What does private AI infrastructure mean?
Private AI infrastructure means language models and AI applications run on servers that belong to your own company, or on a dedicated private zone reserved exclusively for you, rather than through a shared cloud interface from a third party. Data and model access stay entirely within your own control. The term the industry uses for this model is private AI infrastructure.
Definition
Private AI infrastructure describes running AI models on hardware assigned exclusively to one company - whether that means physically inside your own server room, or a dedicated, reserved environment at a provider. Unlike shared infrastructure, data and requests never leave that controlled zone. In practice, the term that has stuck for this model is private AI infrastructure; see the solutions page for the full picture.
Distinguishing from neighbouring terms
The term is often confused with similar but distinct models. The table below places the main variants side by side.
| Model | Where the data runs | Control over model and infrastructure |
|---|---|---|
| Private AI infrastructure | Own or dedicated, reserved hardware | Fully with the company |
| Cloud AI (shared infrastructure) | A provider's shared data centres | With the provider, under its terms of use |
| Hosted API (billed per request) | The API provider's infrastructure | With the provider, accessible only via interface |
Cloud AI in the narrow sense means models run on a provider's shared infrastructure, accessed by multiple customers at once. A hosted API goes a step further: it's billed purely per request, with no visibility into the underlying infrastructure at all. Private AI infrastructure differs from both in that hardware and data handling stay assigned exclusively to one company.
In practice
In a typical NexPatch AI project, a company starts with a pilot on dedicated, private infrastructure, to keep sensitive customer data under its own control from day one. Once the pilot succeeds, the same infrastructure carries over into productive operation, with no need for data to move to an external, shared service. This approach is especially common in regulated industries, where data residency and traceability have to be contractually guaranteed. How ongoing operation of that kind of infrastructure gets organised afterward is covered on the operations page.
If you want to understand how a setup like this is actually built and which building blocks it comprises, the full description lives on the private AI infrastructure page. If you'd rather first check whether self-hosting is worth it for your own case against billing per request, see the self-hosting vs. API comparison.