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

Good morning, everyone. My name is Melanie Strate, and I lead Investor Relations at DigitalOcean. And on behalf of the entire team, we'd like to welcome you to our 2025 Investor Day. We are so excited to see you all here in person at the New York Stock Exchange, and we have an incredible lineup for you today, including a number of presentations from our C-suite, followed by a live customer showcase where you will get to see firsthand what some of our top customers are building on the DigitalOcean platform. But before we jump in, I must disclaim we will be making forward-looking statements as part of today's presentations, and actual results may vary materially from those projected in these forward-looking statements, including our financial outlook. And with that, I will turn it over to our Chief Executive Officer, Paddy Srinivasan.

Thank you, Melanie. Good morning, everyone. How is everyone doing? I'm super excited to kick off DigitalOcean's 2025 Investor Day. Thank you all for tuning in virtually and a very warm welcome to all of you that took the pain to join us here in person in this fantastic Freedom Hall in New York Stock Exchange. Our objective today is to get you out of everything that is happening around us for 3 hours and get you all excited to focus on the main reasons to invest and get super jacked up about investing in DigitalOcean. So let me start that by telling you a little story and tell you why I was super excited to make the biggest investment decision of my career by joining DigitalOcean a little over 1 year ago. In DigitalOcean, I see the potential to replicate the journey that these generational companies that power today's digital economy went through to become companies worth tens of billions of dollars today because their individual journeys have a lot of common things. For example, number one, they were all founded to serve the digital native ecosystem. They're also founded with some core tenets of embracing simplicity as a way to offer their product or service. They also have a business model that is flexible enough to scale with the start-ups that they are serving. They also had a core tenet around obsessive customer service and meeting the needs of their customers. For the most part, they all embrace this concept of product-led growth motion to target developers and startups as a way to fuel their growth. And once they reached a critical mass, they started serving the needs of larger customers, digital native enterprise customers and started meeting their needs by innovating on product as well as adding new go-to-market motions to complement their product-led growth motion. Finally, along the journey, these great companies also harnessed and even in many cases, catalyzed new technology and market shifts that shape their respective domains. And when you look at DigitalOcean, we are on the exact same trajectory. Our founders got us from 1 to 3. They did that by starting DigitalOcean by focusing our efforts on the digital natives. They did that by building a cloud, which was extremely simple to use with a customer obsession that got us tremendous product market fit. And that's the foundation which we rode using product-led growth to develop mind share and market share with developers and startups over the first several years of our existence and build a great foundation that we have today with a company with over $820 million of run rate, 600,000 customers and a lot more. So that's why when the Board recruited me and said, we want you to come and jump start steps 4 and 5, I could not resist as I had done similar things in the past. And today, you will hear from us how we are well on our way on steps 4 and 5. We'll show you what we have done. We'll show you some of the traction we are already seeing in the market. And I will explain that by starting to talk about what unmet needs we are finding with the customers we serve and how we are solving those needs in a very differentiated and durable fashion in both core cloud as well as how we are taking the generational shift that we are seeing with AI and how we are democratizing access to AI for our customers. Next, I will tell you how we are accelerating our growth by scaling with some of our larger customers, and how we are doing that with a combination of product innovation to serve the needs of these customers and also how we are adding complementary go-to-market motion to supplement our world-class product-led growth. And finally, I'll take all of that and translate all of that into how this is impacting and enhancing our financial profile in the medium term and long term. Let's get started by looking at the cloud market. As we all know, cloud is a mission-critical public utility today, essentially running today's digital economy. But as more and more customers start deploying their applications to the cloud, it has started becoming super, super complex, and it has started leaving some customers behind. Why is that? There are 3 reasons for that. Number one is cloud has become super complex. Today, to run a reasonable cloud application, you need a lot more than software engineering. You need teams like DevOps to deploy things on the cloud. You need teams like CloudOps to monitor and manage your workloads once they're in the cloud. You need teams like FinOps to monitor and manage the expenses on the cloud. Why? Because cloud has also become super expensive. In fact, there's a whole ecosystem, a cottage industry that has evolved to help companies make sense of their cloud expenses. When you have to hire a third party to help you understand and walk through the line items of your cloud build, that's when you know things have gone too far. And finally, cloud has also become super intimidating for most companies, because the -- especially the larger hyperscaler clouds can be walled gardens with their proprietary technologies that lack portability. So once you deploy your application, it's really hard to move it. Finally, companies don't get world-class support unless you're spending millions of dollars with the large clouds. So in a nutshell, cloud is complex. It is expensive, and it has become intermediating for many companies. And there's one thing that is emerging, which is more complex, substantially more expensive and super intimidating, and that is AI, which is going to leave more customers behind. That's why DigitalOcean has emerged as a viable alternative to the large hyperscaler clouds by swallowing this complexity so that builders can build. They can focus on innovation. They can scale their companies without having to mess around with infrastructure. And that's what has enabled us to build a company of $820 million of run rate with great financials with 600,000 customers with a global footprint and a tremendous developer mind share. As I mentioned in my opening story, we are just getting underway with our own version of the scale-up journey. And to explain that, let's have a quick look at the markets that we operate in. You've all seen different versions of this TAM slide for cloud. It's a big market, $400 billion, estimated to triple by the end of the decade with cloud and AI. The interesting thing is 1/3 of this is driven by what we call as digital native enterprises. These are companies that offer a service or a product that is technology-oriented. Who is a digital-native enterprise? We are a digital-native enterprise. DO is a digital native enterprise. I'll give you an example. When we were a start-up, we started using Stripe to process payments. And we were probably spending a few hundred dollars on Stripe. And as we grew, we scaled up with Stripe. And last year, we pumped in around $1 billion through Stripe. And we likely spent close to $20 million with them last year. So that's the beauty of digital native enterprises. They start small, but they scale. When they scale, they just power the companies they leverage. That's why they are our main focus. We focus on serving the digital native enterprises. There are 4 million of them worldwide. They drive 1/3 of the cloud market, and we have 165,000 of them. And they have very unique needs in contrast to attritional enterprise for whom cloud is an IT enabler. It runs some internal applications. It's typically part of OpEx. It's governed by fancy and complex IT policies because a lot of the stuff is coming from on-premise. And typically, the division makers are IT and CIOs. Stark contrast with digital native enterprises whose needs are very mission-critical. They use cloud to power their product. So it's very mission-critical in nature. So as it is a core part of the product, it is part of their COGS and often the #1 expense line item in their P&L. So the decision-makers are typically founders or heads of businesses. And if you look at the hyperscalers today, they're so big and they are seeking large complex workloads and where are those workloads? They are on the left side. They are in the traditional enterprise segment because more than half of the world's software is still on-prem today and modernizing them and moving them to the cloud is a big, complex undertaking that the large hyperscaler clouds love. And the needs of these large applications in traditional enterprises are very different from the ones on the right side with digital native enterprises. And this lack of focus is leaving them behind. Let's look at some of these unique needs and double-click there. First of all, digital native enterprises run lean teams. They don't have budget to do heavy investments in DevOps and CloudOps and FinOps and things like that. And with AI, this is getting a lot worse. And they need clouds that swallow this complexity and let them focus on innovation. But simple doesn't mean simplistic. Many clouds that offer simple because they are a simplistic cloud. No, no, no. They need enterprise scale. They need enterprise footprint. They also need an enterprise SLA. Essentially, they need a full-feature cloud without all the complexity. Finally, they also need a cloud partner that they can trust, someone that won't box them into a wall garden. Someone that will provide them with transparency and predictability of cost because nothing can be more crippling than seeing a cloud bill that is shocking. And finally, they need world-class support regardless of how much they are spending. Essentially, digital native enterprises need a cloud partner that is large enough to scale with them, but small enough and hungry enough to care about their success. So how are we meeting the needs? To answer this, let me show you our product evolution journey over the last 12 years of our existence. And this is a 50-quarter view. First 8 years, we had founders who were developers themselves. They focused on digital native businesses. They created a platform with simplicity in its core. They provided a lot of transparency and predictability in pricing. They obsessed over customer needs, essentially nailed the product market fit and hence, the company grew rapidly. You can see that here. These were the steps 1, 2 and 3 in the journey that I started with. The next 4 years, 2019 to 2023 in our evolution was focused on professionalizing the company and tightening the operations. We went public, but we got distracted by our IPO and the COVID distractions. And we took our eye off the ball in delivering the scalability and other capabilities that our customers, especially our larger customers needed during that time, which led to graduation or defection of some of these large companies, which reflected in NDR and a slowing growth rate. As a consequence, we did not execute on step 4 of the journey, which was to scale and meet the needs of the larger customers. However, we are, over the last 3 quarters, not only focusing on this, we are catching up with the speed and ferocity that has me really, really proud. Just look at the type of releases that we have had, and it's actually falling off the page. I didn't have enough room to fit everything here. And Bratin will talk about how in Q3 of last year, we had 42 releases. In Q4, we had 49. And in Q1, we have releases in the 50s. And these are not just simple bug fixes and stuff like. These are big, chunky product updates, all aimed at meeting the needs of our larger customers so that we can scale with them. So you're probably thinking that's great, you're catching up, you're releasing like crazy, so what? I'll tell you so what. Here's a view of our large customer cohorts. In the last earnings call, I talked about our 100,000-plus customers. There's more disclosures here. For the first time, we are sharing our $500,000 and our $1 million customers. First takeaway is that we are adding a lot of new large customers. The second takeaway, which is even more important, is the fact that the larger these companies are, the faster they are growing on our platform. In fact, we are aggressively taking market share in this segment. And most of these customers are existing DigitalOcean customers, which are now rapidly expanding their footprint on us. And since these are existing customers, we have a great relationship with them. Many of them are here in the audience, and you will talk to some of them today. They are co-innovating with us. They're literally telling us what their unmet needs are. And we are doing co-development with them, and they're literally snatching the product from us even before we are able to launch it and market and sell it to them. That's the power of meeting the unmet needs of these digital native enterprises. This puts us in the same trajectory as the illustrious companies that I talked about earlier, who have made the transition to serving the needs of larger customers and hence, scaling their own business. That's the same trajectory we are in. And at our current stage of our journey, these numbers are not only comparable but also favorable to many of the other disclosures you might see from other companies that have gone through similar progressions. Let's now hear from one of our newest customers and why they decided to scale on DO. [Video Presentation] Fantastic. And these kinds of stories are becoming common every week. And a lot of this is happening organically. Let me now switch gears and talk about how we are scaling up and accelerating our growth. Let me start with the exceptional management team that we have assembled. Bratin Saha, who is our Chief Product and Technology Officer, built and scale multiple billion-dollar businesses for AWS in both AI/ML and Cloud. And before that, he ran all software infrastructure for NVIDIA. Larry D'Angelo, who's our CRO, did similar roles at many other public companies of comparable size. He brings a very unique high velocity inside sales motion that he used to scale companies like LogMeIn from $100 million to well over $1 billion. Matt Steinfort, our CFO, that many of you know, was also a public company CFO. And what is interesting about Matt is that he was a founder CEO of a tech start-up for several years before that. It's a quintessential operator. And Wade Wegner, who runs our developer ecosystem and product-led growth motions did similar things at a much larger scale for Azure when he was at Microsoft and Heroku when he was at Salesforce. So as you can see, this is a team that has the perfect set of complementary skills to help scale the company and accelerate its growth. Talking about growth, our growth comes in 2 different vectors. First is accelerating our customer acquisition engine, which really helps us acquire larger customers. And number two is our customer expansion motion, which really helps us with expanding our footprint with existing customers. And to me, everything really starts with product. And on the product, we have 2 dimensions. One is core cloud, where we are continuing to address the needs of the large customers, as I talked about. And the second thing is AI, where we are working on democratizing access to AI by focusing on building our IPA stack; infrastructure, platform and applications. Let me start by focusing or double-clicking on the left side first. As you all know, we have literally been focusing in our past on the bottom right, which is -- if you, again, remember, steps 1, 2, 3, right, where we were really focusing our efforts on product-led growth in servicing the needs of our digital native customers. And over the last 3 quarters, we have changed our focus to address the needs of our larger customers by doing things like adding support for multi-cloud. And in fact, this week, we had a major announcement on that. We are adding things like advanced networking, higher performance SKUs and also adding enterprise-grade SLAs, which are along the pars of -- the same par as a hyperscaler cloud. And not only are the large customers consuming this, they're also adopting these features at a very rapid pace, like almost half of our $100,000 plus customers have used one or more of these features in just 90 days after we released them. It's just incredible. What it shows is we know exactly what our customers need and they're telling us and working with us and adopting these things as soon as we are ready. Now let me switch over to AI. All the headlines that you see are dominated by LLMs, right? And how there's a new version of LLM almost every other day. And really, when you think about the focus on training is going to be dwarfed by the era that we are entering, which is the world of AI inferencing, where we are entering an era where it is going to be AI everywhere. Every software application is going to have AI as a core fabric. And what this is going to do is have an order of magnitude, importance and need for inferencing compared to training. And this is an existential opportunity for customers we serve. For digital native enterprises, this is an existential thing. So how are we addressing these needs? On the left side, you see training clouds, which are, of course, you need GPUs and you need network and storage. And all the magic on the left side happens really on the hardware side. On the right side is our DigitalOcean AI Cloud, which has infrastructure for inferencing, you have a GenAI platform and you have a layer for Agentic applications. And the interesting thing on the right side is, as we see our customers use our inferencing product, it literally lights up multiple boxes on the right. So what you need is not just GPUs, you need a general purpose cloud that has both GPUs and CPUs. And to tell you, the best way to bring the power of GPUs and CPUs, let's hear from the CEO of AMD, Lisa Su.

