All right. Good afternoon, everyone. Thanks for braving another beautiful, wonderful 80-degree day here in Boston at the TMC Conference. Look, I think this is gonna be a really exciting panel this afternoon, because I can think of very few public companies better positioned for the broad trends that we're seeing around inference, developer adoption, and the future of the AI landscape than the DigitalOcean team, who we're privileged to have here with us today. Just quickly, my name's Kevin Curtin. I look after the AI Infrastructure investment banking business at J.P. Morgan, and it's been our privilege to have been aligned with DigitalOcean since their IPO in 2021.
It's very clear, given the recent share price performance and investor reaction, that the market is just now discovering a lot of the capabilities that, you know, we've seen for a long time were in this platform, only accelerated by AI. It's a privilege to have here today both Paddy and Matt, CEO and CFO respectively, for a very great discussion. We've got about 35 minutes. I've got a list of questions prepared. To the extent you all have questions, hopefully you've heard from the conference organizers how to get those up digitally. I'll be continuously taking a look at this thing throughout, but we'll also, of course, leave some time for audience questions as well.
With all that said, thanks again for joining us for the session this afternoon. The first question, Paddy, Matt, you guys have noted that global inference traffic is expected to grow 10 times by 2030 and that DigitalOcean's AI ARR is now 80%+ non-bare metal, so clearly differentiated from some of the other public neo clouds. How does your software-centric approach to inference differentiate your margins and stickiness compared to other GPU rental businesses?
Great. First of all, Kevin, thank you so much for hosting us. It's an annual ritual and a very short commute for me personally.
Yeah.
I always enjoy this conference. Great question to lead us off with. We embarked on this strategy for two specific reasons. One is obviously the financial profile, and the second one is just a self-reflection of what we are good at and what we are what we want to focus on. What we have been really good at over the last dozen-plus years is taking complex infrastructure concepts and making it super simple and accessible to developers at different stages of their respective life cycles. That's what we were really good at in the Cloud 1.0 era, and that is the the reason why we said we have a phenomenal opportunity to replicate that playbook and do it for the AI-native era as well.
When the initial stages of the AI wave started about three years ago, there was a flurry of activity in getting massive GPU farms stood up and catered to the needs of various frontier model companies that were looking to procure large clusters of capacity to train their models. We were very passive participants in that market because we knew that our strengths lay in building great software and not necessarily running data center operations or building hardware systems. Fast-forward to about T-minus 18 months or so, we started seeing the inflection of inferencing was just on the horizon, and we started learning from our AI native customers in terms of what makes a great inference stack.
Fast-forward to two weeks ago, we announced the industry's first DigitalOcean AI-Native Cloud, which is an integrated architecture of five different layers all the way from silicon to agents, all integrated into a single stack, which makes it very, very powerful. The results are there for everyone to see. As you mentioned, over 80% of our AI revenue is non-bare metal, and the bare metal component is decreasing every quarter as we publish our results. The primary reason for that is all of our AI customers are AI natives who are building and monetizing software, and they predominantly use us for inferencing. They use not just the inferencing services, but also they drag through a lot of our core cloud computing stack.
As many of these AI native applications become more and more agentic, they drag through a lot of our other parts of our software stack as well.
It's very clear the, I think, revenue growth trajectory is strong as a result of those trends you just described, Paddy. You guys recently raised your 27 growth outlook to 50% or more on the revenue line, which is a significant jump from just last quarter. What have you seen in the early utilization of your new committed capacity that gives you such high confidence in the acceleration in your business?
A couple of things. One, we added 60 MW on top of the 75 we already have online or bringing online this year. The reason why it gives us a lot of confidence to do this is we are seeing a lot of strong indicators. Our growth has been accelerating every quarter for the last several quarters that I've been here. On top of that, almost every quarter over the last recent past, we have been setting new records from a net new organic ARR added perspective. That's a great leading indicator, pretty much every leading indicator metric has been improving over the years, including the traction we have with our top customers, the million dollar customers, the 500,000 customers, and so forth.
We are seeing a lot of leading indicators that gives us the confidence to go take this capacity down because we feel with the DigitalOcean AI-Native Cloud stack that I just talked about, we have a very strong competitive mode, and our customers are appreciating it. The quality of the customers that you're seeing from us has also significantly improved over the last several quarters, and that gives us a lot of confidence that what we are taking on from a capacity point of view is something that we are very comfortable with given that demand is still far exceeding the supply from a capacity perspective.
