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
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.