On-Premises AI

A hospital that wants an assistant to summarize patient notes faces a basic question: where does the text go? With a hosted model API, each request leaves the building for the provider’s servers. With on-premises AI, the hospital runs an open-weights or licensed model on its own GPUs, and the notes never cross its network boundary. Banks, governments, defense contractors and other regulated organizations make the same calculation.

Control is the benefit. The organization decides where data is stored, who can reach the model, which version runs and when it changes, which helps with data sovereignty and audit rules. The costs are real too: buying and powering hardware, hiring people to run it, and usually waiting longer for the newest models, since the most capable frontier models are often available only through their makers’ clouds. Observability, evaluation and security tooling all have to run inside the same walls.

“On-prem” is one end of a range rather than a single setup. A private cloud may sit in the company’s building or be hosted for it elsewhere, a model can run in a cloud account the company controls, and edge AI puts small models on devices. Ask what exactly stays inside, the model, the data, the logs, or all three. On-premise AI for regulated enterprises walks through the trade-offs.

Guests describe customers asking for both options. In episode 17 at 8:10, IBM’s Maryam Ashoori says watsonx can be deployed “either on premises or the cloud of your choice,” an IBM product claim. In episode 29 at 7:04, CrewAI’s João Moura reports some companies saying “I want on prem. I want a physical server,” while adding he is not sure which way the market will go. Both are Season 2 episodes sponsored by Galileo, with Galileo co-founders as co-hosts (episode 17 intro, episode 29 intro).