Neocloud

Overview

The neocloud emerged from a simple gap in the market. Hyperscalers built their platforms around general-purpose computing, hundreds of services spanning storage, databases, and web hosting, with GPUs added later as one offering among many. When AI demand surged, a new generation of providers appeared that inverted the model entirely: GPU compute as the product, everything else in service of it.

How it works

A neocloud concentrates its engineering on the things AI workloads actually depend on: current-generation accelerators, high-bandwidth interconnects such as InfiniBand, parallel storage that keeps GPUs fed, and access models suited to sustained training and inference rather than bursty web traffic. Many offer bare metal access, sidestepping the virtualization layers hyperscalers rely on. Pricing tends to be structured around reservations and committed capacity, reflecting how AI teams consume compute, in large sustained blocks rather than fleeting instances.

Why it matters

For teams whose workload is AI, the specialization pays off directly: newer hardware available sooner, interconnects engineered for distributed training rather than adapted to it, and pricing that reflects GPU economics instead of general cloud margins. Neoclouds have also broken the geography of compute open, appearing in regions the hyperscalers overlooked, which is what makes regional, sovereign GPU capacity possible at all. The category is proof that in the AI era, focus beats breadth.