, right? On top of it, we have a fairly sophisticated set of compute primitives that sit on top of GPUs, CPUs and storage and things like that. So we have what we call as the GPU droplet, which is a virtualized instance of a GPU. We have core compute. We have many other artifacts that you would expect from a real cloud. But then we have built 3 other layers, which are very new in this new world. First of all, intelligent applications start and end with feeding real-time data. So this whole concept of, oh, I'll generate a lot of exhaust and capture data, and then I'll pass it into a different system where I'm going to do an ETL and get the data prepped and I'll do all the processing and then feed it back to the application, that is dead. Like we now are having customers that want real-time access to analytics and data fed into a reinforcement loop so that RL as a service is improving their application and the model that they're working on in real time. So there is a reinvention of the data layer that is happening right as we speak. The layer above that is what we call as the inference engine. Inference Engine has two parts. One is we have a very rich list of models that we host. And the models are both open source models that we do pass-throughs and enrichment of, and we also have dozens of open source models that we offer day 0 support for. And what does the day 0 support mean? We have the ability to take these open source models and optimize them at a kernel level to make sure that the efficiency of tokens is top notch. So when you are paying for tokens, you have to get not only the best quality tokens, you also need to get the best efficiency of tokens. And that is an opportunity for us, right? So that's on one side. The other side is there are dozen or so services for an inference engine, including things like quantization because you're training your model using FP16, but when you're running it, you're doing FP4. So you have to quantize the model. You have to do -- you have to evaluate different types of models at real time. You need to do guardrailing of your models. You need to have many other primitives to make sure that your models and your -- the quality of your inference output is really top-notch. And then the final layer that we are working on is the agent life cycle layer. Agent run times, agents are very idiosyncratic in the sense that you cannot -- you just simply don't have the time to hydrate and dehydrate virtual machines. You need something a little bit more lightweight. So you have this emergence of this new thing called agent sandboxes and so on and so forth. And agents also need persistent memory. They also need [ formal ] memory and things like that. So there's a whole suite of capabilities to run agents. So these are the 5 layers that we are inventing as we speak, working with our customers to serve the needs of this new AI native application stack, right? So I'll come -- I'll now answer your question in terms of how are we different? We are different because a Neocloud has the bottom most layer. They have phenomenal infrastructure or data center operations, and they have GPUs and CPUs available for rental. But then if you are a company that wants to build, let's say, a new healthcare application with AI native or AI-centric healthcare application, you have to go reinvent all of the other 4 layers. Even if you are one of the inference providers, you get one of the layers, but you have to go build and scale your data layer on AWS. You have to go to a provider like DigitalOcean to get access to raw GPUs or CPUs because most of these agentic applications need GPUs, but they also need a lot of CPU firepower to be able to preprocess and post-process workflows. So there's a lot of incompleteness with the Neoclouds and the inference providers in the market that they have very small pieces of the puzzle, but not [Technical Difficulty]. The hyperscalers surely have it, but the hyperscalers are busy building it for the [ Citibanks ] of the world where it is really hard to do that and also make it so simple that an AI native company can come and start consuming it in a matter of [Technical Difficulty] what we try to do. So a long-winded answer to your question, but the [Technical Difficulty] inferencing and agentic applications, we are reinventing the stack because the old stack as important as it is, it is simply not enough to be able to build and scale these new types of [Technical Difficulty]...

[Technical Difficulty] So across 5 layers opportunities, maybe we could like think about it from a product road map standpoint. Is it -- where do you see sort of the low-hanging fruit of media like demand and opportunities for you to expand your TAM and expand the opportunities with your current customers? Is it -- should we think of it from just a bottom-up approach? Or do you think from a product road map standpoint, where the key opportunities are [Technical Difficulty]...

Yes, great question. So we see the opportunity as we are in a very secular tailwind situation here because it doesn't matter what type of application is being built. Future of all technology is going to include a heavy dose of intelligence. And we are in the business of providing tokens, intelligent tokens and efficient tokens. So if you think about the currency of new types of applications is token-based. It's no longer seat-based. And a lot has been written about that. So I'm not breaking any new news here. But what is important is that the business model is also fundamentally changing from -- for us, it is changing from charging for inputs, which are GPU dollars per hour, to changing for output, which is tokens and the quality of tokens. So as a platform -- inference factory platform provider, for me, my margins are going to improve if I can figure out how to produce more intelligent tokens because I can monetize them significantly better. And I can get more token efficiency and token throughput for the same GPU I'm deploying compared to my competition. So those are the 2 things we are really focused on. Now coming to your question, Mark, where do we see the most opportunity? We see the most opportunity as the inference provider for tech companies that are modernizing their application, injecting intelligence. That's number one. Number two, we see an entire ecosystem that has already emerged in Silicon Valley that are very compute hungry and I should say, inference hungry and more and more demand that we are seeing today is for inference token consumption rather than GPU consumption. So I'm beating up on my sales team to say, as soon as when a contract is up for a GPU, shut it down, move it to my on-demand and inference pool so that I can monetize it in a very different fashion. So the fact that we don't have a lot of super long-term contracts is an absolute blessing for us because I can take that pool and redeploy it and monetize it in a very different way and using a very different currency. The third thing is we are seeing a lot of traction starting to appear on the agentic space where agents are not only intelligence hungry, they're also compute hungry because the nature of agents is that they're autonomous and they're goal seeking in the sense that they will continue to go in infinite loops until they get the job done, which requires intelligence, yes, but they require an enormous amount of compute. And I mean, obviously, we've all seen the announcement from Claude on managed agents and things like that. And I have a point or two to make on that, Mark, if you will allow me.

