Self-hosted models for confidential source code

Self-hosted AI models can help keep confidential source code within approved environments, but organizations must verify the complete inference route, including gateways, downstream services, credentials, logs, retention policies, and fallback behavior. Factory recommends documenting model identifiers, endpoints, providers, operators, and data-handling policies, while using enterprise controls to limit developers to approved model destinations rather than relying only on default selections. Credentials should be managed through secure, organization-controlled mechanisms such as keyless endpoints or runtime helpers, with testing for expiration, revocation, identity failures, and log exposure. Teams should validate models using realistic development workflows involving file access, tool calls, edits, tests, and error correction rather than simple chat responses, and should maintain repeatable evaluations when changing models or gateways. In air-gapped environments, customer-configured internal models are required, while outages should halt work or move it to another approved internal route.

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