Matt Steinfort: Thanks, Mike, for the question. Well, Paddy, I think rattled off an impressive list of incremental capabilities that we’ve offered. We don’t view it really as a portfolio expansion so much as an enhancement of the capabilities that we have. So the company was started as an infrastructure-as-a-service provider selling Droplets, which are basically just compute, bandwidth, and storage. And as the company evolved over the last 10 plus years, we got into platform-as-a-service and offered managed database and Kubernetes and other capabilities that are really just kind of extensions and additional layers on top of the — of that core infrastructure-as-a-service, because our customers grew as, from individual developers and hobbyists to small technology companies and software providers that are running businesses on our platform.
And so, if you went back and listed all of the different products that Paddy had articulated, these are just incremental capabilities that these customers need as they leverage our platform-as-a-service and our infrastructure-as-a-service. They need more flexibility in how the products are configured with different ratios of compute to storage to bandwidth. They need things that make their lives easier because they don’t have giant IT organizations, so auto scaling and other capabilities that enable them to manage their infrastructure better. So we don’t view this any — really any of the products or services that we’ve offered as outside the bounds of the core target customer market that we’re serving. And it’s just as our customers are growing and evolving, we need to grow and evolve with them to keep making it simple for them to leverage the cloud.
Mike Cikos: Okay. And then the other question, more of a follow up here on the 2Q guide. We obviously have the 11% growth in hand, which is a slight deceleration sequentially. Can you just remind us what is that, I guess, Cloudways price increase that we’re going to be lapping? How much of a headwind does that represent when we think about the growth we’re looking at in Q2?
Matt Steinfort: Yeah, it’s interesting. This is why, again, coming from a different market into the software space and observing the, I’d say the obsession, but the focus on year-over-year metrics is interesting to me. Year-over-year metrics are both laggy and also what happened a year ago as a function is as important in the change in the growth quarter-over-quarter as what happened this quarter. If you look at the progression that we’ve guided from Q1 to Q2, we’re projecting increased revenue — increased incremental revenue, which implies an increase in a higher ARR than we added, incremental ARR than we added. So the current trajectory is improving. It’s not decelerating. But when you look at it, as you said, year-over-year, it is a slight deceleration.
And some of that, as you pointed out, is because we had a pop in the Cloudways business. It was about a 10% price increase a year ago. And so the 34% growth that we posted in Q1 for Cloudways is, it’s got about 10 points in there of a price increase and we’ll lap that in the second quarter. So that’ll come out.
Mike Cikos: Thank you.
Operator: Your next question comes from the line of James Fish with Piper Sandler. Your line is open.
James Fish: Hey guys. Paddy, in your opening remarks, you guys talked about discovering gaps in the product portfolio with customer conversations. What were those gaps that DigitalOcean needs to focus in on or were the release of products like backup and [caching capabilities] (ph) those gaps? And really are those gaps on the AI side too or what’s the differentiation on this infrastructure GPU-as-a-service launch against some of the larger players out there like the hyperscalers CoreWeave, Lambda, especially if we start to get supply more balanced in time?
Paddy Srinivasan: Great. Thank you, Jim, for the questions. So let me answer the first question first, which is some of the learnings that I described in my prepared remarks from a core DigitalOcean perspective. So those are what you are already starting to see in terms of our product delivery, right? So a lot of enhancements, as Matt just described, our platform-as-a-service offering is relatively newer compared to our Droplets. And we are — as we focus more and more on builders and scalers, there are some capabilities that they would love to see from us to help them scale as they grow. So you should expect us to release a lot of additional capabilities in the world of advanced reporting and management and visibility of infrastructure capabilities, security enhancements, advanced networking and global load balancing.
Those are the types of things that some of our advanced scalers and even builders are looking at DigitalOcean to provide because as their footprint increases and their business scales up, these are some of the things that they’re asking from us. So in my mind, these are all great opportunities for us to keep scaling our platform as our customers grow. Coming to the second part of your question, which is more around what is the differentiation from a AI, very specifically infrastructure-as-a-service perspective, as I said in my prepared remarks, this is — this feels remarkably similar to the origin story of DigitalOcean in the sense that we’re trying to democratize the accessibility of infrastructure for AI builders, extenders, and companies that are looking to deploy inferencing for their applications.
So the ease of getting started is a very durable advantage that we are looking to bring to our infrastructure-as-a-service and many customers have already started giving us feedback that it’s significantly easier to get started with our infrastructure-as-a-service. We are also looking at not just providing just bare metal GPU services, but we are adding different types of orchestration layers because it’s just not SSHing into raw H100 boxes. There’s a lot of complex things that needs to be orchestrated if you are a small startup or an ISV that specializes in say, ad tech, and you’re just looking to leverage a variety of different AI models, there’s a lot of technology that goes into building or even extending models and introducing, I’m sure a lot of you have heard of things like rags, which are a way to customize these models to make it work in your environment and take into account your context of the application.
So there’s a lot of complications that are involved, even if you’re not a model builder but a model consumer. And our software, as always, has been in the forefront of making it super, super simple. So our platform-as-a-service already does that throughout the full lifecycle of AI and machine learning development. And even our infrastructure-as-a-service goes all the way from bare metal to orchestrated abstractions to make all this easier for our customers. So we feel very good that we are taking our time, even though the momentum is building, we are still taking our time to really understand the needs of our customers at a very deep level, because what we don’t want to do is just satisfy some spiky buzz in demand. We want to build a business that is sustainable and we feel like inferencing is a very sustainable AI business model which will help us over the years to come.
