Last updated · Fusionative
Short answer
A neocloud is a cloud provider that specializes in GPU compute for AI training and inference, rather than offering the hundreds of general services a hyperscaler sells. Neoclouds rent dense GPU clusters by the hour, by reservation or per token, and compete on GPU availability, price and performance.
How a neocloud differs from a hyperscaler
Hyperscalers such as AWS, Microsoft Azure and Google Cloud run broad catalogs of compute, storage, database and application services. A neocloud narrows the offer to accelerated infrastructure: bare-metal or virtualized GPU servers, high-bandwidth networking, fast storage, and increasingly managed inference endpoints.
Because the catalog is narrow, a neocloud can bring new GPU generations to market quickly and price them aggressively. The trade-off is that it must build or buy the operating software that hyperscalers developed over many years: tenancy, self-service, quotas, metering and billing.
Who the neoclouds are
Commonly cited examples include CoreWeave, Lambda, Crusoe, Nebius and Together AI, along with regional and national GPU clouds in Europe, the Middle East, India and Southeast Asia. Telecom operators and data center companies are also launching GPU clouds of their own, often as part of national sovereign AI programs.
The neocloud software stack
- Infrastructure: bare-metal provisioning, GPU drivers and firmware, high-speed fabrics (InfiniBand or RoCE Ethernet), storage.
- Platform: Kubernetes for containers, a VM layer for customers who want virtual machines, and often a batch scheduler such as Slurm for training jobs.
- AI services: model serving runtimes (vLLM, SGLang, NVIDIA NIM), notebooks, fine-tuning and model catalogs.
- Cloud operations: multi-tenancy and isolation, self-service portal and APIs, quotas, usage metering, pricing and billing, observability and security.
Where FusioNative fits
FusioNative's NEO Cloud platform is the cloud-operations layer of that stack: tenancy, a self-service catalog, quotas, metering and chargeback over GPU, Kubernetes and VM capacity the provider owns. It runs self-hosted, so the provider keeps control of its own platform.
| Neocloud | Hyperscaler | |
|---|---|---|
| Core offer | GPU compute for AI training and inference | Hundreds of general cloud services |
| Pricing | GPU-hour, reserved capacity, per-token inference | Per-service metering across a broad catalog |
| Hardware cadence | Fast access to new GPU generations | Broad but rationed GPU availability |
| Software | Builds or buys tenancy, portal, metering and billing | Mature, proprietary control planes |
| Typical customers | AI labs, startups, enterprises, public sector | Every kind of workload |