Thank you, Paddy, and hello, everyone. At AMD, our goal is to push the limits of high-performance and adaptive computing to solve the world's most important challenges. This is an incredibly exciting time for the industry. AI is the most transformative technology of the last 50 years and the ultimate application of high-performance computing. As AI fundamentally reshapes industries and our daily lives, we are focused on delivering high-performance, energy-efficient compute engines and enabling the open software ecosystem. Today's most critical digital services and experiences run in the cloud. As software providers embed AI across these applications and create entirely new ones, the cloud will continue to be an essential layer enabling AI at scale. That is why we are so thrilled with our partnership with DigitalOcean, a leader in scalable, full-featured cloud services trusted by over 600,000 customers and over 3 million active developers. With DigitalOcean GPU Droplets, customers can deploy popular AI models with a zero configuration setup that automatically optimizes both the model and server stack, reducing deployment time from weeks to minutes. We are really excited to equip DigitalOcean data centers with AMD Instinct GPUs, bringing leadership memory and performance to their AI cloud. DigitalOcean is also leveraging the incredible performance and TCO of our EPYC CPUs to run their full application stack. By combining powerful AMD compute engines and open software with DigitalOcean's product suite, we will empower a massive community of digital native businesses to integrate AI into their applications. We're proud to be part of DigitalOcean's growing portfolio of accessible AI tools. Together, we are bringing developers more flexibility and choice so they can move faster and build smarter. We look forward to continuing to partner with DigitalOcean as we enable developers, start-ups and organizations of all sizes with the compute, tools and support they need to accelerate AI innovation.

We're also super excited to partner with AMD as we continue to roll out our inferencing capabilities because that, in my mind, is a really, really important step to democratize AI for our customers. So let me switch gears and talk a little bit about our go-to-market. This is our classic go-to-market view. On the left side, you have our customer acquisition funnel. On the right side, you have our customer expansion motion. The common thing about these 2 sides is product-led growth. On the left side, we use PLG to attract and convert customers. On the right side, we use it to digitally nurture existing customers and get them to adapt and do cross-sell and upsell. So let me click on the left side for a second. So our PLG machine is famously efficient. It's famously efficient due to the mind share that we enjoy with developers. We have over 3 million active developers over the last few quarters. And we are able to attract millions of visitors to the top of our funnel and literally convert a big fraction of them into paid engaged customers without spending too much on marketing or Google. What we're doing now over the last few quarters is adding multiple go-to-market motions to augment this product like growth motion. For example, we are working with technology partners like Hugging Face to open new front doors that drop new customers directly into our funnel. We're also building a new outbound sales team for AI, a small team, to build and develop relationships with the AI native start-ups and the venture ecosystem. We are also amplifying our reach with partners, with channel partners like resellers, systems integrators, distributors. And these channels, especially #2 and 3 are bringing in larger customers. That's a really important point. A combination of these channels, we have only had them for less than 1 year. In 2024, they delivered 20% of our new customer revenue. And it's particularly more impressive when you think about the scale is in tens of millions of dollars. So I'm super optimistic that as we scale this, it is going to yield more results. On the right side, which is our customer expansion, we have traditionally relied on product-led growth, digital nurturing. We have not had a sales motion. Remember, steps 1, 2, 3, that's where we've been living. And now over the last couple of quarters, we have started adding new enterprise sales motion or an inside motion using named accounts, where we now have 8,000 named accounts. And for the top 3,000 of them, we have a part model. What that means is we have both a commercially oriented account manager, and we have a technical account manager. And the combination is intended to serve the needs of these top 3,000 customers. And the account manager's job is to build and nurture a relationship and then look for opportunities where we can migrate new workloads onto the DigitalOcean platform. And when they find it, they engage with a new direct migration team to help migrate these workloads over to DO. And just with a couple of quarters, and we started with 450 accounts, and then we expanded that and Larry will talk about this, we saw an 1,800 bps improvement in the NDR of our large customer cohorts, the $100,000-plus. And this was because of the incredible product innovation I talked about and also the fact that we are now engaging with our customers using an inside sales motion. In a nutshell, we are adding new motions to scale, to acquire new larger customers. And in this case, we are actually targeting customers within our base, and it's nice to have 165 customers to mine and farm because that gives us a very low-risk, high-yield go-to-market motion. So let me switch gears and tell you how all of this is translating into a robust financial profile over the medium term and long term. Before that, I want to give you a little bit of the principles that we use for capital allocation. We use a Weighted Rule of 40, which values growth at 3x free cash flow, which is how typically top public companies are valued in today's market. And we use this for decision-making around growth vectors, but also for our internal incentive structure like our bonus and so forth. This does not mean that we have to invest 3 points of margins to get 1 point of growth. That's not the point at all. It is just a guidance framework to emphasize the fact that we are looking for durable growth vectors. And when we look at growth opportunities, we use 3 Ds. Number one is durability of growth. We'll invest behind things that are durable. That's why we were very conservative in the GPU training world because we were not convinced that, that is a durable growth vector for us. It may be for others, but not for us. But we have a very different view when it comes to inferencing because that, as I showed, it needs a full general purpose cloud, and it adds to our strength. Number two is, we want to be disciplined. We want to be disciplined in looking at the type of customers we are serving, whether this growth vector is really adding to our product DNA, does it align with our go-to-market strengths? So that's the discipline I'm talking about. When we have a checkbox under these 2, we will be decisive in going after these durable growth vectors. And in 2024, we were a Weighted Rule of 40 at 27%. And our objective in the next couple of years is to push it to upwards of 35% and get to 40% as quickly as we can. So in a recap, first, I talked about how we are focused on serving the needs of digital native enterprises and how we are solving their unmet needs in both cloud and now in AI as we enter a new era of AI inferencing. And as I mentioned, this is an existential shift for our customers. And they are really looking to us to help them make AI accessible or in other words, do to AI for them what we did on cloud to help them. Number two, I explained how we are reaccelerating our growth with tremendous product innovation and how we are adding new complementary go-to-market motions to acquire larger customers and also drive the footprint and adoption with our existing customers. And finally, I talked to you about how all of this is translating into robust growth and healthy financials. I have one more thing before we finish. We are in a moment of transition. We're in a moment of transition with AI because natural language understanding is changing the face of software development. AI agents are changing the face and disrupting SaaS applications and the cloud itself. That's why we are changing the cloud with a project codename do.next. Through this morning, I talked about how complexity is killing the cloud. And in the current generation of DO, we're reducing the need for DevOps, CloudOps, FinOps and things like that. With do.next, we are just killing the complexity associated with these roles by just simply eliminating them. To tell you all about this and a lot, lot more, let me welcome our Chief Product and Technology Officer, Bratin Saha.

