Private API
A customer-specific authenticated endpoint avoids sending prompts to a shared model API. Network access, logs and retention can be defined around the workload.
Control the workload without becoming a GPU operator.
Private AI is an operating model: who owns the capacity, who can administer it, where data is processed, and which technical evidence can be reviewed.
Dedicated GPU
Physical GPU capacity is reserved for one tenant rather than scheduled across unrelated customers. Exclusivity is distinct from a logically isolated shared service.
Known operator
Security review requires accountable people and suppliers. A known operator clarifies privileged access, maintenance and incident ownership.
Known location
A named infrastructure location supports data-flow mapping, supplier diligence and jurisdictional review. Location alone does not create compliance.
Managed operations
Private AI does not have to mean buying hardware, recruiting specialists and maintaining an internal cluster. Operations can remain managed while capacity stays dedicated.
Comparison
Infrastructure models compared
A neutral comparison of typical service characteristics. Exact controls depend on each provider and contract.
| Model | GPU exclusivity | Operator / location | Operations | Complexity | Portability | Cost predictability |
|---|---|---|---|---|---|---|
| Shared API | Usually shared | Provider disclosed; hardware chain limited | Provider | Low | API-dependent | Usage-variable |
| GPU marketplace | Varies | Supplier and region vary | Shared responsibility | Medium–high | Generally good | Market-variable |
| Hyperscale cloud | Available by configuration | Named provider and region | Shared responsibility | High | Cloud-dependent | Config-dependent |
| Customer on-premise | Yes | Customer | Customer | Very high | High | CAPEX + operations |
| INFERENC (planned) | Physically dedicated | Known operator; named EU site | Managed by INFERENC | Low for customer | Open-source focused | Planned monthly capacity |
Private deployment
Start a technical conversationPlanning a private AI deployment?
Tell us about the model, workload, data sensitivity and expected traffic. We will assess the required GPU configuration and deployment model.