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With hyperscalers consuming large portions of grid capacity, neoclouds in regions such as the Nordics and Germany are increasingly securing new power allocations for AI-focused builds
In sum – what to know:
Cost, sovereignty, and faster GPU access drive adoption – offering clear advantages for AI R&D, model developers, and regulated industries.
Hyperscaler lock-in and limited ecosystems remain major barriers – making large enterprise migrations difficult despite neoclouds’ pricing and performance benefits.
Significant growth expected in greenfield and edge AI workloads – with consolidation likely to reshape the competitive landscape over the next five years.
As AI workloads grow and regulations sharpen across global markets, neocloud providers are attracting new attention from enterprises and governments. In an interview with RCR Wireless News, ABI Research principal analyst Reece Hayden outlined the forces pushing organizations toward these GPU-focused clouds, as well as the challenges that could limit long-term market share.
Cost is the most immediate driver. Hayden notes that “neoclouds have much, much lower cost per hour basis for GPU capacity,” an increasingly decisive factor for organizations training large models or deploying inference at scale. For R&D teams and AI-native vendors, access to cutting-edge GPUs is often faster through neoclouds than hyperscalers, particularly when new accelerators reach the market.
Sovereignty is another motivator, especially in Europe. Hayden highlights growing regulatory influence: “Transparency around sovereignty and cloud usage is another factor … where the EU AI Act and industry-specific regulation [are] coming into how enterprises have to be deploying their workloads.” Neoclouds position themselves as flexible and compliant alternatives, enabling customers to keep data and compute within specific jurisdictions.
Power availability plays an unexpected role. With hyperscalers consuming large portions of grid capacity, neoclouds in regions such as the Nordics and Germany are increasingly securing new power allocations for AI-focused builds. Hayden explains that this directly links to reliability and service continuity — critical factors for enterprise deployments.
However, neoclouds face substantial strategic hurdles. Hyperscaler lock-in remains the biggest: “Moving [workloads] from a hyperscaler domain into a neocloud … is one very costly, operationally challenging, often regulatory constrained,” Hayden says. Egress fees, architectural dependencies, and entrenched enterprise workflows make large-scale migrations difficult, according to the analyst.
Global scale is another obstacle. Multinational companies expect consistent infrastructure across regions, something most neoclouds cannot yet match. Neoclouds also lack the full-service ecosystems—storage, networking, CPU compute, databases — that enterprises depend on. Hayden notes that while some providers are climbing the stack, “at the moment, it’s very immature compared to where the hyperscalers are.”
Looking ahead five years, Hayden expects neoclouds to gain substantial share in greenfield AI workloads, particularly inference and edge deployments. He cites edge AI as a major opportunity, as enterprises push compute closer to metro locations and end applications. Neoclouds’ faster decision cycles and funding structures make them well-positioned to acquire or partner with local data center operators.
Hayden also predicts significant consolidation among smaller neoclouds, potentially driven either by large neoclouds or by hyperscalers seeking to add AI-specialized capacity.
The trajectory of neoclouds will ultimately depend on how quickly they scale capabilities and position themselves in regions where regulation, power availability, and AI workload growth intersect.