Thank you, Paddy. Hello, everyone. Thank you for being here. I'm Bratin Saha, and I'm the Chief Product and Technology Officer at DigitalOcean. I've been here for about 10 months. And as Paddy mentioned, prior to this, I was at AWS, where I helped to build and led some of the most successful AWS services like SageMaker, BedRock, Amazon Q, EMR, Glue and others. I was at AWS for about 7 years, where I helped build one of the fastest-growing businesses in AWS history, a multibillion-dollar ARR business. And then prior to AWS, I was at NVIDIA, where I led all the software infrastructure for all NVIDIA products. So with that context, let's dive straight in and let's get started with my key themes. First, I want to spend some time on how DigitalOcean reduces customers' TCO, total cost of ownership, by at least 30% when compared to hyperscalers. And that is because it reinforces and strengthens our position in the market. I then want to get to how our rapid product innovation is accelerating our revenue growth that we expect will get to 18% to 20% by 2027. I'll also spend some time on AI, especially the transition of AI to an Agentic world that aligns really well with DigitalOcean's core strengths and enables us to provide a very differentiated offering to our customers. And then finally, I'll double-click on do.next and show how it's going to provide a very different and a very differentiated customer experience for our customers. And finally, I'm also going to talk about our cost optimizations so that we remain efficient as we grow our revenue. So let me start with what Paddy mentioned. The cloud can be too complex, too expensive and too intimidating for many customers. And let me double-click on this a little bit. If today, you had to go in and do the most basic operation in a hyperscaler, the most basic operation in a hyperscaler, which is go in and allocate some compute. You literally have to go through thousands of decisions. Let me walk you through that. So you go to a hyperscaler and you go to their website and you have to go and allocate some compute. You first have to go in and allocate an instance. And then you have to go in and allocate the memory. And then you have to go in and allocate some data transfer rates. And then you have to go in and do something about data transfer rates within the same data center. And then you have to go in and do something for intra-data centers. And then you have to go in and do something about the NAT gateways. And then you have to do something about some of the configuration. It goes on and on and on. And that is the reason why on a hyperscaler, customers need big teams of CloudOps engineers because their job is to figure out the nuances of the hyperscaler so you can use them efficiently. By the way, it isn't over yet. It goes on and on and on. Actually it goes on. Now contrast this with what happens in DigitalOcean -- it keeps going on. In DigitalOcean, what we do is we take your compute, your storage, your networking, your IPv4 addresses, your VPC, we go in and we package all of that. So that as a customer, all you have to do is a single click to get started. So in essence, what we do is we swallow all of the complexity so that as a customer, you don't have to deal with the complexity. And that's why customers in DigitalOcean don't need a dedicated CloudOps team. Now we are both cloud providers. DigitalOcean and the hyperscalers, we are both cloud providers. But we are really selling very different kinds of products. Let me give you an intuition about that. Suppose you had to go buy a computer. You can do it in 1 of 2 ways. You can go to a store and say, "Hey, I'm just going to go and buy all of the components." So you can go buy the CPU, the memory, the hard disk and all of that or you can go in and buy a laptop. Now if you went in and bought all of the components, you could build any computer you want. But you have to take on all of the complexity of building the computer and you have to hire the expertise to build and maintain that computer. Now in the case of a laptop, it's much simpler, a lot more approachable. You may not be able to get any laptop configuration you want, like you may not be able to get any arbitrary screen size matched with any CPU you want. But for most people, it works way better. So now you ask the question, why this difference in approaches? Why do the hyperscalers have to provide you all of these building blocks? And why can DigitalOcean give you a product that's packaged much better and works much better for you? And the reason is that it arises from fundamental differences in our business models. And those differences give DigitalOcean its durable differentiation. If I have to summarize it in one sentence, I would say hyperscale begets hypercomplexity. Hyperscale begets hypercomplexity. What do I mean by that? Suppose you are a hyperscaler, your business model requires you to address the needs of every large enterprise in the market. Now suppose you are a Goldman Sachs or JPMorgan or General Motors or a Wallmart. You have decades of IT legacy. You have decades of IT legacy from even before the cloud was invented. You have decades of IT legacy from even before modern IT was invented. And so the only way that a hyperscaler can actually service the needs of all of the customers is to provide a building blocks approach because then your customers can go in and build any configuration they want. That's the only way to be able to support the zillions of IT configurations that arise from the legacy IT enterprise sprawl. But on DigitalOcean, we really just focus on the digital native segment of the market. By definition, these have modern IT systems. It's still a very large segment. Mind you, it's still $140 billion. But by being able to focus on the Digital Native segment, we are able to escape the complexity of the IT sprawl, and that enables us to provide a product that works way better for this segment. So you get product complexity. It turns out product complexity drives cost complexity. Over here, I've given just one example of cost on a hyperscaler. So I've just taken the networking example, the data ingress and egress and want to show how that works in the 2 kinds of clouds. If you look at this example, say, for example, you're using classic load balance, one kind of a load balancer, the price is about $0.01 per gigabyte. If you use a different kind of a load balancer, then the price is different and variable. So if you use the application load balancer, the price is between $0.03 and $0.09. If you use a different service, like, let's say, you use a database, the price is again different and variable. It varies between $0.05 and $0.09. Now if you, of course, have to talk to the external world, you need a gateway. Well, the gateway is another variable incremental cost. And what you need, you also need a CDN. Well, that's another variable incremental cost. Let's contrast that with DigitalOcean. A single, flat monthly charge, a single flat monthly charge, free inside the data center. So this difference, numerous incremental variable costs in one case and a single flat monthly predictable bill in the other case is the reason why -- if you're a customer on a hyperscaler, you need a dedicated FinOps team. This team isn't building infrastructure. They are not building software. They're just looking at your bill and trying to optimize it. So if I have to summarize this, it really boils down to this. Hyperscale drives high product complexity and that high product complexity drives high-cost complexity. Now that high product complexity also drives a high CloudOps cost and that high cost complexity also drives a high FinOps cost. And so it's no surprise that in a recent survey by Flexera of all cloud customers, TCO emerged as the #1 problem for cloud customers, by far. And it turns out that almost 70% of customers need a FinOps team to manage their cost, 70% of customers need a FinOps team to manage their cost. And that is why DigitalOcean has a unique position in the market segment because we go in and address the #1 problem of cloud customers because according to a study by Forrester, DigitalOcean reduces customers' TCO by at least 30%. Now some of this, of course, comes from a lower infrastructure cost, but a lot of it comes because customers are more productive and because they don't have to manage all of this overhead of CloudOps and FinOps and so on. Forrester also found that when a customer migrates from a hyperscaler to DigitalOcean, their payback period is less than 6 months, less than 6 months. To learn more about this, let's listen to this video from Market Circle. They produce CRM applications and productivity applications so that their clients can manage their projects and sales and customers in a single integrated platform that is also integrated with Apple software. [Video Presentation] Trust me, I feel happy as well when I'm saving costs. So let me now get to the next key theme of my talk, and that is how a rapid product innovation is accelerating our product growth. This slide shows the number of features that we have been launching every quarter. And you will see that our product velocity has increased by almost 6x in the last year. Now this is not happening because we are adding R&D costs. This is really happening from productivity improvements. This has 2 implications. As Paddy said, we had actually stopped innovating for some time, and that led to things like customer churn and defection. As a result of our product velocity now, we have been able to close pretty much all the known causes of customer churn. Not just that, we are now able to innovate on features that more enterprise customers want. Paddy talked about a few of them like multi-cloud support and NFS and so on and so forth. But let me double-click on our PaaS services for a bit because PaaS services are so important for larger customers. So this slide shows how we are innovating on databases, databases that are really important for large enterprise customers. You will see that the out-of-the-box performance of databases on DigitalOcean is about 30% better than the nearest hyperscaler, 30% better performance than the nearest hyperscaler. And the cost efficiency or the throughput per dollar is more than 40% better than the nearest hyperscaler, more than 40% better than the nearerst hyperscaler. And so you can see how our rapid product innovation is helping us innovate so that we can get more value to our customers. And I'm really excited about some of the PaaS and data cloud features that we have coming later this year and happy to take more questions on them later on in the Q&A session. Now customers don't just want enterprise features. They also want enterprise SLA. And so over the last year, we have improved, we have reduced our service downtime by 3.5x through using things like AI. So we are using AI now to predict when servers may go down so that we can get ahead of it. And as you can see on this table, we are now internally operating at an SLA at an availability that's better than hyperscaler SLAs, better than hyperscaler SLAs. So this year, we can now go to customers and say, "Look, you are going to get hyperscaler experience. Remember, the database performance. You're going to get hyperscaler experience. You're going to get hyperscaler SLA and you're going to get 30% lower cost." What's not to like about it? And I can tell you from my personal experience of having talked to hundreds and hundreds and hundreds of hyperscaler customers, this message will resonate very loudly. Now all of this innovation and all of this availability, all of this is nice and dandy. What does it mean to our business? This slide shows you how our product innovations are accelerating our revenue growth. And I'm just showing the features that we launched in the second half of last year, just the features in the second half of last year. As you can see in this slide, if you look at our Q3 revenue and you look at the features that we launched approximately in Q3, they contributed about $6 million or 3% to our quarterly revenue. In Q4, these features contributed $8 million or about 4% to the quarterly revenue. In Q1, we expect the contribution to be much more. And so you can now see how our product innovation is adding to revenue growth every quarter. In fact, in Q4, almost 30%, almost 27% to be precise, of our incremental revenue came from the newly launched features just in Q3 and Q4. And so you can imagine, as our product velocity keeps churning more of these, that accelerates our revenue growth. Let me now get to AI. And specifically, how AI is moving to a place that's really well positioned for DigitalOcean. Paddy talked about IPA; the infrastructure, platform and application. So let me start there. If you look at transformational technologies, they always follow this pattern. The action always goes from the infrastructure to platforms to applications. Let's look at the Internet. The switches and the routers were always there. It's really the emergence of the platform technology, the browsers and HTML that led to all of these applications, the e-commerce sites, the search sites that drove the adoption of the Internet. If you look at the PC, the chips and the memory and all of that was always there. It was really the emergence of the platform, the Wintel platform that led to the creation of the applications, Microsoft Word, PowerPoint, Adobe apps that became a part of our lives and drove the adoption of the PC. If you look at smartphones, it was really the emergence of the platforms, iOS, Android that drove the apps, and that became a part of our lives. And AI is also following a similar transition now. And that transition is happening and will happen because of agents. Now all of you will probably have heard of agents. 2025 has been the year of agents. In a sense, you can think of an agent as a piece of software that can pretty much act and think like humans. And because of that, these agents are going to drive a massive wave of automation in digital enterprises. Let me show you how. If you think about a normal customer service call today, a normal 10-minute customer service call today, a lot of that today can be handled by generative AI agents. So if you look at the cost of that call with a human agent, it's about $3. With a generative AI agent, you can do it for $0.01. Now the cost of AI is going down by 10x year-over-year. The quality of these models is improving exponentially year-over-year. So you draw the line. In 3 years, the conclusion is inevitable. A lot of human workflows in these businesses will get automated or get assisted by these agents. And that means that digital native enterprises will really have no option but to reinvent themselves with agents so that they can serve customers in the best possible way. And that is going to drive a massive amount of cloud and AI TAM expansion. Because if you think about it, the TAM for these agents is effectively the human salary TAM, and that's trillions of dollars. And that means these agents, as they get adopted by digital native enterprises are going to drive an expansion of tens of billions of dollars. And at DigitalOcean, we fully intend to participate in this TAM expansion by providing a very differentiated offering. Let me explain why. So for this, let's dive a little bit deeper into agents. If you think about agents, they're really a sequence of 4 steps. They take a command from the user to do certain tasks. They then get all of the data they need to do their task. They then do some planning and reasoning, how am I supposed to do the task and so on. And then they finally go in and perform the actions. So what are the building blocks that agents need? Well, for the language commands, they need a language model. It's really a foundation model that does language interpretation. For acquiring all the information, they need access to a database and need to retrieve the data and do some data processing. For all of the planning, they need a planning model. That's again a foundation model, but that's more optimized for planning and reasoning. And then they need some application logic, so they know how to do the job. And then finally, to perform the actions, they need some software orchestration code. Now given these building blocks, what are the infrastructure requirements for agents? Well, the language model needs a GPU, but these GPUs are going to drive inference, not training. All of the data processing, that needs databases and CPUs. The planning model, again, needs a GPU. But again, this GPU is driving inference, not training. And finally, all of the software code needs CPUs. So let me pull all the infrastructure pieces together. This diagram is really important to understand where AI is headed and what it means for cloud companies. And 2 things should come out from here. One, the future infrastructure is a lot more than GPUs, a lot more than GPUs. You need the full application infrastructure, the CPUs, the databases and so on. That is something that we at DigitalOcean have perfected over the last 10 years. The second is that the GPUs are going to be used for inference. In fact, inference is likely going to be more than 10x. So let me relate this back to what Paddy was mentioning. AI had training, but the puck is moving towards inference. And the reason is that you can build all the AI you want. Ultimately, customers need value out of it, and you get value out of something when you're embedding the AI in certain application. And that is why we did not necessarily chase after the large training workloads because that's really all about GPUs. But as the puck moves towards inference, that plays to our strengths. You can't build an agent without a database. Remember, the database performance, 30% better than the nearest hyperscaler. That is our strength. That is what we have mastered over the last 10 years. And so as AI moves towards this Agentic world, it plays really well with DigitalOcean's key strengths, and that is what enables us to build a really differentiated offering. Now you can go about this as a cloud company in 2 different ways. You can be a niche cloud company and put in some GPUs and say, "I'm ready for AI." Or you can be like DigitalOcean, where we have fully featured data centers that can run both the application stack and the AI. And you can see that if you're a niche cloud, you are actually really inefficient because what you have done is you have separated the application and the AI and you've introduced a lot of hops. But if you're a DigitalOcean, you are really well suited for where AI is headed. This takes me to how we are going to differentiate. We are building a purpose-built generative AI infrastructure. And as I mentioned before, it has a lot more than GPUs. There's vector databases, there's search index and all of that. But we are also purpose building this to provide the lowest TCO for inference. Remember, inference is 24/7. It's production workloads, low cost is critical. And we are providing the lowest inference by customizing the network for inference, by customizing and optimizing the software so that you get the best TCO. And we think this is already up this quarter. Customers will get up to 80% lower TCO on DigitalOcean. This is not all. We are also innovating at the platform layer. In January, we launched the preview of a generative AI platform. It's loaded with features. I'm not going to go after each of these boxes. There's like agent routing, multi-agent routing, knowledge bases, guardrails and so on and so forth. The key point, though, that I want to communicate is that there is a lot of DigitalOcean innovation on top of the GPUs and on top of the LLMs. And that is the source of our differentiation. A lot of innovation outside of the GPUs and the LLMs, and you need this to be able to build agents and applications in a meaningful way. So what does this mean for customers? Well, if you compare it to AWS BedRock, it takes half the time to build an agent. We support 50% more content types, and you get 10% more accuracy. Trust me, a lot of customers' brain cells will be pretty happy. Not just this, because of the amount of innovation that we are able to pack into that generative AI platform, into that software, our economics at the platform layer are actually much more compelling. So every dollar of GPU revenue drives more than $1 of other revenue. And not just that, for the GPUs at the platform level, our gross margin is actually twice and the payback period is 1/3 of those of the GPUs at the infrastructure level. So what are customers saying about this? Well, we haven't hit GA. So we haven't even started go-to-market. In just 8 weeks, we have more than 2,000 customers who have built more than 6,000 agents, a lot of them in production. I have a number of use cases here, and I've made this slide very busy because I get a lot of questions from people on what our customers doing. And they're doing some really interesting production use cases like e-commerce invoice processing, like help desk chat, like analyzing financial documents. To learn more about this, let's listen to Sonar Home, a customer that is integrating agents inside real estate offerings. [Video Presentation] The way we work is going to change dramatically. Now you have a choice. DigitalOcean Generative AI platform, 50% less time to build, 80% lower TCO for inference, 10% better accuracy, 50% more content types. Where do you think customers are going to go? And that is why I think the DigitalOcean will play a big role in this AI and TAM expansion. Let me now get to do.next and how we are fundamentally reinventing the customer experience. Paddy mentioned that we are at a seminal point in technology. There are really 2 paradigm shifts going on. One is natural language becoming the new UX and second, agents automating all of the work. And when you have these paradigm shifts, there are winners and there are losers. The winners are the ones who can use these paradigm shifts and reinvent themselves. And losers are the ones who don't reinvent themselves. And so let me show first, with a product that is now in the hands of customers, how we reinventing the DigitalOcean experience. [Video Presentation] I want to emphasize, this is a real product in the hands of customers that we should be launching in approximately a quarter. And I hope a few things came out. The developer is no longer mucking around with core trying to deal with their web hosting issues. They're just talking to their web hosting system. The second, debugging and fixing, the work done by very experienced engineers is getting completely automated out by an agent. This is one of the most sophisticated uses of AI anywhere in the industry. And so you can now imagine how much easier, how much simpler, how much more approachable we make the cloud. So not only does this give us a more differentiated product, it actually opens new revenue streams for us. So today, customers, for example, spend up to $200 a month fixing the system issues. And that gets automated away. So we will be offering these agents as a paid offering. Not just that. Today, annually, DigitalOcean gets 300,000 hours of customer support for these issues. A lot of that gets automated away. So we get a lot more efficient as well. So if you go back to the 3 questions that Paddy mentioned, our position in the market, revenue growth and getting more efficient, you can see how do.next kicks us into a higher gear on all of those vectors, providing a highly differentiated product, opening new revenue streams and opening new highly differentiated revenue streams and making us a lot more efficient. So where are we headed? Well, we'll, of course, embed these agents in all of our services to provide that experience to our customers. But there's also one other thing I want to point out, and that is -- unlike hyperscalers, our customer workloads and our customer workflows are much better suited for this kind of innovation. And the reason is the same. When as a hyperscaler, you're dealing with decades of legacy and dealing with legacy workflows that came even before the cloud came, it's going to be very, very difficult to do this kind of innovation on those customer workflows. Let me now get to the other side of the Weighted Rule of 40, how we remain efficient so we can return more cash to our shareholders. This year, in March, we opened the India R&D center. And going forward, a lot of our incremental hiring will be in low-cost geos. That significantly reduces our cost per head and allows us to invest without increasing our expenses. We are rapidly using AI to improve our productivity. In fact, our developers now use generative AI for coding. And we have seen that developers using generative AI are now turning out 40% more code, 40% more code. We are also using generative AI for our system operations and our productivity has improved by 37%. And I expect these productivity improvements to keep increasing. We are also improving our gross margin. So we are using AI, for example, for sweating assets longer, for using assets for longer so that we get better depreciation. And that should yield an annualized 240 basis points improvement in our gross margin. We are also working on data center optimizations, things like network optimizations, data center consolidation that should yield 100 basis points in annualized gross margin improvements. And you should expect us to keep continuing to work on these cost optimizations in subsequent years. So let me wrap up with the key takeaways from my talk. DigitalOcean provides at least a 30% better TCO than hyperscalers, which helps us address the #1 problem that cloud customers have and reinforces and strengthens our position in the market. Our rapid product innovation is now accelerating our revenue growth that we expect to reach between 18% to 20% by 2027. AI is moving to an Agentic world that aligns really well with DigitalOceans core strengths. And that enables us to provide a highly differentiated offering to our customers. And I hope you saw that our AI strategy is not just about buying large forms of GPUs, but really about innovating on top of them so that we give a lot more value to customers. And finally, with do.next, we're really reinventing the cloud experience to provide a much better, more differentiated product. All of this while we continue to improve our cost structure. So building the product and innovating on the product is just one part of the story. The other part of the story is how we take our innovations and take them to customers. And for that, I'm pleased to welcome Larry D'Angelo, our Chief Revenue Officer.

