Guide
Private AI: cloud or on-premise?
The question often comes in that form, but it hides several. Separating them lets you choose on real criteria rather than on a feeling of control.
Four distinct questions
Where does the application data live? That is often already settled: your software is hosted somewhere, and the agent does not change that choice.
Where does inference run? That is a separate decision. An agent deployed on your side can call a remote model, and a managed-cloud agent can use dedicated capacity.
Who operates it? Deploying on your side moves operational load: supervision, updates, incidents. That is not resource-neutral.
What leaves the boundary? A web search or external monitoring leaves by construction. What is allowed is defined independently of where things are hosted.
The criteria that actually decide
The nature of the data
Review data sensitivity and permitted processing. Storing data in a SaaS does not authorise sending it to a new model provider.
Your operational capacity
On-premise deployment assumes a team and processes. Without them, the gain in control is paid for in degraded availability.
Volume and response times
Dedicated capacity is sized for a peak. A shared service absorbs variation. Depending on your load profile, one or the other is more economical.
The functions you need
Some capabilities — external monitoring, search — assume outbound access. A disconnected environment restricts the functional scope, not just the network.
A path rather than a final choice
Many organisations choose badly because they decide too early, before knowing what they will actually use.
- 01
Validate the use
If your requirements allow it, validate a task on managed cloud. Otherwise, choose a suitable environment from the pilot stage, potentially using fictional data.
- 02
Measure the real profile
Volume, request types, expected response times: once known, these figures make sizing possible.
- 03
Tighten if needed
Move to dedicated inference or on-premise installation, knowing what you are deploying. The move requires fresh qualification.
We put forward no price and no reference configuration in this guide: they depend too much on context to be useful outside an assessment.
Frequently asked questions
Is on-premise inherently safer?
Not automatically. A poorly operated deployment on your own premises can be less safe than a properly run service. Security comes from practices, not only from location.
Is a self-hosted model sufficient?
For some tasks, yes. For others, the capability gap remains noticeable. That is tested on your real cases: a generic leaderboard says nothing about your usage.
Can we mix both?
Often. Sensitive processing within a dedicated boundary and the rest on an operated service is a common setup, provided the boundary is explicit.