What is private AI?
Private AI is an arrangement in which access to AI systems and the data they use is controlled for an organisation. Where the application, files and model run—and what leaves that boundary—determines what “private” means in practice.
By Exemplary AI · Updated · Illustrated explainer
The key idea
A private application can still send data to an outside model. Follow the complete request.
The idea, illustrated.
Draw the boundary around the whole request
Figure 10Source files and prepared knowledge.
Assembles the request and checks access.
Processes the selected context here.
Selected prompt and context cross the boundary if an external model is enabled.
Its own service, location and terms.
- 01
Locate each component
Identify where files, extracted text, the application, search and the model are hosted.
- 02
Follow outbound routes
Check model calls, connected tools, logs, support access, backups and updates—not just the original upload.
- 03
Name the operator
Someone must maintain the installation, manage access and handle changes after the initial setup.
On-premises, private cloud and managed cloud
On-premises means the installation runs on equipment in an organisation’s environment. Private cloud commonly means an isolated environment in a cloud account. Managed cloud describes who operates the service; it does not, by itself, specify the data region, backup policy or model provider.
Cohere’s deployment documentation illustrates that enterprise models can be delivered through several hosting arrangements. The distinction for a buyer is control and responsibility across the whole service, rather than whether the word “cloud” appears in the description.
Primary source: Cohere: Deployment options — overview
What changes when there is no internet connection?
An air-gapped installation cannot rely on a live external service to answer requests. The required software and model files have to be brought into the environment through an approved process. NVIDIA’s air-gap deployment documentation demonstrates this preparation for its model-serving software.
Offline operation also changes updates, troubleshooting and integrations. A tool that depends on a public internet service cannot work unchanged inside an isolated environment. The team must plan an internal alternative or remove that dependency. A model being local is only one part of that plan.
Primary source: NVIDIA: Air-gap deployment
An everyday example
A contract question inside an organisation’s environment
An employee uploads a contract to an on-premises installation. Search selects relevant text and a locally running language model prepares the response. The file, selected context and model computation remain inside the described arrangement.
If the team instead enables an external model, the selected prompt and context travel to that provider. The original file staying on-premises does not make that request fully local.
A common misconception
“On-premises means no information can leave.”
External models, tools and operating processes can create outbound paths. Those paths need to be identified and controlled for the actual installation.
How Exemplary helps
We build the setup around your boundary.
Exemplary installs and commissions on-premises or private-cloud deployments, with handover for your operators. Managed cloud is available under an agreed operating scope. We make the model route and responsibilities part of the setup.
Follow the complete data journeySources & further reading
Primary publications and documentation. Accessed 22 September 2026.
- 01Deployment options — overview
Cohere · Product documentation
- 02Air-gap deployment
NVIDIA · Product documentation