As a sales leader, you can imagine how excited my team and I are to sell all the recent innovations and product newness that Bratin and his team has brought. So good morning, everyone. My name is Larry D'Angelo, and I'm the Chief Revenue Officer here at DigitalOcean. I've been with the company for about 8 months. And prior to that, I ran global sales for a public company called LogMeIn, where during my journey, 8-year journey, we took sales from about $100 million to $1.4 billion. And what's exciting about that journey is it has many parallels to the journey we're on today that Paddy outlined in his presentation. LogMeIn had a great product-led growth motion, but we had to layer on high velocity inside sales model, account management, upmarket product capabilities and upmarket motions, enterprise-like treatment, increasing our funnel through channel partners and technology partners. So all those same principles that we had then, we're applying to DigitalOcean. That's why I have such confidence that we're going to make all the innovations work and drive growth as Bratin mentioned, 18% to 20% over the next couple of years. What I'm excited about is the pace of recent innovation and progress that we've made as a team and as a company. And we've been together for a relatively short period of time, most of us less than 1 year. And as I mentioned, I have a passion and a lot of experience around great high-velocity go-to-market models like the one we have here and just really an interest in trying to simplify the lives of our customers, help them realize more value and grow their businesses. And I think all of us look forward to the great growth and opportunity that we have ahead. So to start, there are 3 items I want you to take away from this session. First, as Paddy mentioned, we have a world-class product-led growth engine, and we're going to build new motions to complement it. We're adding a named account team or named account strategy to put touch on our highest and most important customers. And we're going to scale the go-to-market organization without materially changing the financial profile of the company, and we want to remain highly efficient as we scale. Now our full life cycle PLG engine, it's full cycle, meaning it doesn't just bring in leads, it actually converts, as you know. That has driven most of our growth to date. Today, it drives 4 million unique visitors, 150,000 sign-ups and that results in about 60,000 customers at any point in time that come in to try the platform or actually begin their journey on building their business. And historically, customers have started small, less than $50, but many of those customers have actually built their businesses, and we have examples of thousands of customers still on DO today that have driven their revenue into the hundreds of thousands. With all this traffic, the PLG engine, it's super efficient. We only spend 7% of sales and marketing cost expense to revenue. And our magic number of [ 2.2 ] is well ahead of any industry benchmark. And as you know, magic number is really the incremental ARR over prior period sales and marketing costs or expense. And it's a great way to look at sales productivity and sales efficiency. And I would think you have to be wondering, how do you have this great engine, all this traffic and you're only spending 7% on sales and marketing. Well, I can say that we've actually immersed ourselves into the world of the developer. It starts with our great developer community. Our community, it's rich in content. We have thousands of articles, thousands of pieces of content. It attracts millions of visitors every year. We sponsor great open source events like Hacktoberfest. I'm not sure folks know, but DigitalOcean actually started Hacktoberfest, and we still govern it today. So 65,000 developers across 171 countries come in every year and use the platform. And our overall voice and awareness in the marketplace continues to expand, attracting and engaging customers, whether it's at events or communities. And all of this development, all this developer engagement is what powers the funnel and feeds that left-hand side. Now once we acquire customers or fill that left-hand side, there's key reasons why they stay with us. First, customers can continue to build their business and innovate on DigitalOcean, whether it's core compute, AI or both. And they only pay for what they use. There's no hidden fees, as Paddy mentioned. Customers don't get a bill and like need a FinOps team to try and disaggregate it. And customers realize the strong total cost of ownership that Bratin mentioned. That's really important, not just for their existing work, but as they decide to grow or bring new workloads over. And we have great white glove support. We provide a high touch, a high class of touch that the hyperscalers simply can't. And that not only draws customers in, but it helps customers stay. So the proof that the funnel is delivering really can be seen in our growing globally diverse customer base, 638,000 strong. And this may or will surprise you. We're actually the third largest cloud in terms of customer count behind AWS and GCP. But more importantly, you can see the growth in the number of our high-spend customers. Our high-spend customers are those customers that are spending $600 ARR or more, and you'll hear these names we classify as builders and scalers and scaler plus. But more importantly, you see the growth of our scaler plus or our $100,000-plus customers growing 85% over the past few years. Now at our inception, while we did focus on the developer and DO is still a great place for developers to come in, try the product and start to grow the business, our focus is really on the high-spend customers. They make up 88% of our revenue, growing at 16%. And the revenue they generate, it's different. It's stable and it's durable. And I'll get into exactly what that means, but that's important. So if you take one piece away from this slide, it's the larger the customers are, the more durable the revenue. And here's what I mean by that. They have overall higher revenue growth. They consume more products. Those more products they consume make them stickier. And when they're stickier, they have a positive impact on NDR. And if you look specifically at the $100,000-plus ARR customers, you see 37% year-over-year revenue growth. And with all this, we still have a huge opportunity to drive expansion. We only have 5% to 6% expansion penetration in our larger customers, and that's where we're going to point our resources. So the net is this. We have a very strong track record of growing large customers. As Paddy mentioned, 165,000 digital natives, 500 customers paying more than $100,000 ARR. And we have -- here's just 4 examples of customers who have substantially grown their business on DO. PriceLabs, [indiscernible], Bloom and [indiscernible]. And let's hear directly from Bloom in their own words about their journey on DigitalOcean. [Video Presentation] Now with 4 million digital native enterprises available, as Paddy mentioned, less than 165,000 that we have, we have a huge untapped market. But that's just where we are today. So now let's shift to the new drivers that are going to help us drive expansion and growth, both new customer acquisition and into our customer base going forward. So for 2025 and going forward, we have to improve on 2 dimensions. First, it starts with the product. As you heard from Bratin, we have all these great upmarket capabilities that we're now delivering, and that's important because customers can now stay and grow with DigitalOcean. They don't have to leave the platform to grow. We have all these great capabilities that allow them to continue to grow their business. And on the go-to-market side, we have to do 2 things. We have to drive new customer acquisition, and we have to expand our existing customers. So to start, let's focus on the customer acquisition side, that left-hand side of the funnel. So we have 4 new growth drivers that we're going to embark upon. The first, it's more of an improvement. It's increasing the yield of the existing funnel. Even though it's world-class, we still think we can improve conversions and yield. Second, we're going to leverage technology partners to widen the funnel or widen the aperture where they will actually drop customers into DigitalOcean directly. And then we're layering on new dedicated motions. As Paddy mentioned, we have an outbound motion specific to AI-centric companies. And finally, we'll invest in channel partners and channel partners will do 2 things. One, they'll just bring us deals directly; or two, they'll bring in deals that are more qualified and they'll bring them in, in the later stages of the pipeline. And we'll go into each one. So to improve the already great PLG engine, we're intensifying our focus on high lifetime value customers. So what does that mean? We're capturing more relevant information at sign-up, so we can better qualify customers early in their journey, and then we can kind of create digital but bespoke onboarding processes to help them consume products faster and hopefully grow faster on the platform. And just as we did for the cloud, we're going to do the same for AI when it comes to content. We had all this rich, thousands of pages of content for the cloud. We're doing that for AI. So we continue to offer rich AI-focused content that's attracting and engaging developers, and we feel that there's a lot of opportunity to grow the community through that. And for in-person engagement, whether it's our global meetups or how we show up at events, we'll be more deliberate in our approach and attract and engage customers that fit more in the profile of who we're looking to serve. And so for technology partnerships, this is a great opportunity. We want to leverage the millions of high-value curated developers that these partners have. And these partners, they widen the funnel. They create new front doors directly into our product. An example is Hugging Face. They have a massive collection of AI models, over 1.5 million. And we made it dead simple for developers to directly connect to some of their most popular models with a single click. Same with Laravel. Laravel is a leading PHP platform and developers can seamlessly click and activate Laravel Forge right through DigitalOcean. And so far, we have 5,000 active developers that have come through this partnership. And these partnerships, yes, they're great because they give us a new front doors and new top of funnel, but it also kind of extends our technology ecosystem, gives more value to the customers so they can continue to build their businesses. For new logo acquisition, we're starting with AI. We spun up a very small lean team to focus on AI. We've kept that team fairly lean. The team remains extremely efficient and highly productive. They target early-stage venture-funded companies that are AI-centric, early in their journey, and we want to grow as they grow to further penetrate AI market. And you can look at the team, they drove 160% of ARR growth in Q4 alone, and we expect that to continue. And channel partners, they give us great leverage and really bring high-quality customers into the funnel, as I mentioned, partners like GMI, Shade Form, Storj, they're more on the AI side. They either provide value-added services on top of our platform or they offer marketplaces where they resell AI-centric technologies versus customers -- or partners like Persistent or Aquadeal, they're more systems integrators, SI, where they help facilitate workloads from the hyperscalers and bring new customers in as part of that relationship. And some of these partners have been in place for weeks, some for months, some for a quarter or so. We've already driven 9 new scalers, which are $100,000-plus customers in the time that they've been associated with DigitalOcean. We have a pipeline filled with many more. So to recap, we have new motions or channels to fuel customer acquisition and augment this best-in-class PLG funnel. And even though some of these started, as Paddy mentioned, in the second half of 2024, you can see the early success. 20% of customer revenue in 2024 came from these non-PLG channels. And when you look at growth from new customers in the first 12 months, that's accelerating, and that's what we're looking for. And this is really without yet having realized the full impact of any of these drivers you'll see on either the acquisition or the expansion side. So now let's move from the left-hand side of the funnel, the customer acquisition side, to the right-hand side and focus on expansion drivers in terms of tapping into our customer base. So we are obsessing on the right-hand side of the funnel. We have a huge expansion opportunity here, and that's where we have our new resources pointed. We're assigning our biggest spend customers and our highest potential spend customers, and we'll get into those details. We created a migration services team to either bring workloads from the hyperscalers directly or to work with our partners and help them facilitate workloads to continue to grow our business and drive expansion. And we've assembled a team who is mining unassigned accounts, either customers in the early stage of their journey or later stage, but accounts that are not unassigned from a named account basis. And so in the first half of 2024, we began focusing on the top 450 accounts. We picked 450 and said, "Hey, let's assign what we call technical account managers." Their job is to drive nurture and adoption, think of like classic customer success. So let's assign technical account managers, and we want to provide them some mechanisms so that they can act proactively with these customers. So we did 3 things. First, we instituted a health score, look at usage, adoption, support tickets, product upgrades, product downgrades. So the health score gives us an indication at any point in time, what's kind of the temperature of the customer. Then we created what's called a growth room. We said, of these 450 customers, what products can they grow into? And we work with marketing to drive campaigns to try and push those products. And then we created a war room. The war room was really think triage. You have product engineering, sales, support, marketing. Any time any of these customers had an issue or a hiccup, we kind of swarmed on it, make sure we created a remedy and we pushed it out quickly. We saw the performance improve in that top 450. So in the second half of the year, we expanded to 1,500. And providing that same treatment, we saw 120 bps increase in NDR of these largest customers from the net expansion they drove. And that gave us great confidence to further expand. So for 2025, we increased kind of that circle to 3,000. So we have our technical account managers assigned to the top 3,000 high-spend accounts, but we also instituted a brand-new growth account management team. Their job is to drive really the commercials, the upsell, the cross-sell. And we pulled our solution architects in closer to those 3,000. So we created a pod-like structure to provide a little bit more enterprise-like treatment, but without enterprise cost and complexity. In addition to that, we then looked at the next 5,000 customers, but not by spend, by potential. So through propensity modeling and analytics, we said, okay, what customers have early characteristics of the top 3,000. We picked 5,000, and that's where we specifically have growth account managers assigned. And so their job is to farm those accounts, create upsell, create cross-sell and bring them up to high spend. And so we're confident that as we penetrate each of these rings, we now have 8,000 that are signed, we can continue to assign accounts and drive increased expansion. To help us support these efforts for the technical account managers and the growth account managers, we had to institute a migrations team because part of the expansion effort or a lot of it is, as Bratin spoke about, moving workloads from the hyperscalers. And with the migrations, customers that we engage with are very excited to come into DO. Why is that? Because once you go through the value proposition, customers, whether it's starting with a test workload or a dev workload, customers are excited to try. And why as they go through this migration, as Bratin mentioned, they realize an immediate cost savings upfront. They realize as they go through the total cost of ownership, how we're less dependent on the functions and formality you need in a larger cloud. We have a program where we shield customers from the cost in the work. Meaning that if we're doing the workload move, we'll do that at no charge. If a partner is doing the workload move, we'll actually fund the partner to help do the work, and we'll try and minimize as much on the customer as possible. The idea is to try and relieve the burden. And once customers come to DigitalOcean, they have a completely different support experience. It is white glove, it's high touch. It's something that the hyperscalers can't afford to apply to the types of customers that we're bringing in. And so I kind of can go on and on about the greatness of our migrations. But I think what may be better is to hear from another customer, [indiscernible], who went through this process and they can talk about the benefits of moving to DigitalOcean. [Video Presentation] And you can see, as customers engage with the company, it is a different experience, right? It is low cost. We still have to have the capabilities that Bratin's team are building the upmarket capabilities. But when they get into the support and they realize that we're there to help them grow their business, it's a completely different experience. And not only [indiscernible], you'll hear from others in terms of videos, and you'll hear from our customers directly. So our final driver is really around farming the base for the next $100,000-plus accounts. And this is a very low-risk, high-yield effort. We created models to look at customer behavior and usage to try and identify those that had early patterns that they could basically grow large. And the idea was to, as I mentioned before, look at the unassigned accounts and leverage these signals to kind of create a force prioritization for the reps. So we'll look at something as, hey, someone just attached an SSH key within a certain period of time. They had a workload on 2 data centers that spread to 3 or 4 or they're actually consuming more product or new features faster kind of than the mean of their population. So any of those are indications that, hey, someone may be looking to grow and may be looking to procure more. So we take those and we send those directly to the reps. A rep gets in, in the morning, opens up their dashboard and, like, I have 15, 20 or 30 active opportunities that I can exercise. And the reason why they're so valuable is because the customer is at some high point of influence of doing something. And so we've been able to take advantage of that. And when you look at kind of a long tail of the funnel, we're very confident we can continue to staff this as we show success. So we're confident that these drivers are going to drive great revenue in 2025. And the early results show that we're on track. We realized an 1,800 bps increase in NDR from $100,000 customers in 2024. As I mentioned, that's where we have our focus. And the NDR of these customers, it's still improving and still accelerating to the point where 37% of ARR growth of $100,000 customers was realized in Q4 2024 alone. And just like on the acquisition side, these efforts are -- they're not mature, these motions. Some of these have just been started a few months. Some of these started in the back half of last year. And it's no coincidence when you look at the products aligned to the upper right here that the growth is also aligned with innovation. All the stuff that Bratin's team has been doing, the go-to-market team has been consuming and trying to use to drive customer growth in revenue. And if you recall, why this is so important, everyone loves large customers, like no one is going to say they don't want a large customer, but the value of our customers when they're large. The revenue is more durable and stable. They stay longer. As Paddy mentioned, they grow faster and they retain better. So the great news here is that we can scale without materially changing the financial profile of the company, and we can do so with remaining highly efficient. And let me underscore that because that's a very important point. We just reviewed 7 new growth drivers across customer acquisition and expansion. And we kind of have this philosophy. We want to nail all these before we scale it. We want to invest in growth, but we don't want to get too far over the tips of our skis. And so it had $100 million in 2025. We made modest additions to the sales team. We had a lot of roles that we actually repurposed to point towards revenue. We operate a high velocity inside sales model. As I mentioned, we have a lot of experience with that, and that's really what is going to power a lot of our new motions. And our change in sales and marketing spend or the change in cost is minimal relative to what we can generate. And we're also leveraging AI and a lot of propensity modeling really to drive demand for the team. So in closing, what I want you to leave here is knowing that we have a world-class product-led growth engine, and we're going to build new motions to complement it, and we're very confident in those based on our past history and success. We're adding a named account team to focus on our most valuable and most important customers, and we're going to scale the go-to-market again in a very highly efficient way and not change the financial profile of the company, and we'll continue to remain highly efficient as we do so. Thank you. We're actually going to take a 15-minute break, and then we get back. Our CFO, Matt, will kick us off. [Break]

