Focus: Neoclouds
The rise of the Neocloud: Where connectivity will defi ne the next phase of AI infrastructure
By Dr Thomas King, CTO, DE-CIX W
e’re used to technology moving at breakneck speed, but the blink-and-you’ll-miss-it pace
of AI is restructuring the cloud landscape in ways we could have not imagined even two or three years ago. Demand for GPU- accelerated compute has surged and, to meet that demand, a new class of cloud provider has emerged to fi ll the gap leſt by hyperscalers – oſt en constrained by capacity, cost or geography. T ese platforms were fast to market,
focused on performance rather than breadth – and highly specialised. In their early phase of life, they were loosely grouped under labels like “AI clouds” or “GPU clouds”, but neither phrasing fully captures the new generation of cloud they represent. T is is a pattern borne out over the ages – practice fi rst, defi nition second. Vendors and operators were
experimenting with soſt ware-based networking long before soſt ware-defi ned networking emerged, and compute was
inching closer to users for a decade or more before the term “edge computing” became widely accepted. Now it’s the cloud’s turn. T e term “neocloud” has now become embedded in the industry vernacular, to describe a distinct category of cloud infrastructure providers that are purpose-built for AI workloads, operating independently of hyperscale platforms and traditional enterprise hosting models. As consultancy fi rm McKinsey puts it: “Hyperscalers have secured the lion’s share of advanced-chip allocations to manage advancing AI workloads, leaving many AI start-ups, research labs and enterprises unable to access capacity at the speed they require” – and this is the very gap that neoclouds are aiming to fi ll, but in order to do so sustainably they’ll need to think about more than just raw compute power. Neoclouds might just have made it
into our lexicon, but they’ve grown at extraordinary speed in the past couple of years, with some estimates pointing to
year-on-year growth exceeding 200% during the height of the AI infrastructure boom, and projected revenues approaching $20bn in 2026 alone. T at momentum, however, is quite
fragile. As hyperscalers expand supply and competition intensifi es, the limits of a “bare metal fi rst” strategy – in which hardware is king – are becoming increasingly apparent. Hardware alone doesn’t off er a long-term advantage, considering the rate at which it depreciates, which means that, in order to be successful and avoid going head-to-head with dominant hyperscalers, they need to think beyond raw compute power and instead consider data sovereignty, agility and optimised connectivity. T is year, the defi ning characteristics of neoclouds will extend beyond GPUs to include network architecture, interconnection strategy and the ability to operate as a trusted part of a wider digital ecosystem.
The “bare metal” reality check T e fi rst wave of neocloud growth was driven by an urgent shortage of AI-ready compute, but that window is narrowing fast. While the market expanded at breakneck speed, the underlying economics are becoming more challenging. Bare-metal GPU services are increasingly exposed to price pressure as hyperscalers expand capacity and competition intensifi es, while the useful lifespan of accelerator hardware continues to shorten due to the sheer speed of innovation. T at makes it diffi cult to sustain margins through hardware alone (Bare-Metal-as- a-Service, or BMaaS), particularly when neoclouds are smartly positioning themselves as alternative sources of raw compute. As a result, many providers are shiſt ing
their focus toward AI inference workloads, which promise more predictable demand,
08 April 2026
www.electronicsworld.co.uk
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