For the past decade, hyperscalers have defined how CIOs and IT leaders think about their organization’s cloud infrastructure. Scale, abstraction, and convenience became the default answers to almost every compute question. But artificial intelligence (AI) is breaking the economics of cloud computing, and neoclouds are emerging as the response.
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 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.
This is not a return to on-premises thinking, nor a rejection of the cloud operating model. It is the next phase of cloud specialization, driven by the practical realities of running AI at scale.
Why AI breaks the hyperscaler model
AI workloads differ fundamentally from traditional organizational compute. They are GPU-intensive, latency-sensitive, power-hungry, and capital-heavy. They also scale unevenly, spiking for model training, throttling for inference, then surging again as models are refined, retrained, and redeployed. Hyperscalers were designed for breadth, not the specific demands of GPU-heavy AI workloads. Their strength lies in offering general-purpose services on a global scale, abstracting complexity behind layers of managed infrastructure. For many organizational workloads, that abstraction remains a strength. For AI workloads, however, it increasingly becomes friction.
Companies are now encountering three interrelated constraints that are shaping AI infrastructure decisions. Cost opacity is rising as GPU pricing becomes increasingly bundled and variable, often inflated by overprovisioning and long reservation commitments that assume steady-state usage. At the same time, supply bottlenecks are constraining access to advanced accelerators, with long lead times, regional shortages, and limited visibility into future availability. Layered onto this are performance trade-offs, where virtualization layers and shared tenancy reduce predictability for latency-sensitive training and inference workloads.
These pressures are no longer marginal. They create a market opening that neoclouds are designed to fill.
What neoclouds change
Neoclouds specialize in GPU-as-a-service (GPUaaS), delivering bare-metal performance, rapid provisioning, and transparent consumption-based economics. Many provide cost savings of up to 60–70% compared with hyperscaler GPU instances, while offering near-instant access to the latest hardware generations. Yet the more significant change is architectural rather than financial.
Neoclouds encourage organizations to make explicit decisions about AI workload placement. Training, fine-tuning, inference, simulation, and agent execution each have distinct performance, cost, and locality requirements. Treating them as interchangeable cloud workloads is increasingly inefficient, and often unnecessarily expensive.
As a result, AI infrastructure strategies are becoming inherently hybrid and multicloud by design, not as a by-product of vendor sprawl, but as a deliberate response to workload reality. The cloud market is fragmenting along functional lines, and neoclouds occupy a clear and growing role within that landscape.
Co-opetition, not disruption
The growth of neoclouds is not a hyperscaler extinction event. In fact, hyperscalers are among their largest customers and partners, using neoclouds as elastic extensions of capacity when demand spikes or accelerator supply tightens.
This creates a new form of co-opetition. Hyperscalers retain control of platforms, ecosystems, and company relationships, while neoclouds specialize in raw AI performance, speed to hardware, and regional capacity. Each addresses a different constraint in the AI value chain. For companies and organizations buying cloud services, this blurs traditional cloud categories. The question is no longer simply which cloud provider to use, but how AI workloads should be placed across environments to optimize cost, performance, sovereignty, and operational risk.
The greatest risk for CIOs and technology leaders is treating neoclouds as a short-term workaround for GPU shortages. Neoclouds introduce new considerations: integration complexity with existing platforms, dependency on specific accelerator ecosystems, energy intensity, and vendor concentration risk. Used tactically, they can fragment architectures and increase long-term operational exposure. Used strategically, however, they unlock something more valuable: control.
- 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 organizations to deliberately orchestrate AI training and inference across hyperscalers, neoclouds, and on-premises environments based on performance, cost, and compliance requirements.
From cloud strategy to AI placement strategy
Neoclouds are not an alternative cloud. They are a forcing function, compelling organizations to rethink infrastructure assumptions that no longer hold in an AI-driven world.
The new competitive advantage will come from AI placement strategy – deciding when hyperscalers, neoclouds, on-premises, or edge environments are the right choice for each workload.
Over the next five years, IT leaders will be defined not by how much cloud they consume, but by how precisely they place intelligence where it creates the most value.
Mike Dorosh is a senior director analyst at Gartner.
Gartner analysts will further explore how neoclouds and AI workload placement are reshaping cloud and data strategies at the Gartner IT Symposium/Xpo in Barcelona, from 9–12 November 2026.