All right. Well, thanks, everybody, for coming back. I really appreciate that everybody is here on a rainy Friday during a, let's just say, a very eventful week. And I'm very grateful for you attending and listening to our story because I think we've got a great one. My name is Matt Steinfort. I'm the Chief Financial Officer at DigitalOcean. I've been here 2 years, and I'm the longest tenured executive that you've heard from today. So thank you. Thank you. I'll try to make it through the day, and we can keep that streak going. The reason I'm excited is when I joined the company, I saw DigitalOcean as a phenomenal opportunity. And sitting or standing where I am today, I think it's an even bigger opportunity. With the new team that we have, with the new focus that we have, the strategy, the increased pace of execution, I think this is a very different company than it was just 2 years ago. And I think that the opportunity in front of us is even bigger than I had contemplated. So Paddy and Bratin and Larry have talked a little bit to you about the strategy and the changes that we're making and the progress that we've made. And so I'm going to spend my time with you talking about the financial implications of this and how that translates into the medium-term and long-term financial outlook. So I'm going to start, and I'm going to frame my conversation using the same kind of 3 takeaways that Paddy started the conversation with earlier this morning. We have an incredibly compelling position in the market, and I'm going to share some statistics that basically give that credibility. We're accelerating our growth. Larry talked about a lot of the levers that we're pulling. And I'm going to dig into how those levers are going to translate into that higher growth rate for us over the medium term. And then we talked about on this path to higher growth and reaching 18% to 20% over the medium term that we would do that in a really disciplined and profitable way. So I'll dig into all of the profitability drivers to give you a sense of how these will evolve over the coming years. So to start, to me, the biggest evidence for the position we have in the marketplace and the durability is the scale and scope of our customers and our revenue base. We exited last year with $820 million of ARR. So clearly, we're serving someone's needs. We have over 638,000 customers, and there's no real concentration. We're distributed around the globe. We've got 60% of our revenue comes from outside North America, 70% comes from outside the United States. And we have no real concentration from a size of customer, use case, a vertical perspective. So we've got a highly diversified customer base. We've grown this business in a very profitable way. You've seen us drive -- improving EBITDA margins. We generate a lot of cash, and we've done a very good job of returning value to the shareholders, increasing non-GAAP earnings per share over 400% since IPO. We've built this business on a highly durable product-led growth engine that Larry described, that Paddy introduced at the beginning of the day. We add almost 13 points or more from growth from our product-led growth engine, just adding new customers to the business. We do this in perhaps the industry's -- one of the industry's best cost-effective models where we only spend 7-or-so percent of revenue on sales and marketing. So it's a very, very efficient model. And once we get customers, they stick with us and they stay with us for a long time. Our churn has been very stable over the past several years. It's in the 10%, 11% range, and it just doesn't move. Like it moved a little bit at the end of COVID, and it's come right back. And when our customers come, they're very loyal and they want to stay with DigitalOcean. And you've seen that from some of the examples that folks have shared with you through some of the videos. And we've now added an entirely new growth vector that will enable us to grow our revenue even faster with AI. While we built this business, we've done this in a very profitable and I'd say, disciplined manner. When Paddy talked about the Weighted Rule of 40 earlier and how that's a guidepost for us, that's not an academic exercise for us. It's actually how we measure the company. The entire employee base's bonus program is based on the Weighted Rule of 40. The executive's equity plan and performance equity is based on the Weighted Rule of 40. We believe that balancing growth, which is incredibly important for us right now with profitability is the right way to run this business over the long term. And we feel like we're doing a pretty good job of it so far. We exited 2024 with a Weighted Rule of 40 at just under 28%. That was on the back of 13% revenue growth and 17% free cash flow. As you've heard from the other folks today, we have the ability to drive that number up and improve it meaningfully over the medium term. But before I get to the medium term or the long term, let's just focus on the near term for a second. It's April 4, and we're a couple of days into the new quarter. As you saw in Q4, we had very strong results. We've started to show some really good momentum. That momentum carried forward into the beginning of 2025. So we just want to make sure everybody understands we're reiterating our guidance for both Q1 and for the full year. And while you may be disappointed, okay, we're just reiterating, you're not giving me a lot of color. It's only 4 days in. We're going to be talking more about this in May when we get to our Q1 earnings. But what I'll tell you is we're highly confident in our numbers and in our ability to perform versus expectations as we've set in our recent track record. This sets us up for a compelling medium-term outlook. We believe that we can drive Weighted Rule of 40 to 35% plus over the next several years. We'll do this by driving revenue growth to 18% to 20%. As Larry talked about, the contributors to this will be driving more new customers and boosting the front of the funnel and growing expansion with our existing customers. We'll do this in a very profitable way. We'll continue to keep the margins in kind of the same ballpark that they are today on both an adjusted EBITDA and an adjusted free cash flow basis. And we're very focused on improving the balance sheet strength. We have an incredibly strong balance sheet, but we're looking to further delever as we execute over the next several years. So I've talked to you about the foundation. I've given you a little bit of statistics about why I think that we have such a compelling role in the market. I've talked about the momentum that we're generating. And I've talked about the near-term outlook or the medium-term outlook. And so now I'm going to break this down a little bit more and describe some of the levers that we're using to grow this business. It all starts with reaccelerating organic growth. And by organic growth, I mean growth that's not driven by M&A and it's not driven by across-the-board price increase. It's growth from your customers and the addition of new customers. And this is an incredibly important thing when you think about the durability of the business. You can always do M&A, you can always -- every once in a while, maybe you can do a price increase, but the real measure of a business is how effective you are growing organically. And the challenge that the company has had, and Paddy alluded to this and Bratin had talked about it, is from the period of around, say, 2019 to when we went public and maybe for the first couple of years after, we did not invest in product innovation and did not kind of keep up with the pace of our customer needs. And so as a result, we saw a decline in our organic growth. Now this was masked a little bit because we did do a price increase in 2022. We also bought a big business, Cloudways, in 2022. So that was able to keep our growth up for a bit. But when you look at the actual organic growth in 2023, it had dipped to about 7%. Fortunately, with Paddy coming on, Bratin and Larry and the rest of the team, we've made a tremendous amount of changes, and we've been able to invert that and get that back to where we've driven growth from 7% in 2023 to 13% in 2024. And we feel very good about the trajectory that we're on based on all the initiatives that we've put underway. So to peel this back a little bit more and just to talk about the 2 components of organic growth, they're pretty simple. The first one is just getting revenue from the customers. We have an incredibly strong product-led growth engine that we've all talked about. And when you look at that and you think about that in the context of Paddy's 5 stages of evolution in the digital native enterprise ecosystem, you think about, okay, someone who's got a really strong product-led growth engine, they can count on it all the time. It just goes up and up and up. That's a phenomenal base to build on. And you can see that in our results from '21 to '23, we're just steadily crept up, and we didn't spend any more money. In fact, we spent more money probably than we should in some of the earlier years, and it just still continued to creep up. But the problem with that is if you're relying solely on product-led growth, the percentage growth that you get from that doesn't increase with the size of your business. So you start to cap out. And that's what you saw in our growth rates. Our growth rates started to decline. We were adding more incremental dollars every year, but it wasn't moving the needle as much. And so what you need to do is you need to add additional growth vectors to that. And that's what we've done. We've added 3 new go-to-market motions. We've also introduced a new product capability with the emerging AI capabilities. And so as a result, you see in 2024, we had a 26% increase in the dollars associated with new customer growth, which brought our percentage growth back to like 14%. And so the key for us on a go-forward basis is to continue to invest in those new go-to-market motions to take advantage of the AI opportunity and to use that to continue to drive about 13 points of growth from new customers. The second part of that formula is getting expansion from your existing customers. You have to -- in our kind of a business, cloud business, you have to grow your existing customers. You can't have a leaky bucket where customers are shrinking on average over the period. You have to be able to drive growth. And the company was very good at that in its early stages. But as you saw and I talked about in the earlier slide, the lack of product innovation, the fact that we hit a choppy market for a little while at the end of COVID, which I'd argue was probably more of our own execution, not innovating for our larger customers, and then we hit them with a price increase, we drove expansion the wrong way, and we actually had expansion as a headwind for the company in 2023. As I said before, we've largely turned that ship around and driven that such that we've made a huge improvement in 2024 around the net expansion. And we're positioned to have net expansion on the back of the progress we've made with these large customers be a positive in 2025. And then as we scroll that forward, not only will we continue to improve the performance of the core cloud and its retention with our customers, we also have the benefit of the emerging AI capabilities. As we get into InferenceCloud, as we get into more GenAI, these are more recurring, predictable businesses and the demand will grow versus a training workload where you don't know whether you're going to have the same kind of demand from one month to the next, that will also contribute to expansion. And so those last 2 points are incredibly critical. I'm going to pause here for a second because I think they're just so important. If you say, okay, why are you up there saying you're confident you're going to grow 18% to 20% over the next several years when you exited last year at 13%. And what I'd say is, I say, come back to the conversation that Paddy had earlier this morning about the 5 stages of company's evolution serving digital natives. We've built a company with over $800 million of ARR with 638,000-plus customers with 500 customers that spend more than $100,000 in ARR with 25 customers that spend over $1 million on a single growth vector, the product-led growth. That's the only growth vector we really had for the majority of the company's existence. On top of that, our product strategy was almost exclusively focused on small developers, which was great when the company started, but it ignored the needs of the larger customers and created that leaky bucket where we had defection challenges, and we just weren't growing with our existing customers. Fast forward to today, just 12 months, over the last 12 months, we've introduced 3 new go-to-market vectors, which are already gaining traction. We've refocused and reenergized the product innovation. You've seen all the great things that Bratin and team are cranking out and how well our customers are responding to that. And we have an entirely new growth vector with AI/ML. So as I stand here and as we sit here as an executive team and as a company, we feel really good about our ability to accelerate growth because we're a phenomenally different company than we were even just 12, 18 months ago. This leads us to a very clear revenue growth algorithm, and it just comes back to what I've said. We need to improve the rate at which we add new customers, and we're doing that. We're doing that by adding AI. We're doing that by widening the funnel, bringing on channel partners to bring in new bigger customers, widening the technology partners to bring in a lot of volume into our self-serve funnel. And we're doing it by also expanding the rate at which we can grow our existing customers. And you've seen that in the improvements that we're generating in NDR. This positions us well for 18% to 20% growth by 2027 and also puts us on a path to get back to, hopefully, at or exceeding the market level growth around 20%. So we've talked about the growth levers. And now we need to talk about the profitability side of this. As we've said, we're definitely focused on accelerating growth, and we believe we have a real clear path there, but we need to do it in a profitable way and continue to make disciplined investments. And so I'll take you through the cost side of the equation. To start, the company has a very strong track record of controlling and managing costs. I mean, I think that's pretty obvious. If you look at all of the key profitability metrics, they've all improved over the course of time since the IPO. We've driven improvements in gross profit despite the fact that in the recent years, we picked up more AI. It's still in its infancy. We're still in start-up mode and the margins are clearly not as high yet as they are in the cloud, but we've been able to absorb that and still drive gross margin improvement. We've driven a lot of adjusted EBITDA improvement, over 1,000 basis points of improvement there, clearly effective at kind of dialing our costs in and getting them under control. We've driven a lot of CapEx efficiency. You can't see it. I'll talk to it in a slide. But if you think about the improvements we've made in core cloud has enabled us to increase our investment in AI without really changing the CapEx intensity of the overall business. That's a pretty big feat in today's market. And we've also paid a ton of attention and been very, very focused on stock-based comp and cash flow generation so that we're returning value to shareholders. So I'm going to click through each one of those and give you a little bit more detail. Bratin talked about gross margin opportunity. So we have material opportunity over a multiyear period to improve our cost of revenue. The 2 biggest drivers of cost of revenue are colo and power in depreciation, which is basically just how the CapEx flows into the cost structure. From a data center and power standpoint, you've heard and seen us executing on the beginnings of our data center optimization strategy with our Atlanta facility that we just brought on in this past quarter. You may not have heard, but we're also consolidating one of our London facilities. We have a very expensive footprint, and we can manage that cost down over time while also being able to provide a lot more geographic coverage and do it in a more cost-effective way, and that's part of our strategy going forward. Bratin and team have also enabled us to get better utilization out of our existing fleet of infrastructure, both on the CPU side and the GPU side, and we expect that utilization to drive margin improvements over time as well. In addition to improving the unit costs, we have the ability to benefit from a mix shift in our business. So if you think of the unit cost of any one product that we sell, our PaaS products, our platform-as-a-service products, those have higher value. We charge more for those. There's more value added to the customer. They're growing 5x as fast as our Infrastructure-as-a-Service products, which means within our core cloud products, that will be 30% of our revenue in the next several years. If you look at within AI and Bratin talked about this, as we move to more platform and application layer products, they have higher value, they have higher margin. It will be a benefit to us in overall margins as well. And the fact that when we sell a GenAI product, it tends to pull through cloud revenue, which also has higher margins. So there's a lot of mix benefit that we believe that we'll get in over time. In the immediate term, clearly, the ramp-up of infrastructure on the AI side is a counterforce to that. But we believe with all of the benefits we have and optimization potential we have, we can mitigate that margin pressure. From an OpEx standpoint, we clearly have room to continue to drive operating leverage. As Bratin said, we've hired over 50% of our new roles are in lower cost, high talent markets. And we're now at a point where we've got over 60% of our -- or about 60% of our headcount is based outside of the U.S. in some of these markets. We've also done a really good job of controlling overhead, dropping G&A as a percent of revenue from 17% to 14% over the last several years. And we haven't even started to really benefit yet in a measurable way from the financial standpoint from the automation and the AI that Bratin and the team are trying to implement within our organization to make ourselves more effective in how we operate the cloud. With all of these opportunities, we're not only looking for kind of unit cost improvements around OpEx and CapEx, we're also looking to constantly reevaluate all of the dollars that we spend to prioritize those investments towards the highest priorities we have. And so you'll see us constantly looking at reexamining what we spend and reallocating resources to the kind of highest priority areas, which is a way of helping us to invest in new parts of the business and the higher growth opportunities without increasing the overall cost. This all leads to higher EBITDA margins. As I said, over 1,000 basis point improvement from '21 to 2024. And we believe that with the revenue growth that we're driving and with the investments that we're going to make in the business, offset by the efficiency that we're still driving, we can maintain EBITDA margins in approximately the same range over the next several years. From a CapEx standpoint, and I alluded to this earlier in my talk, we've driven a lot of improvement in capital efficiency in the core cloud. You can see that in the '21 to '22 decline. And it's masked a little bit even the amount that we improved in 2023 because we started to have AI spend as early as 2023. And now what you've seen is we've reinvested a lot of that efficiency gain into the AI platform. But if you look over the last several years, we haven't fundamentally changed the cost structure of the company. We're certainly spending a bit more capital now than we were, and we'll continue to spend about this level and say, the high teens, low 20s, but it's to drive growth that you're seeing on the results from our recent progress. All of this delivers solid free cash flow, and we are committed to continuing to generate healthy free cash flow. But what I'd leave you with is we are very, very focused on growth. And so if we see an opportunity to invest that has a good return and that has a compelling impact on our growth, we'll make that investment. And the plan that we're articulating to you right now is a perfect example of that. Getting from 13% growth today to the 18% to 20% growth over the next 3 years is 5 or 7 points of revenue growth. So again, if you say Rule of 40, what is that going to cost you? Well, it's only going to cost us a couple -- say, 3, 4 points of free cash flow margin. So even without waiting it on a straight Rule of 40 basis, we're driving more revenue growth than we're investing from a free cash flow margin standpoint. And we'll continue to make decisions like that as we execute over the next several years to maximize the opportunity for this business. So we spent the majority of today appropriately talking about the organic growth opportunities and that's clearly our #1 capital allocation priority. But I'm going to briefly talk to you about the other 3 priorities just so you have a fulsome picture. Those priorities are share repurchases, M&A and maintaining balance sheet flexibility. From an equity and dilution standpoint, I think we have a very good track record of putting shareholder interest at the forefront. We've reduced our stock-based compensation by over 600 basis points over the last couple of years, while at the same time, attracting and retaining and building a very, very talented executive team and talent across the technical organization and throughout the whole company that you're witnessing the benefit of and the impact of here today. We've also taken a lot of shares out of the market. We've done over $1.5 billion of repurchases over the last several years, reducing the share count by 13%. And we will continue to focus on these priorities going forward to offset dilution and continue to return value to shareholders. From an M&A standpoint, I'd say that M&A is certainly a part of our history. We've used M&A to accelerate revenue as we did when we acquired Cloudways. We've used it to accelerate the product road map, which we did when we acquired Paperspace. We will continue to look for selective accretive acquisitions to be able to accelerate our plans. But the plans that we've laid out to you today are based on organic growth only. So we're not predicated on acquiring our way into that. But if we were able to find an opportunity that was accretive, we would certainly take a hard look. From a leverage standpoint, I love this business model that we're in and coming from where I came from before in a very different industry. We're delevering right now, and I really enjoy it. And we're generating cash. We're growing EBITDA, and we are on a path already to get to less than 2.5x leverage by the end of 2027. Previously, I've stated a range, a target range of 2.5x to 3x is our long-term target. As we've evaluated our opportunities and taken a look at the market, we think that we can safely drive leverage under 2.5x, and we think that's a good target for us to have. People ask, and I'm sure there's a bunch of folks in this room that are going to ask in just a few minutes. Well, what about your current debt? You've got $1.5 billion that's due at the end of 2026. And again, every time I get this question, I start with this is the best debt instrument I've ever seen in my entire life. We have $1.5 billion of debt with 0 coupon, so we pay nothing. In fact, we make money on -- interest that we keep that cash in the bank. So people are paying us to borrow their money. I don't know if we'll be able to do that again, but it's a great thing to have. But what I can tell you is we're very cognizant of that maturity coming up at the end of 2026, and we anticipate addressing some or most of that or even all of that maturity at some point this year before it goes current. So I'll close with just kind of a recap of the plan and why we're really confident in this plan. Again, the strong performance we demonstrated in Q4, the momentum we've seen as we've headed into 2025 gives us confidence in our guidance for '25, and we've reiterated that. As you think about the new go-to-market motions that we've added, you think about the product velocity that really started to increase maybe just halfway through last year and the fact that those impacts have just started to impact our business. There's a lot of runway there. That gives us confidence in our medium-term outlook of driving 18% to 20% growth with mid-teens kind of free cash flow. If you think about it from a longer-term perspective and say, "Well, why do you think you can get back to at or above market level growth?" I think of the TAM. I think it is a giant market we're in. It's a giant market. Companies that are much bigger than us are growing faster than we are. We should be able to grow at least at the pace that they are. I think about the large customers where we're really just starting to get -- Larry talked about the concentric circles, and we're touching more of these customers. We haven't even really cracked the surface of that. And then I think about AI as a holy cow, you weren't even doing this 18 months ago. It's an entirely new growth vector. So we sit here thinking we are well positioned to get to growth at least the industry or better. And that's why we're hoping that as you see the potential shareholder value that will be created as we go on this journey, that you want to participate with us. So with that, I'm going to bring Paddy back on to close out the day for us or this session.

