A neocloud is a cloud provider, usually founded or repositioned in recent years, whose business is focused mainly on renting GPU computing for artificial intelligence and other compute-heavy work. The term distinguishes these specialists from the large general-purpose public clouds, the hyperscalers, which offer GPUs as one product among hundreds. “Neocloud” is an industry label rather than a defined standard, so providers described this way differ in size, services and business model.
At a glance
- Neoclouds center their offer on GPU as a Service: renting GPU servers and clusters for AI training, fine-tuning and inference.
- Many sell bare-metal GPU servers and large clusters with high-speed networking; some also offer virtual machines, managed Kubernetes or model-serving services.
- Buyers often consider them for GPU availability, pricing or access to newer GPU models, depending on the provider and the market at the time.
- They usually offer far fewer surrounding services than hyperscalers, and coverage of regions, compliance certifications and support varies widely.
- Large deals often involve reserved capacity for months or years, so provider stability and contract terms matter.
What problem it solves
Demand for GPUs grew rapidly with generative AI and large language models. At times, the GPU models AI teams wanted have been hard to get from large clouds on short notice, or available mainly through long commitments. Training large models also needs many GPU servers linked by very fast networking, a setup that general-purpose cloud regions were not originally built around.
Neoclouds emerged to fill that gap. By concentrating capital and engineering on GPU infrastructure, they aim to offer GPU capacity, cluster designs and pricing that suit AI teams, often with simpler product menus and contracts built around reserved capacity.
How it works
Infrastructure. A neocloud deploys GPU servers in its own data centers or, frequently, in leased or colocated space. Because GPU servers draw far more power than typical servers, these deployments often depend on high-density power and, increasingly, liquid cooling.
What is sold. Offers commonly include on-demand GPU instances, reserved capacity for a fixed term, dedicated bare-metal servers and full clusters. Some providers add storage, orchestration tools, managed inference endpoints or help optimizing AI workloads. Exact offers change quickly.
Pricing. Pricing is usually per GPU per hour, with lower rates for longer commitments. Storage, networking and data transfer may be charged separately or bundled, depending on the provider.
Customers. Neocloud customers range from AI model developers and research teams to enterprises running AI projects and, in some cases, other cloud providers buying extra capacity.
When it matters for buyers
- When a GPU project is blocked on capacity or cost. A neocloud may offer the hardware sooner or at a different price; compare the total cost including storage, networking and data movement.
- When you need a dedicated training cluster. Cluster networking and reservation terms are central to what neoclouds sell.
- When data location or ownership matters. Ask where the data centers are, who operates them and which laws apply.
- When deciding how much to commit. AI demand and GPU prices can change fast; balance price breaks for long terms against flexibility. Our bare metal solutions page covers sourcing dedicated GPU and server capacity.
Questions to ask vendors
- Which GPU models are available now, in which locations, and how much capacity can you commit to us by date?
- Is our capacity reserved and dedicated, or shared and subject to availability?
- What networking connects GPU servers within a cluster, and what storage is available alongside it?
- What do storage, networking and data transfer cost, beyond the GPU hourly rate?
- Where are your data centers, who owns and operates them, and which security and compliance reports cover them?
- What are the minimum term, payment terms and exit terms, and what happens to our data and capacity if your company is acquired or stops operating?
- What uptime and hardware replacement commitments come with the service?
How it differs from a hyperscaler
A hyperscaler is a very large public cloud provider offering a broad catalog of services, including compute, storage, databases, analytics, security and AI, across many regions, with GPU capacity as one part of that catalog. A neocloud focuses mainly on GPU computing, typically with a much narrower set of surrounding services and fewer regions, and often aims to compete on GPU availability, cluster design or price. Many organizations use both: a hyperscaler for their general cloud estate and a neocloud for specific AI training or inference capacity, which means planning for data transfer and security between the two.