"Gartner estimates that by 2030, neocloud providers will capture around 20% of the $267bn AI cloud market. Neoclouds are purpose-built cloud providers designed for graphics processing unit (GPU)-intensive AI workloads. They are not a replacement for hyperscalers, but a structural correction to how AI infrastructure is built, bought and consumed. Their rise signals a deeper shift in the cloud market: AI workloads are forcing infrastructure to unbundle again."
"These pressures are no longer marginal. They create a market opening that neoclouds are designed to fill. Control over cost visibility, through transparent, consumption-based GPU pricing that reduces overprovisioning and exposes the true economics of AI workloads Control over data locality and sovereignty, by enabling regional or sovereign deployments where regulatory or latency requirements demand it Control over workload placement, by allowing organisations to deliberately orchestrate AI training and inference across hyperscalers, neoclouds and on-premises environments based on performance, cost and compliance requirements."
Neoclouds are purpose-built cloud providers optimized for GPU-intensive AI workloads, offering specialized infrastructure rather than replacing hyperscalers. AI workloads require different design priorities than traditional compute, with hyperscalers optimized for breadth and general-purpose services, creating friction when running GPU-heavy AI tasks. Neoclouds address cost visibility through transparent, consumption-based GPU pricing that reduces overprovisioning and reveals true workload economics. They enable regional and sovereign deployments for data locality and compliance, and allow deliberate workload placement across hyperscalers, neoclouds, and on-premises environments to balance performance, cost, and regulatory requirements. Neoclouds compel organizations to shift from cloud strategy to AI placement strategy.
Read at ComputerWeekly.com
Unable to calculate read time
Collection
[
|
...
]