On-premise AI

On-premise AI runs models and supporting infrastructure inside an organization's own environment rather than a vendor's cloud. It is chosen where data sovereignty, regulation, or air-gapped operation prevents sending data to a third party, and it trades operational convenience for control.

The tradeoff is real in both directions. Running your own inference means capacity planning, upgrades, and hardware that a hosted service handles for you, and it can lag on model availability.

What it buys is the ability to satisfy requirements that hosted deployment cannot meet at any price, which is why it tends to be decided by compliance rather than by engineering preference.