I think in terms of those leading indicators, it's both revenue and new customer wins, right? As we heard, you know, in a really strong message at Deploy, your launch of the DigitalOcean AI-Native Cloud is enabling high-growth AI natives like Cursor and Ideogram leaving hyperscalers for DigitalOcean. How does the zero lock-in open source stack that you guys highlighted at Deploy help you to drive better unit economics, better outcomes for these really desirable customers innovating at the frontier?
Yeah, it's a great question. This journey started several quarters ago when we announced Character.AI. They also moved from a hyperscaler to us. There are multiple reasons for that. Number one is, purely from a cost performance perspective, we give them the kinds of throughput at a very low latency and accuracy that is not easy to get from other cloud providers. Like the work that we do at a kernel level, optimizing the software to ensure that we get them the type of throughput that reduces their total cost of ownership by 30%, 40% in many instances on the same class of hardware is a very compelling value proposition for these AI natives. We also have to understand that these AI natives build and monetize software.
For them, any spend on infrastructure hits their margin profile, right? The more successful they are, if they're not careful, if they make the wrong choices in infrastructure and wrong choices in the models that they're supporting, it is just detrimental to their ability to keep scaling their business. They're very aware of the choices that they're making. That's a very important part. The second thing is what we are about to witness in the general market, which is something that we've been seeing over the last two quarters, which is step one for AI natives was to introduce intelligence into their workflow. That was number one. Number two is the transition that most companies are going through is make their workflows agentic, right? It sounds easy, but it's really hard to do.
Having the ability to do both the thinking part and the doing part in the same stack is very unique to our AI-Native Cloud. There aren't too many cloud stacks where you can get intelligence to power the thinking of your application and get the right modern computing primitives to be able to perform the actions part that is required for the agentic, the new modern agentic applications. Having the ability to do both in a single unified stack is very, very powerful, and that's one of the things that AI-native companies really appreciate from us.
Yeah, you actually anticipated my next question. You know, agentic workloads as you guys see them consume 15 times the tokens and use four times the CPU power. If we're talking about building, you know, sustainable businesses, the unit economics of compute really matter. At your Deploy day, we saw a lot of really innovative features and full platform elements that you guys rolled out, like the model router, for example, that enable really preferential unit economics for your customers. Can you talk about the adoption of features like that and how much traction you're seeing with these AI natives?
Yeah. It is still early days. Deploy was only two weeks ago, but we are seeing a tremendous amount of reception for the features we just launched. For those of you who have not seen it, we announced a feature called Inference Router. Essentially it is a layer that sits on top of the various open source and closed source models. In fact, Marc Benioff was in a podcast, All-In Pod, over the weekend, and he talked about the fact that Salesforce is using $300 million worth of Anthropic tokens. He talked about the fact that, hey, there will be a company that will come and invent a layer that will do smart routing of tokens because not every task requires Opus 4.7.
I'm actually going to send him a note saying, "Hey, that company's already here. It is public, and we have actually shipped this two weeks ago." The Inference Router does exactly that. We showed a demo where we took a workload and routed all of the traffic to 4.7 on one side, and we used our Inference Router on the other side. As we went through the five-minute demo, not only were we able to see that the cost differential was at least an order of magnitude.
The second thing which was shocking to a lot of people was for many of the tasks, like writing a unit test code or doing a very simple translation of actions to outcomes, these kinds of things were significantly faster and more complete using an OSS model like Kimi K2.6 or Qwen 3.2 or something like that for a number of reasons. We are getting into a paradigm where you need some of these sophisticated tools to manage your footprint for two reasons. One, obviously the total cost of ownership or the ROI that you get, especially when you are trying to monetize software, you're very margin aware and margin mindful.
The second reason is, you also don't want to be boxed into a single model provider. We see a lot of our AI natives becoming multi-model and embrace open source in a big way. To accomplish all of these things, you need a modern AI native stack.
Yeah. If you guys haven't seen the videos, I checked them out over the weekend on YouTube. It's actually pretty cool. If you want to see more about how that works, the videos are there. Really quickly, I think the IT security here is so good, it's actually kicking the iPad off. To the extent you all have questions, I'd like to pause right now in case you've submitted them, they haven't come through, and give you guys a chance to ask. If not, I'll just keep rolling through our questions. Sorry again for those of you that might have submitted them electronically. All right, cool. We'll keep rolling. The key metric that really matters for you guys, among many, is $1 million+ customer ARR.
