DigitalOcean Rethinks the AI Cloud Around Storage and Velocity

As new AI cloud providers appear, many compete on a single number: how many GPUs they can rent by the hour. Dan Brown, who leads hardware engineering at DigitalOcean, sees the real contest elsewhere. In a recent TechArena Data Insights episode, Solidigm’s Jeniece Wnorowski and I talked with Dan and Solidigm account executive Ty MacAdam about building and delivering holistic data center infrastructure solutions for AI workloads. Dan's argument was consistent throughout: raw GPU capacity means little if data cannot reach it quickly, and that is where DigitalOcean is placing its bet.

The Data Velocity Problem

Dan frames the core AI infrastructure challenge around three factors: where and how data sets are captured, how quickly that data can travel from its source into an AI cluster, and how fast systems can exchange tokens between systems in the AI cluster to reduce the data down into a meaningful set that can be used for inference. When any of those steps drags, the entire pipeline slows.

“The AI solution designs are limited by what we call data velocity,” Dan said. “How quickly can you get your data in, transform it, and get it back out for next useful step in the pipeline.”

Much of the friction, he noted, comes from customers trying to run modern AI against aging storage. Many arrive with legacy arrays that have been in service for a decade or more, then discover that infrastructure cannot keep pace. Moving that data into DigitalOcean’s high-performance block and object storage, or directly onto the AI machines, is where the gains show up. Solidigm supplies the high-speed NVMes, including PCIe Gen5 drives, that sit close to the GPUs and keep the read and write cycle tight.

More Than a GPU Landlord

Dan is direct about how DigitalOcean differs from the wave of providers rushing into AI hosting. Many, he argues, do one thing only.

“They’re taking GPUs and servers, shoving them into boxes and then selling them by the hour to customers,” Dan said. “We call it a GPU landlord.” With those providers, he explained, customers must bring their own data scientists, hardware engineers, and expertise to make the hardware useful.

DigitalOcean’s answer is a full suite of compute and storage products that surround the GPU and share the same backbone network and data center, tightly coupled to improve velocity. Rather than selling raw capacity by the hour, Dan said, the company is building a unified cloud where every resource around the AI cluster exists to make that cluster faster and more cost effective to run.

Preference-Aware Routing

A clear expression of that approach is DigitalOcean Inference Router, which reads the intent behind each request and matches it to the model that best fits the developer’s task and priorities, whether they are optimizing for quality, cost, or latency, without any application-side routing logic. The approach is grounded in years of preference-aware routing research from the team behind the open-source Arch-Router work, now part of DigitalOcean. Dan described the result as token as a service, and he said the productivity effect has been striking.

Dan said the productivity gain has been dramatic: teams can now prototype a working product for a few hundred dollars in minutes, rather than standing up entire teams of administrators and data scientists to build the back end first.

Density and the Nanosecond

Asked what organizations still overlook, Dan pointed to density and reached for a piece of computing history. Admiral Grace Hopper famously wore an 11.8-inch-long coil of copper wire. She used this to illustrate that for every such length of copper in a system, a nanosecond of delay is introduced in that system. Across a large AI cluster, Dan noted, miles of copper and fiber add up to real delay.

DigitalOcean’s response is to build the densest possible clusters, adopt liquid cooling, and keep local Solidigm storage on each server so data moves host to host in microseconds rather than milliseconds. That translates directly into shorter compute time, faster time to first token, and lower customer costs.

Ty reinforced the point from the storage side. “Storage used to be a passive layer where everybody just thought of it as where the data would sit. That doesn’t hold up anymore,” he said. “Storage isn’t where the data sits. It’s more about how fast intelligence moves.” He added that Solidigm’s high density quad-level call (QLC) NAND puts more data closer to compute at lower power, which reshapes the total cost of ownership math at deployment scale.

The TechArena Take

DigitalOcean’s message to technology decision makers is a useful reminder to a market fixated on GPU counts. Buying accelerators and fast networking is only part of the equation. If data cannot reach those GPUs quickly, or if teams overspend on oversized models for narrow jobs, the investment underperforms. Refreshing legacy storage, right-sizing a model to a task, treating proximity between data and compute as a design decision rather than an afterthought are practical places to start to get the most out of an AI infrastructure investment. As agentic workloads raise the pressure further, the providers building for velocity, not just capacity, look best positioned for what comes next.