On-Premise AI
AI infrastructure deployed and run entirely within an organization's own environment, rather than a shared cloud service.
On-premise AI means the model and the infrastructure it runs on are deployed inside an organization's own environment, its own servers or a private cloud tenancy, rather than called as a shared, multi-tenant cloud service. The organization controls where the data physically lives, who can reach the deployment, and whether anything leaves its own network boundary at all.
For life sciences specifically, that control matters because the data at stake usually isn't ordinary business data. It can be patient-level information, unpublished clinical evidence, or a brand's unreleased claim strategy, and a given customer's data-residency or security requirements can rule out a shared cloud deployment entirely, independent of how good a model's output is. That's a separate question from whether a model is accurate; it's about where the computation and the data are allowed to sit in the first place.