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I’ve been tracking the rise of neoclouds in the past couple of years, and I’ll start by admitting that the numbers are genuinely staggering. Synergy Research Group reports that neocloud revenues hit $9 billion in the fourth quarter of 2025 alone, up 223% year over year, exceeding $25 billion for the full year. The market is forecast to approach $400 billion by 2031 at a sustained 58% compound annual growth rate. ABI Research puts GPU-as-a-Service revenue from neocloud providers on a path to surpass $250 billion by 2030, up from $42 billion in 2025, and forecasts more than 2,200 neocloud-operated data centers in operation globally by 2035, up from just 558 in 2025. CoreWeave alone has eclipsed $5 billion in annual revenue faster than any cloud platform in history. These numbers describe the fastest-scaling category of infrastructure the industry has ever seen.
So what is a neocloud, exactly? In short, it is a specialized cloud that focuses entirely on serving up AI-based systems. Instead of the thousands of services a hyperscaler offers, a neocloud delivers GPU compute, high-bandwidth memory, and fast interconnect purpose-built for AI training and inference workloads. Providers such as CoreWeave, Lambda, Nebius, and Crusoe deploy dense GPU clusters with liquid cooling and high-speed networking that can be brought online in months rather than the three to five years a traditional hyperscale data center build requires.
They are, in effect, an architectural response to the fact that AI workloads impose rigid constraints around parallelism, locality, and compute concentration that generalized cloud elasticity was never designed to serve. I see a clear place for them in the modern enterprise architecture life cycle, sitting between public cloud hyperscalers and managed service providers (MSPs), a purpose-built layer for the specific AI workloads that neither hyperscalers nor MSPs serve efficiently.
