A neocloud is a cloud provider built primarily around GPU computing and AI workloads. AWS, Microsoft Azure, and Google Cloud also rent GPUs, but they combine them with broad catalogs of compute, storage, databases, identity, security, and other managed services. In practice, neoclouds tend to emphasize access to accelerators and the high-speed connections between them; the trade-off can be greater responsibility for operating the infrastructure around a workload.
“Neocloud” is an informal industry label, not a formal cloud standard or an official list of approved providers. Treat it as a description of a provider’s focus, then assess the specific service, capacity, network, support, and operational requirements you need.
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What is a neocloud?
A neocloud is a cloud provider focused on GPU compute and AI workloads rather than a wide range of general-purpose cloud services. The OECD describes smaller providers focused on AI compute, while RunPod notes that there is no formal definition or register for the category. The term therefore describes a market tendency, not a precise technical or regulatory classification. OECD; RunPod
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesThese providers generally center their offerings on accelerator capacity, clusters, and networking suitable for AI training or inference. Some offer bare-metal servers or lightly virtualized access, giving customers a more direct view of the hardware and cluster topology. Exact configurations and operating models vary by provider and service.
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How does a neocloud differ from a hyperscaler?
The main distinction is emphasis: neoclouds specialize in GPU infrastructure, while hyperscalers pair GPU instances with a much broader cloud platform. Both can provide GPU compute. The label alone does not establish a particular price, performance level, availability, or feature set; compare the actual service against the workload.
| Decision area | Neocloud tendency | Hyperscaler tendency | Why it matters |
|---|---|---|---|
| Primary focus | GPU compute and AI workloads | General-purpose cloud services as well as GPU instances | A GPU-focused service may fit a job centered on AI compute; a broader platform may fit an application that also relies on many other cloud services. Microsoft Azure; OECD |
| Service catalog | Typically more specialized and narrower | Managed compute, storage, databases, identity, security, and regions | Any savings in GPU rental can be offset by the engineering effort or separate services needed around the workload. Microsoft Azure |
| Hardware access | Often bare metal or lightly virtualized | Often more abstracted, with specialized GPU configurations available | More direct access can make infrastructure details visible, but customers may have to manage more of the stack. Distributed jobs can depend on the cluster’s actual configuration. Microsoft Azure |
| GPU networking | High-speed connections between GPUs are a central consideration | GPU networking is available for particular instance types | For multi-GPU or multi-node training, inspect the specific network and topology rather than inferring them from the provider category. Microsoft Azure; RunPod |
| Capacity and enterprise platform | Can be another source of GPU capacity | Offers a broad platform and established enterprise integrations | Check current capacity and provisioning directly. For either type, account for support, compliance, data location, reliability, and the services your organization requires. Microsoft Azure |
What does a neocloud mean for cost and operations?
The hourly GPU rate is only one part of the cost. A bare-metal or lightly abstracted service can leave more work to the customer, including cluster scheduling, handling failures, moving data, keeping drivers consistent, monitoring, and securing the environment. The effort required depends on the provider’s service and the team’s existing tools and skills.
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Before comparing quotes, include the operational work and any supporting services needed to run the job. A lower GPU-hour price or faster access may be a tendency, not a guarantee for a particular provider, region, or date; actual total cost depends on the workload and available capacity. Microsoft Azure
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Which companies are neoclouds?
There is no official, exhaustive roster. The OECD’s 2025 report gives CoreWeave, Crusoe, Nebius, and Lambda Labs as examples of smaller providers focused on AI compute. 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 the marketplace. These are examples tied to their respective sources and dates, not a definitive current directory. OECD; NVIDIA
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do market estimates say about neocloud growth?
Published estimates point to rapid growth, but they are forecasts and attributed estimates—not settled outcomes. The figures below come from different reports and should not be combined into a single projection.
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| Source and publication context | Reported estimate or forecast | How to read it |
|---|---|---|
| Gartner, 2026 | Neocloud providers are forecast to account for 20% of a $267 billion AI cloud market by 2030. | A forecasted share of the stated market, not a realized market share. Gartner |
| Synergy Research Group figures as reported by Nutanix, 2026 | More than $25 billion in neocloud revenue in 2025 and nearly $400 billion by 2031; Nutanix also reports $9 billion in Q4 2025 and 223% year-over-year growth. | Attributed estimates and a forecast as presented by Nutanix. Nutanix |
| Synergy Research Group figures as reported by Knight Frank, 2026 | $23.9 billion in 2025 and $179.1 billion by 2030. | A separate attributed estimate and forecast. It differs from the Synergy-attributed figures reported by Nutanix, so the two sets should not be merged. Knight Frank |
| Knight Frank, 2026, attributing investment to S&P | Close to 200 operators globally and around $10 billion invested in the prior year. | The operator count is an estimate that depends on how the category is defined; the investment figure is attributed to S&P. Knight Frank |
How should you decide whether to use a neocloud?
Start with the job and the operational model, not the category label. Use these questions to compare a specific neocloud service with a GPU offering from AWS, Azure, or Google Cloud:
Quick Recap
- What are you running? Distinguish training from inference and identify whether the workload is mainly GPU compute or part of an application that depends on other managed services.
- How large is the job? Establish the number of GPUs required and whether the workload needs multiple GPUs to communicate across a high-speed fabric.
- What cluster topology and network does the service provide? Confirm the actual configuration for the instance or cluster under consideration; the term “neocloud” does not guarantee a specific interconnect.
- What will your team operate? Identify who handles scheduling, failures, drivers, monitoring, security, and data movement, then include that effort in the comparison.
- Where must the data reside? Check data-location requirements, applicable compliance needs, support arrangements, and the service’s current capacity and provisioning directly.
- What does the complete platform cost? Compare GPU charges alongside supporting cloud services and the staff effort needed to build and run the environment.
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