That metric grew 179% year-over-year this quarter, which was significantly faster than the overall business. What's driving success in retaining and growing these top customers when the market had previously worried about a graduation effect in the base?
Yeah. This is an overnight success, two years in the making. It has taken a lot of heavy lifting from our side to ensure that we identify the reasons why some of our top customers with sophisticated workloads were forced to take some of those workloads to other hyperscalers, and we methodically started addressing those things. It took us about four quarters to put a dent on that, and there are a lot of things we did from a performance enhancement, advanced networking features, fixed some of the security requirements for more modern distributed global developer organizations and things like that. There were maybe half a dozen to a dozen capabilities that were missing from the platform that we've addressed and fixed.
Over the last four quarters, not only has the million-dollar and $500,000 cohort started growing significantly, we've also seen a lack of churn there, which is really remarkable. We are starting to see the same thing happen with our top AI workloads as well. It is a very deliberate strategy from our side because we felt like this is something that is so foundational for the success of the company that needs to be addressed. This time, we want to be more proactive and address this type of graduation effect.
That's why we're really focused on addressing the needs of these AI natives proactively because the decision that they are making today when they're between $25 million-$100 million in ARR is very likely the same platform decisions they're going to stick with when they are at a $5 billion run rate. We wanna make sure that we catch them and catch them young, but have the ability to have them grow with us, with our platform.
Paddy, I'll give you a break for one and call on Matt for a question on, you know, capitalization and how you guys are thinking about your balance sheet. You ended the quarter, Matt, with $1.1 billion in total liquidity and repaid the Term Loan A in full. How do you think about using your balance sheet flexibility to secure the data center capacity that you need, the long lead equipment, making investments on behalf of customers more effectively than peers who might not have access to the same pools of capital you guys do as a now $18 billion public company?
Well, I think we've demonstrated a couple of things. One is, we're gonna make very thoughtful kinda economic decisions about the pace at which we add capacity and the returns that we generate. One of the tenets we have is, we're not gonna run the company as a public LBO. We're not gonna be highly levered and burn a ton of cash. We have a handful of guardrails. You know, our guardrails are we're not gonna run the company at above four times leverage for any kind of meaningful period of time. If we do, it'd just be because we turned on a data center and it was ramping. We're not gonna burn a ton of cash.
It was important for us then to position our balance sheet, which is now incredibly strong, very flexible balance sheet, to be able to orient our leverage towards growth. To do that, we looked at our Term Loan A, and that was a $500 million facility that was costing us about $50 million a year in terms of mandatory prepayments and interest. We said we could use that capital much more efficiently by using it to lease gear, to pay for gear over time, which has been our primary financing vehicle for the new data centers when we bring them on.
Instead of paying $100 million up front for gear, we'll pay it over four years or five years, and we'll still own it at the end. By paying down the Term Loan A, we'll pay off the stub of the 2026 convert at the end of the year. All of our leverage capacity, we can then deploy towards fueling the growth, which enabled us to add the 60 MW that we announced last quarter. That's not the end. We still have a lot of room, and we've said publicly that we're continuing to evaluate adding incremental capacity that could hit 2027. Certainly we're in the process of looking at 2028 and 2029 capacity as well. We feel really good about where we sit.
We think that we've demonstrated that we can tap into a lot of different parts of the market very effectively. You know, almost a billion dollars equity that we raised earlier this year, and the stock was actually up that day, was, you know, I think a good signal of the market appreciating us and the differentiated approach that we're taking to chasing the AI opportunity.
Maybe I'll just pause there because I know your capital structure, you know, as a public company with low leverage that's not trying to lever, you know, bare metal contracts, so to speak, is a little bit different, and pause for questions from the audience on capital structure specifically. Okay.
There's one over there.
Oh, there is one?
Yeah.
Oh, okay.
Commercial.
Hey, we might have a mic for you if in the back.
It fell. There it is. Great. Just maybe talk about going forward, given the recent equity raise, how you plan to finance these new data center capacity additions. Mostly, you know, how much is from equipment financing, how much may be from cash on hand versus other sources.
Yeah. The primary funding vehicle that we're pursuing is equipment financing. We will continue to lease equipment. I hate to use the word lease 'cause it confuses a lot of people. We will pay for gear over time. We'll pay for it over four years or five years or, if we can get it, six years, and we'll own it at the end. It's the same as buying it, you just pay over time. That's a highly effective tool for us, and we've been able to tap the OEM markets, the bank markets, and we still have some runway, a decent amount of runway to continue to tap those markets.
