Glossary
On-premise AI
On-premise AI refers to running AI systems on a company's own infrastructure rather than in a vendor's cloud. Data, models, and compute stay under the organization's direct control. For small and medium-sized businesses, this matters most when highly sensitive data must not leave the premises or internal policies restrict cloud use.
Last updated: 2026-08-03
In an on-premise setup, all AI processing – language models, search, data storage – runs on servers the company owns or controls: in its own data center or a private environment. The counterpart is software as a service (SaaS), where a provider operates the system. On-premise means maximum data sovereignty: content never leaves the company's own infrastructure, and the organization alone decides on updates, access, and the models in use. In return, it also carries the responsibility for hardware, maintenance, and security itself.
In day-to-day operations, on-premise AI is above all a trade-off between control and effort. Running it yourself requires suitable hardware, staff for operations and security, and a plan for model updates. Many SMEs therefore choose a middle ground: cloud operation with clear contractual and technical commitments, such as AI processing that defaults to the EU and a data processing agreement under Art. 28 GDPR. On-premise remains the right choice when internal policies or industry rules require that data never leave the premises.
Chifty runs as SaaS today: hosted in Germany, with AI processing defaulting to the EU. For companies with their own infrastructure, Chifty is additionally preparing an on-premise option – currently a pilot project available on request. It runs self-hosted in your own environment, with a signed offline license and no license server or phone-home; your own AI models are possible. Regardless of the deployment model, the same principle applies: the AI must not bypass permissions – answers draw only on sources approved for the asking person, and show their citations.