DigitalOcean Holdings, Inc. Citi’s 2026 Global TMT Conference

Well, thank you for hosting us, Mark.

Yeah. So thank you, Mark. It is a great question to get started with. I am coming up on 3 years, not quite there yet, but in the world of AI, that is almost an era. When I came in, the first thing that really attracted me to DigitalOcean was I have been working on and off on developer platforms, right from my early days at Microsoft, for close to 30 years now. That pedigree and the DNA of the company, which was relentlessly focused on catering to the needs of developers of all sizes and shapes and different sizes of companies, was really attractive. The second thing that was amazing about DigitalOcean, which still never ceases to surprise me, is the strength of the product to be the primary customer acquisition engine.

Those were the two things that I knew were the strengths of the company coming in. One of the first things we had to do was to make a decision on what kind of company we want to be in AI. The big bet that we took, in retrospect, about two years ago was, do we want to focus on training or inference or both? We made a very strategic decision to say that training was more on the hardware side, and our hypothesis was inference needs a lot of software. At that time, it was not that evident, but now it is fairly common knowledge that inference is not only a lot heavier dependency on software, but inference is also a more durable workload. It is a workload that resembles cloud patterns.

It is a production workload, and it is typically consumed when you have product market fit, and it is eventually billed right back to customer usage, for the most part. That is a bet that we made. Along the way, we had to make many other bets. For example, we had to make the bet that software needs to evolve to serve the needs of inference in a very different way than what training was. We also made a bet that we have to nail software first and then build scale. That is the inflection point we are in as a company. We feel very confident given that we have built our inference stack working hand in hand with customers.

That's one of the luxuries we have that many other neoclouds don't have, is having a front row seat in co-development, co-innovation with leading edge AI natives that push the boundary of inference, gives us the ability and the confidence that we are building the right thing. We are catering to the needs of the most demanding AI native customers. We feel we are on a really good path to a great software platform. Now we are addressing the scale, or we are building scale to support the software. A lot of neoclouds, or most of the neoclouds, I would say, went after scale first because they were focused on training and are trying to cobble together a software platform. The other main major decision that we continue to make is, I fundamentally believe that technology platforms, especially developer-oriented infrastructure platforms, cannot be assembled.

They have to be built from the ground up. Of course, you can have a couple of acquisitions here and there on small feature gaps, but fundamentally, it is really, really hard to cobble together. This is not an application stack like Salesforce.

There's a reason why you don't see Azure, Amazon Web Services, or Google Cloud really going in an acquisition spree to build their platform. Developer platforms, especially in AI infrastructure, have to be built from the ground up, and that's the platform we call as AI-Native Cloud. It has 5 layers, and it is beautifully integrated into a single pane of glass, provides all the control pane required to build and scale inference applications. A lot of small decisions that have put us in a very good position of strength, I would say, because we have some of the frontier inference workloads running on our platform.

Yeah today? Yeah, it's a good question.

So it has been 4 months since we launched. We went into GA, it's probably slightly less than 4 months, and we have a lot more than 6,000 customers at this point. It goes back to our strength in product-led growth. Of the 6,000 that we announced in August, maybe there are a handful that we acquired through our direct sales motion. A vast majority, like 99% of those customers, came to us by the virtue of our product lending itself to a try and then buy kind of model. So it is just organic word of mouth developer adoption. The reason why the product market fit was just jumping off the page is a couple of reasons. One, there's just a lot of latent demand for inferencing.