What private AI really covers
A private AI is a solution where the organization controls the execution environment, the accessible data and the rules of use. It can be hosted on site, in a private cloud or with a partner, as long as the technical and contractual boundaries remain clearly defined.
This notion concerns the entire chain: document storage, search indexes, models, logs, authentication and interfaces. Just hosting the model internally is not enough if the documents or requests then pass through an uncontrolled service.
When a public service remains relevant
A public AI service can accelerate experiments on non-sensitive content. It reduces the initial infrastructure effort and quickly provides access to new models.
However, it remains necessary to verify the conditions of use, the location of processing, data conservation and integration possibilities. A practical prototype does not automatically constitute an acceptable architecture for production.
- Ideation on non-confidential information
- Rapid testing without deep integration
- One-off uses with a clear data policy
The criteria that should guide the decision
The sensitivity of the information is the first filter, but it is not the only one. Frequency of use, volumes, latency, availability and ability to change models also influence the choice.
A serious comparison must focus on the total cost: licenses, consumption, infrastructure, integration, supervision and skills. The cheapest option at start-up can become expensive when it becomes widespread.
- Nature and classification of data
- Access rights and expected traceability
- Applications and sources to connect
- Medium-term cost predictability
- Internal capacity to operate the platform
A hybrid approach is often possible
Organizations are not required to apply a single architecture for all uses. Public information can use an external service while sensitive knowledge is processed in a private platform.
The condition is to define simple and technically applicable rules: what contents can output, what templates are allowed and how users identify the right environment.
Start with the uses, not the hardware
Before selecting a server or provider, describe the expected tasks, users, necessary data and service level. These elements then make it possible to compare the architectures on a concrete basis.
A first well-defined use case makes it possible to measure quality, cost and adoption before extending the platform.