On-prem AI development inside your security boundary

On-premises AI development for private repositories requires evaluating every dependency in the workflow, including the execution environment, control plane, model endpoint, package sources, operational-data collection, and permitted network routes. Factory describes cloud-managed, hybrid, and fully air-gapped deployment patterns, with Factory Private offering a customer-controlled control plane in a VPC or on-premises environment while still requiring approved inference services, tools, and connectivity. Teams are advised to first make repository builds reproducible using internally approved artifacts, avoid unreviewed public downloads, and verify where file content travels when used for model inference. Initial agent access should be narrowly scoped through disposable workspaces, limited permissions, command and network restrictions, sandboxing, and continued source-control review, with tests confirming that prohibited actions fail. Successful operation also depends on clear ownership of releases, model availability, certificates, recovery procedures, and measurable results from bounded maintenance tasks, with artifact versions, endpoint settings, policy tests, and reviewed changes recorded before expanding use to additional repositories.

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