What On-premise AI means in practice
On-premise AI is the broader category that sovereign AI sits inside. A workload is on-prem when every component — GPU inference, embedding generation, vector storage, orchestration, logging — runs on hardware physically located in customer facilities (or in a colocation facility the customer leases). On-prem is necessary but not sufficient for sovereign: a deployment can be on-prem and still phone home to a telemetry endpoint, fetch model weights from a vendor registry, or call out to a third-party API for one feature. Sovereign deployments close those loopholes by blocking all outbound network egress at the cluster namespace level.
Related terms
Sovereign AI →
An architecture where customer data never leaves the network perimeter, model weights run on customer-controlled hardware, inference logs stay in the customer's SIEM, and the entire stack can operate air-gapped.
Air-gapped →
A deployment with zero network connection to the public internet — components cannot reach external services, and external services cannot reach them.
More in this category
The Sovereign AI Playbook.
Enterprise generative AI that runs on your servers.
- 01Definition. The four non-negotiable properties of a sovereign deployment — and the three patterns that look sovereign but aren't.
- 02Why now. Three forces that converged in 2026: regulator pressure, the open-weights capability gap closing, and unit economics flipping.
- 03Reference architecture. The five-layer stack we deploy: applications → agent runtime → sovereign LLM → RAG + KB → data stores. With model and tooling choices for each.
- 04The regulators. SAMA · RBI · UK ICO · EU AI Act · DPDP · HIPAA — what each requires and how it maps to architecture.
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.