Thank you, Matt. I'm not going to talk to this slide. I'm going to just take a couple of minutes and speak from my heart. So hopefully, you had a really good 2.5 hours, and I want to bring us home by drawing a full circle to the journey that I started describing to you. I was brought here to really execute on steps 4 and 5 that I showed you in my first slide. Essentially, what that means is, number one, accelerate our growth by scaling with our largest companies because we know we have them in our base. We have 165,000 of them. And as you heard from Bartin, how we are starting to execute on our product development road map and how fast we are launching some really material features that these customers are absolutely hungry for. And you also heard from Larry how we are adding complementary go-to-market motions to really go after and service these large customers and make them successful on our platform. So with that one-two punch, I hope you've seen enough to give you the conviction that we are well on our way to accelerating our growth on the back of our scaling customers that are expanding rapidly on our platform. So that's number one. Number two is, there comes a moment in time in every company's history where there is a big seminal opportunity in front of us. For us, that is the world of AI, where our customers are facing this existential question around how is AI going to change their world. And right now, AI is super complex and super expensive for them. And they're literally asking us to democratize and get them access to AI in a way that they can consume. And to me, that is the world of inferencing. And you heard from Bratin how we are thinking about developing a full stack cloud. In fact, all of the boxes that you saw today are all in the hands of customers, and we will be going GA in a few weeks. We're getting tremendous feedback, great adoption with AI. And I feel very confident that over the next several quarters, we're going to be executing on our infrastructure platform and Agentic layer of our AI stack. So with that, I hope you got the conviction around we have a great team, we have an even better strategy. And the most important thing is we are executing, and we are executing with speed and velocity like I've never experienced before. And to think about it, our executive team has been together for only 2 full quarters. We're entering our third quarter together. And we had 2.5 hours of content to tell you about what we have accomplished in literally 2 quarters. So with that, I hope you have enough conviction to go and buy a little bit of DigitalOcean and spread the world. So we are really excited to, first of all, host you. Thank you so much for coming, especially in a week like this. We really appreciate that. We don't take it for granted. And now what we're going to do is set up a few chairs here so that we can transition to Q&A. And I've been asked to say the way we are -- yes, you can go ahead and do it. So the way we are going to do the Q&A is we're going to have a couple of mics. So if you have a question, please do raise your hands, and I will try my best to play MC, but we're going to have all the speakers up here on stage. And we'll be happy to take as many questions as we can in this compressed time frame. And as you all know, our earnings call is right around the corner. So we'll be happy to report our earnings. And Matt talked a little bit about our Q1. So we'll be happy to answer questions there as well. So with that, let's welcome the speakers back on stage, and we'll transition over to Q&A.

