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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →A neocloud is a cloud provider built primarily around GPU compute and AI workloads. Unlike AWS, Microsoft Azure, and Google Cloud, which offer broad cloud platforms alongside GPU instances, neoclouds tend to focus on accelerator capacity, high-speed GPU networking, and more direct access to the underlying infrastructure. The term is an industry label, not a formal cloud category, so providers grouped under it do not all offer the same services.
What is a neocloud?
A neocloud is a provider whose main business is supplying compute for AI and other GPU-intensive workloads. It may offer access to individual GPU machines or large clusters designed for training and serving AI models. Some providers expose hardware through bare-metal or lightly virtualized environments, giving customers more direct control over infrastructure.
There is no formal registry or universally accepted definition. The OECD describes smaller providers focused on AI compute, while RunPod notes that the term has no formal definition or register. As a result, “neocloud” is best treated as a useful shorthand for a GPU-first service model, not a certification or guarantee of specific hardware, performance, or service levels.
How does a neocloud differ from AWS, Azure, and Google Cloud?
The central difference is specialization. Neoclouds tend to concentrate on GPU capacity and the networking needed to connect accelerators in large clusters. Hyperscalers sell GPUs too, but place them within a much wider platform that can include managed compute, storage, databases, identity, security, and global regions.
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| Decision axis | Neocloud tendency | Hyperscaler tendency | Why it matters |
|---|---|---|---|
| Primary offering | GPU compute and AI workloads | Broad general-purpose cloud plus GPU instances | Choose based on whether the work is mainly AI compute or part of a larger application stack. |
| Service breadth | Narrower, specialized catalog | Managed compute, storage, databases, identity, security, and regions | A lower GPU rate can be offset by engineering work or by needing separate services. |
| Hardware access | Often bare metal or lightly virtualized; cluster topology may be more visible | More abstraction, with specialized GPU configurations available | Distributed training can depend on accelerator interconnect and topology. |
| Networking | High-speed fabric between GPUs is central to large clusters | GPU networking is available on particular instance types | Check the topology and networking for the workload; the provider label alone is not enough. |
| Access and capacity | May provide another source of GPU availability | Broad platform and established enterprise integrations | Confirm current capacity and provisioning directly because availability changes. |
| Operations and enterprise needs | More direct infrastructure responsibility may fall on the customer | More managed services, global reach, and compliance infrastructure | Include reliability, support, compliance, data location, and staff effort in total cost. |
These are tendencies, not rules. A neocloud can offer managed services, and hyperscalers can provide highly specialized GPU configurations. Compare the actual instance, network, operational model, and service commitments for the workload rather than relying on the category name. Microsoft’s overview of neoclouds also frames lower GPU-hour pricing and faster access as common tendencies, not guaranteed outcomes.
Why GPU networking and infrastructure access matter
AI training workloads often divide computation across multiple GPUs. In those cases, the speed and structure of communication between GPUs can matter as much as the accelerator model. A large cluster with a suitable high-speed fabric may be more useful than a nominally similar number of GPUs connected in a less suitable configuration.
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More direct infrastructure access can help teams tune software or understand cluster topology. It can also mean the customer takes on more of the work: scheduling jobs, handling failures, keeping drivers consistent, monitoring systems, moving data, and securing the environment. The GPU hourly rate alone does not capture those costs.
Which companies are neoclouds?
The category boundary is flexible, so published lists are examples rather than authoritative directories. The OECD’s 2025 report names CoreWeave, Crusoe, Nebius, and Lambda Labs as examples of smaller providers focused on AI compute.
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In a May 2025 announcement for DGX Cloud Lepton, NVIDIA listed CoreWeave, Crusoe, Firmus, Foxconn, GMI Cloud, Lambda, Nebius, Nscale, SoftBank Corp., and Yotta Data Services as NVIDIA Cloud Partners offering GPUs through its marketplace. This is a dated marketplace roster, not a complete list of neoclouds or a statement that all listed companies have identical business models. NVIDIA founder and CEO Jensen Huang said the marketplace “connects our network of global GPU cloud providers with AI developers.” (NVIDIA announcement, May 18, 2025.)
How to decide whether a neocloud fits your workload
Start with the needs of the job and the team operating it. A GPU-focused provider may fit work that depends heavily on accelerator access and cluster networking. A hyperscaler may be a better fit when the workload relies on its broader managed platform, enterprise integrations, or global infrastructure.
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- Define the workload. Identify whether you are training a model, fine-tuning it, or serving inference, and estimate how much GPU capacity the job needs.
- Specify the cluster requirements. Ask what GPU count, interconnect, and topology the workload needs. Verify the actual configuration and network rather than inferring it from a provider’s category.
- List the services your team must operate. Account for scheduling, storage, monitoring, security, driver management, and recovery from failures. Compare that effort with the managed services available on the platform.
- Check data and enterprise constraints. Determine where data must reside and what reliability, support, compliance, and integration requirements apply.
- Validate current capacity and terms. GPU availability, regional access, pricing, and provisioning can change. Confirm them with the provider for the specific configuration and timeframe you need.
- Compare total cost, not only GPU-hour rates. Include data movement, associated services, engineering effort, and the operational responsibility your team will assume.
What market forecasts say—and what they do not
Published estimates indicate strong expected growth, but they are forecasts, not measured outcomes, and different summaries report substantially different totals. Keep each figure attached to its publisher and forecast period rather than combining them into one market estimate.
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- Gartner, 2026: forecasts neocloud providers will account for 20% of a $267 billion AI cloud market by 2030. This is a projected share, not a realized one. (Gartner release.)
- Synergy Research Group figures as reported by Nutanix in 2026: more than $25 billion in neocloud revenue in 2025 and nearly $400 billion by 2031; Nutanix also reports $9 billion in the fourth quarter of 2025 and 223% year-over-year growth. These are attributed estimates and forecasts. (Nutanix report.)
- Synergy Research Group figures as reported by Knight Frank in 2026: $23.9 billion in 2025 and $179.1 billion by 2030. These figures differ from Nutanix’s Synergy-attributed figures and should not be merged with them. (Knight Frank report.)
- Knight Frank, 2026, attributing investment to S&P: estimates close to 200 operators globally and around $10 billion invested in the prior year. The operator count depends on how the category is defined. (Knight Frank report.)
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