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Neoclouds tend to be smaller and more regional compared to hyperscalers, with strong traction in sovereign data center projects
Neocloud providers have emerged as one of the most closely watched segments in AI infrastructure, offering purpose-built GPU capacity at a moment when demand for compute continues to surge.
In an interview with RCR Wireless News, Reece Hayden, principal analyst at ABI Research, explained how these providers position themselves relative to traditional hyperscalers, and why their architecture and business models set them apart.
“Neoclouds at their core are really just AI infrastructure provision. And it’s really about providing GPUs in the most effective way in purpose-built architecture to enterprises, AI ISVs, AI vendors,” the analyst said. Rather than focusing on services or software ecosystems, Neoclouds lead with hardware availability and GPU-centric architectures designed specifically for AI training and inference, according to Hayden.
Their consumption models vary, ranging from bare-metal GPU rental to colocation arrangements where the neocloud supplies the servers, racks, and cooling. Hayden notes that “neocloud is providing… the infrastructure, the servers, the racks to enable the cooling, all of the different factors you need to deploy and implement GPUs into data centers.”
The contrast with traditional hyperscalers is sharp. Hyperscalers remain broad service platforms rooted in CPU-based general-purpose compute. Hayden points out that many in the industry describe hyperscalers as “CPU clouds,” whereas Neoclouds design infrastructure around GPUs from the ground up. Scale is another defining difference. Hyperscalers are global; neoclouds tend to be smaller and more regional, with strong traction in sovereign data center projects, particularly in Europe.
Neoclouds also emphasize simplicity and predictability in pricing — something many enterprises struggle to secure from hyperscalers. Their business models revolve around transparent GPU-per-hour structures. They also rely heavily on open-source software, while hyperscalers often deploy proprietary managed services that make it harder for enterprises to switch, the analyst added.
Despite these advantages, their current market presence remains limited. Most neoclouds have been operating for only three or four years, and their customer base is concentrated in AI R&D labs, AI vendors, and model developers—not large enterprises. Hyperscalers dominate enterprise demand due to their full-stack ecosystems and the cost of moving existing workloads.
“Enterprises are very much constrained within the hyperscalers. That’s not only because of the ecosystem around that, but it’s about the pricing and around the egress fees, the ingress fees, all of those different things really constraining a lot of the enterprise market towards the hyperscalers,” Hayden said.
Global scale is another barrier. Multinational companies require consistent deployments across regions, something most Neoclouds cannot yet offer.
Still, Hayden emphasizes that neoclouds have carved out a distinctive role: GPU availability, rapid deployment, transparent pricing, and architectures purpose-built for AI. While they do not yet rival hyperscalers in scale or enterprise reach, they represent a fast-growing challenger segment within the broader AI infrastructure ecosystem.