You have Mike Cikos from Needham. Two questions here. The first, on the 5% to 7% growth that we're looking for from those existing customers, how do we think about where that's coming from across the customer base? Should we be thinking about like 80% plus, 90% plus coming from the Scalers and Scalers+, just given the amount of dollars driven or the product adoption you guys are seeing? Or is that not necessarily the right way to be thinking about it when you're providing some of these targets here?

So let me start and then, Matt, you can add in. So there are 2 dimensions I think about, Mike. Thank you. First of all, thank you for your question. So when I look at the expansion, the expansion, the 5% to 7% Matt talked about from our existing customers. Those come from 2 different dimensions. One is customers, as you said, like Scalers+ and our -- as Larry talked about, we're pushing Builders to Scalers, Scalers to Scalers+, Scalers+ to $500,000 plus, $500,000 to $1 million plus, right? So we have a tremendous on-ramp of graduating customers from within our base and get them, do the cross-sell and upsell of our existing product features. And Bratin had a slide which talked about how the new product features are driving more incremental revenue every quarter, and that graph is going up to the right. The interesting thing there is not all features are inherently monetizable, right? So some features we charge for, and some features are just part of doing business with the DigitalOcean platform. But -- so that's 1 vector. The second vector is getting more workloads from existing customers. And then the third one is also we have a fairly robust set of AI customers and especially in inferencing, as they start scaling, we are also starting to see that footprint growing from our AI customers. So that's how I look at it. Matt?

Yes. No, I think you hit it right on the head, Paddy. I think the Learners population, that big bulk of customers tends to be just kind of hover and so most of the growth that we'll get in terms of expansion will be from the Builders, the Scalers and Scalers+ and the AI customers as we weave that into expansion over time.

And can I ask just one more. I think it was going back earlier in the presentation, but there was 240 basis points of signed -- or expected from being able to sweat your assets harder, prolong asset life using AI and it's a 2-parter here. So the first is like is that in any way contemplated in this calendar '25 guide? Or is this all just on the -- come as we think about '27? And then the second piece is like I haven't heard a company be that specific as far as the expected benefit coming from AI. And it reminds me of like if you go to calendar '22, a lot of software companies were saying, "Hey, we're in a different macro but we're being more cautious on spending. We're being more thoughtful, we're optimizing." Like 9 months later, all these software vendors that we're seeing, they were doing that, then so their customers start to do it, right? And so if I think about the growth targets that we have from you guys, are you in any way contemplating your customers getting smarter and how they're using you because of AI as well? Does that make sense? I'm sorry, long-winded question.

So there are 2 parts to the question, right? One is the sweating of the assets, and the second one is -- so maybe Bratin, you can start with the first one, and then I can take the second one.

Yes. So I think we are being very deliberate and methodical in how we are using AI. And so you saw all of those numbers, the productivity improvements. And that is allowing us to sweat assets for longer. And that is what is leading to that basis points improvement, the 240 basis points that we talked about. You asked a question about others haven't been that specific. And our motivation, our operating modus has been we got to instrument everything we do, and we got to make sure everything we do. Otherwise, you don't know what you're driving, right? And so that's just as Paddy mentioned, Larry and Matt mentioned, it's just a new operating discipline we have now where we're really measuring everything we are doing and making sure we're not just investing for the sake of investing.

And the second part of your question, Mike, is we anticipate, expect and we are enabling our customers to be smarter about how they're using our platform. The demo that Bratin showed about the Cloudways Copilot is exactly that. And as I was explaining to some folks over the break, one of the biggest drains of time for us as well as our customers is when there is a certain incident because cloud -- stuff happens on the cloud and when there is an incident, people generally spend hundreds of man-hours trying to figure out what is happening and where things are going wrong, trace it back to the development, check-ins and things like that. So that's what we are attacking first to say, okay, where is the most human effort going, and that's what we are impacting. And we are actually helping our customers to use us better and stop wasting time. So we are going to be a big catalyst and an enabler for our customers to use us more efficiently as well as build tools to reduce wastage and be more productive in their own lines of businesses.

Maybe for Paddy and Bratin, I want to ask you about the durability of growth. So many of -- durability of growth. So many of the dynamics that you're talking about, the value prop that you have for your customer base were true 5 years ago. And so help us understand what's different this time in being able to translate that value prop to growth? And on the AI piece specifically, I know you stripped that out of your NRR specifically because you tend to see variability in experimentation, 160% year-over-year growth. Help us understand the durability of the AI piece in particular.

Okay. Great. I'll start the big -- first question and then Bratin can surely answer the second one. So the first one, what is different this time? This is different, but all seriousness aside or all joking aside, the -- what is different -- and that's why in my recap, Gabriela, I said, what really matters is execution. And I think on the execution piece, we feel, given that all of us are real, very deep technologists, we feel we understand the needs of our customers at a very detailed level. And not only us, we have assembled a team of real world-class Tier 1 technologists and many of them are here in the audience today. So, one, I'm very confident that we understand what our customers are going through. Number 2 is, from an execution point of view, I think in 2 quarters, we have shown what we are capable of doing. And in fact, Bratin is accelerating his product delivery. That's number two. Number 3 is we've never had a real go-to-market outside of our product-led growth. So in the short time, Larry has been here. And I always say high velocity inside motion is really, really hard to nail. And there are very few people who have nailed it and scaled it and that's what we are trying to do. And we have a unique luxury that most companies don't have, which is our incredible customer base. And there are 2 things I had to take out from my presentation. Number one is the share of wallet we have with our existing -- even our successful customers, we have a lot of room to grow with them. And as we are bringing out -- like this week, we released the multi-cloud support, we can now connect to on-premise. We can connect across different clouds. And these are like really meaningful capabilities we never had. And this is what will enable us to get a bigger footprint with our existing customers. And as Larry mentioned, we are also doing things in a very efficient low-risk, high-yield manner both forming our base, but also bringing in channel partners. And he talked about how we have added 9 new Scalers, like $100,000-plus accounts from just 3 partners in, what, 90 days?

Yes.

And so I think these are all the reasons why I believe this time it's different. Not -- I mean the previous management team did incredibly great things in different dimensions. But in terms of accelerating our growth by scaling with existing customers, I think we are absolutely on the right track and we are picking up a lot of momentum. And the #2 thing is something that Bratin can answer from an inferencing point of view.

And as we were saying, we are also focused a lot more on inference, that's a production application. It's much harder, you won't want to usually take a production application out and go away. And we have had some really interesting situations where people have seen us -- going back to the previous question, there's actually a customer we are talking to now. They've seen us use AI for a particular thing that we're doing, and they actually want to adopt it inside their own company as well. So these are, as we move more towards -- let's not just build AI let's use AI, the use is about getting business value. And once you've gotten business value, it's production. And once it's production, it doesn't get taken away. And that's really the reason why we didn't chase after those big training workloads, as Matt was talking about, but really more focused on the inference side of the house.

Raimo Lenschow from Barclays. Two questions. First, if you think about you moving upmarket like -- and obviously, then you have more complexity with the customers, like how do you decide how far up do you want to go? And kind of what's the competitive landscape then? Is it just taking [ low end ] of AWS, et cetera? But there are also like other clouds that are playing in there. And then I had one follow-up.

Yes. So I don't think of this as low end and high end. What our customers want, we will deliver regardless of however high end they are. And I feel like we have the self-confidence that we can measure up to any cloud provider, and we are winning cloud workloads from any hyperscaler all day long. And you saw Bratin's presentation where we're not going to be shy about landing a few punches, and I feel we will scale up and down the right side of digital natives. And the other beautiful thing about digital natives is that it's not bogged down by the complexity of the size of the customer. It could be a very small company, and you have 4 customers of ours outside in the customer showcase, their spend and sophistication on the cloud is the same as any large enterprise brick-and-mortar company that you will encounter. So it's really about understanding the needs of those customers and being customer obsessed, like for example, we are working with a customer who called me and Bratin over the weekend. And we are talking, that CEO has our -- he's texting with us. And they're not even that big of a customer, right? So I think the way we are scrappy, hungry to get these workloads with these large companies, I don't think there's any limit in terms of going up and down the stack in terms of complexity. And we also feel like we have the right inside sales motion to be able to do that without having to bring a bunch of new skills into the company.

And I'll just add one other thing, just from having dealt with a lot of cloud customers, if you're giving them lower cost and similar SLA and similar experience, they'll come. Like I haven't yet met a customer who said, "Hey, you know what, I'm paying too little, let me." So the other thing Paddy mentioned this in his answer is just like this week, we enabled multi-cloud support, where a customer can now run a workload securely on hyperscaler [indiscernible]. What that means is it makes Larry's migration motion a lot easier because you don't need to do a lift and shift, lock, stock and barrel, you can start moving some of your workloads in the newer workloads. So for us, it's really about listen to the customer, iterate as quickly as we can and bring value to them.

And then you kind of -- that leads straight into my follow-up, like you mentioned earlier, and that was the discussion we had with your previous team as well that you kind of need more product, you're a tech company, you need more. How much do you need to lean in on marketing and kind of showing the customer base again that you are a different beast compared to like as you said earlier, the last few years, there was a little bit of a -- you fell down a little bit there. Like how is the perception in the market can you do there?