Beyond that, as you get into bigger quantums, there are other sources of capital that you can tap into in that same equipment financing structure. Having said that, we're not wedded to that as the only vehicle that we use to fund growth. As we demonstrated, you know, we could use equity. We've done converts in the past. We will optimize the cost of capital, and we will optimize the kinda cash burn. Sitting here with incredibly low leverage right now with a lot of growth in front of us, we have a lot of degrees of freedom. I'd say we're open-minded, and we'll be economic about, you know, what's the best cost of capital.
Right now, the equipment financing market has been very attractive for us.
Any other questions before we go on on that topic? Okay, great. NDR has recently stabilized at about 101%. You guys this quarter delivered a record $62 million in incremental organic ARR. Should we view Q1 as the definitive inflection point where your net expansion will now outweigh churn in the "legacy business"?
I think we passed that inflection point a while ago, I think, you know, the better of those two metrics is the incremental ARR. We're adding incremental ARR at a record clip. Every quarter is an elevated number. NDR for us, again, you gotta remember, we have 650,000 customers, only 20,000 of which are what we would consider to be digital native enterprises. We think about that 630,000 customers as effectively a paid freemium group. The NDR of that cohort, just by the virtue of they're indie developers, they're tiny, you know, in some cases individuals, the NDR is always gonna be something less than 100.
It's always gonna be in the mid to high 90s. That waters down the overall NDR, then it, you look at NDR, that doesn't even include any of our AI revenue. You know, for us, the better metric is to just look at the growth rate of the key customer cohorts. We disclose what we disclose on purpose. We show you the $100,000, $500,000, million-dollar customers and the NDR in each of those cohorts, which we disclosed last for the fourth quarter. I think it was 115% NDR for the million-dollar-plus customers. We're not gonna disclose that every quarter, but it's higher this quarter. All of them are higher this quarter. They're just going up.
That's what you wanna see is, are your big customers spending more with you? Are you growing your big customers? We are. You also wanna see that your AI customer revenue is growing, and it's growing 220 something percent. I mean, it's all of those leading indicators that people look at like NDR, which is a lagging leading indicator, are trying to get towards are you gonna grow your big customers? Are you gonna be able to have them accelerate? We're doing that. We're demonstrating that. We look at the absolute growth rates of the target segments much more than we look at the NDR metric.
That's kind of a SaaS metric, and it's useful in certain contexts, but it's not perfect metric for our business at this juncture.
One metric that stands out about you guys is the 220-something percent growth that you just quoted, and you guys still have a consolidated 40%-ish EBITDA margin. How have you been able to figure out how to grow and invest in capacity profitably?
Well, one is, we've stuck to our strengths. We don't chase bare metal opportunities where there's not a lot of margin and, it's a scale play. We look for customers that are gonna take multiple layers of the five-layer stack that Paddy articulated so that they're buying not just GPU and to get GPU access. They wanna buy inference services. They'll attach core cloud, and they'll be able to drive higher margins for us and higher ARR per megawatt.
Then of course, we have the core cloud business, the CPU side, which I think people are starting in the industry to become aware is gonna be an even more critical differentiator in the market for people who are offering AI services, is you need a CPU-based cloud as well for all those primitives. The margins there are materially higher than they are in the GPU world today. We've been able to marry those two things together. The other thing that I don't think people appreciate is the margin that matters for us is operating income, right?
That's, you know, if you're going to be apples to apples and compare us to software companies or compare us to AI infrastructure companies, our operating margin, GAAP operating margin, is in the top kind of quartile of companies out there, and very few of them are growing at the rate that we're growing. We're marrying very good kind of underlying economic margins. We're getting a ton of operating expense leverage as we grow. You know, we're going to grow 50% + next year. Certainly not going to add 50% to our cost structure. There's other economics that are different than our core. You know, these big AI natives, they don't pay with credit cards, so you're not paying a couple points to Stripe. The bad debt profiles are different.
There's just a lot of leverage that we can drive in terms of improved margins that offsets the fact that AI in general across the industry is a lower margin business than, you know, historical kind of cloud computing. By doing all of that, we've been able to maintain very, very strong operating margins and EBITDA margins, and we expect to continue to be able to do that.