That's the next question.

So because there's been a lot of questions around it, right? And I've been doing this for 30 years, so I've seen a pattern or two. And I want to start by saying closed source systems validate the category. Open source systems scale the category. So it is fantastic that Anthropic is validating that, hey, agentic apps are here, and this is how you deploy it. If you're an individual user, you're obviously -- that's a great place to do it rather than messing around with an open claw and things like that. Even if you're an enterprise and you are willing to make a bet on a closed ecosystem, that's also great. But our target customer profile is somebody that's building a business on intelligence. And it is -- as we have seen over the last 30 years, it is really hard for an ISV or an AI-native company today to bet their business with a closed ecosystem. Our top 10 customers, literally nobody is a single model company. Literally, everyone is using some closed source models, but a lot of open source models. That is how these AI native companies are scaling. And that, to me, is a great opportunity, whether you're building an agentic system or just developing a new AI native application with a lot of inferencing or you are an old school company that is trying to inject intelligence into your application, all of them are inferencing hungry. All of them are compute hungry. And I feel like I'm an arms dealer in this massive replatforming that is happening, and we cannot provision compute and intelligence fast enough to serve the needs of all of these use cases. Agentic workloads are just one of the use cases, and they need a lot of intelligence. They need a lot of compute and a lot of agents are not going to be built within the 4 walls of a closed ecosystem. There's going to be a tremendous amount of open source across the 5 layers that I was talking about, right? There's open source in every layer that we support, and it is amazing to see the emergence of open source in pretty much every aspect of this new computing stack.

Yes. No, that's terrific. Thank you for demystifying some of the Frontier lab announcements. Maybe to that point, we're -- maybe, I guess, at the realm of the next frontier lab announcement. But from your purview and what you see from what they've done so far, what do you see sort of the risk to your, call it, the 5-layer stack of the inferencing opportunity? Yes, there's open source [Technical Difficulty] risk that you see Frontier Labs making in terms of like their splash within that inferencing opportunity?

Yes. So the Frontier Labs announcements or generally the model threat is at a layer above where we sit. We're still in the business of providing essential infrastructure. The application space is where I see most of the disruption happening. Will some of these model companies also announce different parts of the inferencing stack or some of the infrastructure? Sure. But to me, it is all great validation. So for example, my first job out of college was working at Microsoft writing kernel level code for Windows NT operating system. And if you remember, like early -- fast forward a few more years, early 2000 [Technical Difficulty] servers had a dominant market share. But another 5 years or so, Linux started taking off. And today, I think it's -- 90% servers have Linux. [Technical Difficulty]...

[Technical Difficulty] how do you think of the build versus buy [Technical Difficulty]...

Yes. So that's a great question, Mark. So we definitely look at [Technical Difficulty] 3 dimensions. [Technical Difficulty] -- we are obviously building a lot of what we are shipping today. We partner [Technical Difficulty] and on April 28, we have our Deploy Conference here in San Francisco, and we'll be talking about a tremendous amount td for the last 6 months that we'll be announcing [Technical Difficulty] -- like you said, Mark, we just did an acquisition of a technology company called Katanemo, and we will [Technical Difficulty] for ways by which we can accelerate our product road map. Like we cannot ship these features fast enough. Literally, our top customers are snatching these features from our hands and because they don't want to sit around messing with infrastructure. So I'll give you one great example of the company that we just acquired, they have this technology called Plano AI. Essentially, for the last 6 months, we've been working with some of our customers. And this is why I keep going back to the fact that none of our top AI native companies are single model. In fact, we have many companies that have clusters of models that are looking at incoming prompts. We have a company that uses 31 different models to process a single prompt. And that is not an outlier, by the way. We -- most of our companies -- most of our customers have at least half a dozen to a dozen models that are processing incoming -- single prompt is processed by a cluster of models, right? And then we have some companies that are saying, hey, can you actually do this routing of traffic to the right model at the right time for a combination of accuracy, cost, latency and throughput? Can you come up with some heuristics either that we can [Technical Difficulty] actively or you come up with dynamically at run time in a matter of milliseconds and route it to the right model at the right time because inference quality matters, inference efficiency matters even more. So these are kinds of features that we started building and then we came across this piece of technology. We quickly moved to acquire them. So we are always scanning the market to partner with great companies and eventually, if things work out and if it is the right thing, we'll acquire as well.

Terrific. That's great to hear. Maybe just to wrap it up and ask a final question, I'm sure all investors are interested in this question is how does the [Technical Difficulty] change monetization? And how do we think of the unit economics and the opportunity?

Yes. So from our customers' perspective, as I said, they're moving into a token based [Technical Difficulty] smarter, no credit to us, like we haven't done anything, but we are very comfortable with consumption-based pricing model. That's what we have done for the last [Technical Difficulty] -- as I was mentioning, the -- we are moving from an era [Technical Difficulty] per hour. The vast majority of products are moving more and more towards an outcomes-based, token-based pricing so that they can market up and [Technical Difficulty] companies are consuming tokens, enriching the tokens and providing outcomes to their customers. So as long as we are in the path of value creation and align our unit of consumption and our business model to the unit of consumption and the business model of our customers, we're going to be in a great shape. But as I said, we have had 12 years of experience being in this consumption-based pricing model. So I feel really good that finally [Technical Difficulty]...