Great. Great question. Thank you. I was hoping someone will ask this question because we have such an incredible developer mindshare. Not only that, all the stuff that Bratin is building, so we'll have a chance to talk to Wade in a second. But my challenge to our marketing team was to say, hey, how do we translate. So two things. One is we have to be able to tell the story of all the great features we are building. So we are now averaging at least one deep dive technology webinar a week. And the second -- and along with, we have -- we publish like 3 or 4 articles every day. You can go look at our blog. It's just an incredible repository of technology information. The second thing is we are publishing -- my challenge to the team was, "Hey, I want one customer case study every week." And the team is over-delivering on that. So you can go and look it up and it's a rich repository of different use cases, different workloads, different things. So we are waving our flag very, very aggressively with our existing customers. And the last thing I'd leave you with is we have just started measuring this thing called share of voice, which is measured by a third party. And as I think it was in one of the presentations, I think Larry's presentation where we are actually bouncing and punching above our weight class pretty consistently. Like most weeks or many weeks, we are in the top 3 of cloud, along with the hyperscalers, above many hyperscalers. We are in the top 3 in terms of share of voice in both cloud and AI. So I feel we are right with a big voice out there in terms of controlling and changing and dictating the narrative when it comes to cloud and AI adoption.

Great. Jaiden Patel from JPMorgan. To follow up on a previous question. In that NDR of 105% to 107%, what's the assumption for the cohorts below Scalers? And then I have a follow-up.

Matt, do you want to answer that?

Could you repeat the question?

The question was about 105 to 107 -- first of all, it's not 105% to 107% NDR. It is 5% to 7% from expansion, which has NDR and also expansion of other customers that are not part of NDR, which is like the AI workloads and things like that. The question was the makeup of the expansion number.

Yes. And Paddy, just -- you hit the main points. I mean, clearly, we're working aggressively to get the NDR for the whole business above 100%. And we got very close to that at the end of last year, we talked about the core cloud -- traditional cloud business, was above 100% in Q4. That number needs to be in the low hundreds, right, for that for to get 5 to 7 points. The balance of that is going to come from the AI expansion. As we get AI customers, if you said today, why don't you include it, and we've answered this a number of times. One, a lot of those customers aren't even a year old, so they wouldn't even be in NDR cohort anyway. But two, the majority of our early customers were training workload oriented, and it's just more sporadic usage. So between those 2 things, getting core cloud NDR into the low hundreds and getting the benefit from the more recurring revenue from inferencing and our genAI solutions. That's how you get to the 5% to 7%.

Great. And as a follow-up, how do you think of the guide and the long-term model, given the current developments and economic policies, what are you baking in around that?

Go ahead, Matt.

So I think everybody in this room is probably doing a similar assessment of what does this mean? I mean it's been a couple of days, the landscape is shifting. You don't know what the implication is going to be. But what I can tell you, as we've thought about it is the majority of the focus on tariffs appears to have been in more hard goods and manufacturing. We're a digital native business ourself. Our customers are digital native businesses. There's a lot of software, there's a lot of kind of technology, but there's not a lot of physical goods. And so when you think about, they say, well, that's your first order and maybe second order effect, but what about components? What about servers, what about data center? And we're still going through that kind of assessment. We're in active conversations with our leading suppliers, many of whom have manufacturing facilities in the markets in which we operate. So for our U.S. data centers, we're buying gear that's primarily built in the U.S., and we can do similar with a lot of our companies that we buy from are global companies and we can procure from in-region locations. So I'd say it's way too early for us to conjecture. But what I can say is that if you look at the customer base we have, and you say, "Well, what happened the last time there was a disruption?" You'd say, "Well, you guys went backwards in terms of expansion." A lot of that was us. We weren't innovating. We weren't kind of delivering on our bigger customers. We did a price increase. Right at the same time, everybody was optimizing. There was a lot of self-inflicted wounds. But if you kind of you peel the onion a little bit. You look at the core customers, you're like, you still added customers at pretty much the same rate you were doing before. And the core NDR outside of the really big customers was pretty consistent. And so we feel like the diversity of our customer base, the fact that we're not concentrated in any one region that we're hopeful that it's not a huge impact to us. But I can tell you we're still evaluating, and we'll keep everybody updated as we learn more about what the potential impact would be.

Tom Blakey with Cantor. Great presentation, by the way. Just maybe double-clicking on the large cohort. You guys seem to have done a great job of funneling that down even to the 8,000 named accounts and whatnot. I'd be curious to kind of see after -- I know you've only been together for a short period of time, but one of the unique parts about this story is that you could be mining an existing customer and keeping that customer from churning and getting that double benefit of having them expand on your platform. What does that look like when you go from 630,000 to 165,000 to -- is it 100 customers that could spend $10 million? Is it 1,000? Just would love to just double-click on that...

Yes. Maybe I can start and then, Larry, you can talk about how we selected those customers. So I don't think there is -- we need to put a cap on how many customers can spend how much. I mean I'll just give you a number. It's -- our share of wallet has a lot of great opportunity for us. So even with our larger customers, there are so many more workloads that we can be migrating to our base. And Larry, why don't you talk about how -- so in the 8,000, we have 3,000 which are our top spending accounts. And you want to talk about how we selected the other 5,000?

Yes, basically, we look through hundreds of thousands of customers and said, of those customers, let's look at what attributes drive growth. And so you have the top 3,000, and you say, okay, they have certain attributes. They use so many data centers, they use these types of products. They grow at this rate velocity. They're in these types of markets, have these types of domains. So you take that, then you kind of create an anatomy and you push that through the hundreds of thousands of customers and then the ones that rise to the top, have the characteristics most like those 3,000. And we just said, hey, let's start with 5,000 and kind of back to our nail it before we scale it. And as we continue to prosecute those accounts and drive expansion, then we can continue to open that aperture. So we picked the 5,000 given that we had -- based on the account managers we have, we felt it was a pretty good ratio of coverage. But as Paddy mentioned, as we start to realize growth and expansion, we can continue to expand that.

When you -- and just to be clear, when you look at this large -- that's a pretty large cohort of customers, they could spend 7 figures on your platform, the current existing product...

Absolutely. Yes, yes. And we had a disclosure on that today. Yes, absolutely.

And then maybe just double-clicking on the free cash flow margin, the longer-term target of 20% growth, obviously, investing there to get there with the mid-teens free cash flow margin. Just maybe rank order or talk about the top kind of drags in terms of the investments needed there that would like pull back the free cash flow margin to hit 20% plus growth? And maybe specifically, does the free cash flow margin kind of framework that you set include the refinancing of the convert?

Yes. So that's a great question. All of the numbers that we showed here were unlevered. So the margin targets we're talking about, the Rule of 40. We're talking about unlevered free cash flow. It doesn't include the interest. If you think about the interest, though, again, when you take the leverage target that we're talking about and the growth rate at which we're performing, that will be kind of low single digits over this kind of period of time. If you assume we're not refinancing the full $1.5 billion and we're going to bring that down, and we're going to continue to drive leverage down. But your -- first part of your question was around what's driving the decline from where you are today to mid-teens. And it's giving ourselves the ability to increase capital for AI. It's giving us -- ourselves the ability to invest in R&D to accelerate the product road map. It's giving ourselves a little bit of opportunity to bolster the sales and marketing investments if we see demand. And basically, what we said is we think we can deliver the 18% to 20% revenue growth and only consume about that much incremental free cash flow. Now can we tell you, well, did it come out of gross margin? Or did it come out of OpEx or did it come out of CapEx? It's a little bit early for that. We're still doing a lot of nailing before we scale. And we just want to make sure we set expectations that it is going to cost us a bit, we think. And as I said in my presentation, this is a plan that we think delivers 18% to 20% growth. If we saw an opportunity to accelerate that or to get there faster and it had a good return and it had good economics, we'd make that investment. We communicate it to folks. But the plan that we're on right now, we believe we can get to 18% to 20% with only going down to mid-teens free cash flow.

Josh Baer with Morgan Stanley. Great presentation and detail. I wanted to ask one on total cost of ownership. Obviously, a very compelling piece of the value proposition, but I was hoping you could unpack that. How much of that is a cost -- like a structural cost advantage versus a pricing decision to take lower margin? And then the follow-up would be like why is 30% the right level of benefit versus the hyperscalers, why not 40% or 20%?

So I can start and then you can talk about the actual structural things. So 30%, we -- that's typically what we see from our customers. But there are several customers that have significantly more cost savings realized, and then there are some depending on the nature of the workloads, how they use certain aspects of our platform, it could vary by the workload. I think the most important thing is I think Bratin had a slide which talked about the fact that there's cost savings, like pure savings based on our pricing model and our packaging. The second thing is cost avoidance based on certain things that you don't have to do with us compared to a different, more complicated cloud.

And it's about half and half. So the cost of the infrastructure, the lower cost is -- out of that 30% is about 15-ish percent. The remaining part of that is the cost avoidance. We have many customers who get way more than 30%, like Picap that Larry talked about, they get like 70% cost savings. So that 30% is kind of low watermark. We have lots of customers that get lots more.

And I think the other part of maybe your question, Josh, was around we -- I just want to be explicit. We have not changed our pricing, right? We've changed some packaging here and there, which every company does. But in terms of pricing after the last price increase, it's not that we have changed the pricing. We lowered the pricing to get more discounts to our customers. That's not how we have accomplished this. This is like truly when you look at like the bundled offering of a Droplet where you get a little bit of compute network storage, there are some fundamental differences in how we package our cloud versus some of the other cloud providers.

And then just wanted to clarify, the 240 basis points of sweating the assets and 110 from other optimization, what time frame is that? Is that looking through '27?

So the work -- do you want to take that, or do you want me to take that?

So that's -- I think that's a combination of the impact that we've already had in terms of increasing the useful life of the gear. And it's also a view kind of into the future over the next couple of years. What I can tell you is in the 2027 guide, we haven't fully baked in the cost efficiencies that we think we can drive in gross margin. We haven't baked in the full data center optimization initiatives, and we haven't baked in a lot more in terms of the utilization improvements.

Yes. So it's -- those numbers are, in some ways, the tip of the iceberg of what we can get.

We have time for...

One last question, please. Yes, go ahead.

Kingsley Crane from Canaccord. On products, you have 50, 60 meaningful releases per quarter. Velocity has increased 5x over the past year. It's amazing. It's clearly helping some of your largest customers expand. But if we think 2 to 3 years out, the platform could look a lot different. So I'm curious how you balance that innovation with ensuring that the product can stay simple.

Yes, great. Bratin?

Great question. So that -- we have a very good process established to make sure that we pay a very high amount of attention during the design, during the consumption, during the shipping to certain fundamental product tenets we have in terms of what the user experience should be. How quickly can you set something up? What does it take you to set your access control rights? What does it take you to allocate all of the resources you need? So that's just part of the process of how we are developing the products and how we review the products and how we continue iterating on it based on customer feedback. And a good validation of that is if you look at our generative AI platform, it's like takes half the time to get it done. So that shows that as we launch more features, we are going to continue to stay true to that tenet.

Great. Thank you, Bratin. And that's -- unfortunately, that's all the time we have for Q&A. We are going to be around. So let me just wrap this up by saying thank you, panelists, and I'm going to welcome Wade Wegner, who is our Chief Ecosystem and Growth Officer, just to give us a little bit of idea on how the customer showcase is going to work.

Right. Thank you, Paddy. Hello, everyone. My name is Wade Wegner, Chief Ecosystem and Growth Officer. But today, I'm actually your ambassador to our customer showcase, which is going to take place right outside here. So we are very fortunate to have 4 customers who are going to spend time with all of you answering questions, talking about their business and talking about how they're growing on DigitalOcean. So first, we have Autonoma, which offers an all-in-one solution to digitize machines, processes and the customer experience. We have NoBid, who connects publishers to advertisers through an advanced auction optimization platform. We have Picap that is the leading rideshare company in Latin America also providing logistics. And Scribe uses AI to make process documentation effortless. And so here's how it's going to work. If you all take a look at your badge, you should have a number on your badge, on either side of it. That number is going to correspond to a customer, and that customer is all set up out there in a booth. So when we get started, you're going to hear a bell ring and please proceed to that booth, where you're going to get a short presentation from the customer, and you'll have an opportunity to ask some questions as well. When the bell rings again, we're going to shift clockwise to the next booth and pretty steadily, you'll be able to hear from all of our customers. And so yes, it should be a lot of fun. We really hope you enjoy talking to these customers. We actually now also have a 10-minute break before we get started. So we have some food and light refreshments, so please avail yourself of that, and we'll get started soon. Thank you so much.