Before we go back to Paddy, to talk a little bit more about the business and performance, any questions for Matt on capital structure or P&L? All right. Paddy, you know, I think we've talked a lot about unit economics, about TCO advantages, you know, delivering rationally for your AI native customers. All that would be one part of the story, you guys have really proven that there's high performance, predictability, and value to the platform. Independent benchmarks recently ranked DigitalOcean number one in output speed for models like DeepSeek-V3, outperforming hyperscalers by nearly four times. How much of this is driven by your vLLM optimizations, your software stack, the decisions you're making that are value additive versus the underlying hardware in the architecture of your system?
Yeah, it's a good question. The underlying hardware, in this case it was B300 Blackwells, is very important, but every cloud provider have access to the same hardware. The difference is in the software optimizations we make at a kernel level. It is the combination of the kernel optimizations we make for a specific family of models for a given hardware. That's where the secret sauce is or the tuning that happens at a kernel level. That's what is enabling us to get, in this case, 230 tokens per second is significantly higher than what you can get from like most other cloud providers, including hyperscalers. We take a lot of pride in that.
The reason why that is so important is right off the bat, if we are 50% better, that is 50% fewer tokens that our customers have to spend on. It makes a meaningful difference for them in terms of how much the total cost of ownership that results or delivers for our customers. We have a couple of very detailed technology articles which we have authored along with our customers to showcase exactly how that happens. For example, there's this concept of disaggregated inferencing, where we split the inferencing step into multiple steps and optimize every step along the way using GPUs and the software that we are optimizing. That's how we are able to get this type of throughput with extraordinarily low latency.
If you look at the Artificial Analysis, you'll see the throughput and latency as the two axes, and we are number one in both. The other thing I want to also mention is it's not just a result in a lab, right? Yes, that is important, but it is also equally, if not more important that inferencing is a real-world workload. One customer that we had up on stage for Deploy is a company called Hippocratic AI, and Hippocratic AI provides voice agents for hospitals. This is as mission-critical as it can get because it is providing primary patient care in acute postoperative type of scenarios in hospital settings.
You have to be super mindful of the latency. You have to be super mindful of the uptime for these kinds of workloads. Given that we have gone through the school of hard knocks for the last 15 years in terms of knowing how to build, operate, and manage Global cloud infrastructure really helps us make the transition from GPUs for training to actually deploying cloud infrastructure, AI infrastructure for inferencing at scale, which is a real-world workload.
With an eye on the clock, we've got about five minutes left. Any questions from the audience for Paddy or Matt? My last one, you guys have had a pretty blowout quarter in Q1. I think the stock reaction solidifies that. As you guys have done investor callbacks, and obviously the story is getting out much more broadly, into the broader research community, what haven't we talked about today or gone into in as much depth, that you think, you know, investors, analysts should know about DigitalOcean that maybe isn't quite out there yet?
I'll start from a more strategic perspective. First thing is, this is once in a generation opportunity that we are looking at. What we haven't really talked about a lot is the competitive moat that we already have and we are building actively. It is one thing to say, you have access to the same GPUs, I agree. In inferencing and agentic applications, most of the magic happens in software, and that requires a very sophisticated software stack, because the next generation of AI native applications are just starting to formulate, right? You have coding as a micro vertical is in full bloom. Pretty much every other SaaS category, vertical software categories, physical AI, are all in a very nascent stage.
There's a huge wave of new agentic applications that are going to be coming. What do agentic applications need? They need lot of intelligence, and they need a lot of agentic computing. What we have even today is a five-layer integrated stack from silicon to agents all working in a single stack. I think that competitive moat is very, very important because this is what AI natives need, and this gives us a structural advantage both from a technology perspective, but also from a unit economics point of view. That's why we feel like we wanna step into this generational opportunity, and we have the ability to do so.
Matt, anything you'd add to that?
I think that, to me, the conversation has shifted notably with investors from a year ago, probably not even a year ago. We were still getting questions on why do you guys exist and why can't the hyperscalers do what you do? I think that the market is starting to understand the difference, and I think that's reflected. I don't think they fully get the amount of addressable market opportunity we have. There's a lot of startups that have a lot of buzz, you know, inference wrappers that are providing layers of value added in the ecosystem. As Paddy just said, we're launching capabilities that can subsume some of those layers.
You know, I just I don't know that the market fully gathers that yet. I think that as we continue to win customers like Cursor and others that are huge customers of those kind of wrapper companies, but they're coming to us because they see the value that we can provide and the value there is when you disintermediate that extra layer, I don't think that's fully contemplated by the market at this point.
I agree. I think that's a great place to leave it. Thanks everyone for your time this afternoon, join me in thanking Matt and Paddy for theirs.
Thank you.