Factory Private
Enterprise AI
Self-hosted AI agents and control-plane ownership
September 18, 2026 - 2 minute read
Self-hosted AI agents can leave an enterprise with more operating responsibility than its initial evaluation considered. The important decision is which services the organization must own, which hosted services it permits, and who will maintain the resulting system.
A local process alone does not answer those questions. Session handling, inference, analytics, and connected tools may have different operators. Compare their actual boundaries before choosing a deployment.
Self-hosted AI agents change who operates the system
Factory offers a managed control plane and a customer-owned alternative. Factory Managed provides hosted orchestration and operations while Droids can run across existing development environments.
Factory Private puts the control plane in customer infrastructure. The published comparison identifies differences in ownership, data boundary, and access controls.
Cropped excerpt of the linked deployment comparison, captured September 18, 2026. Execution options and authorization scope require offering-specific confirmation.
Choose against a requirement rather than a label. A workload that permits a hosted control plane may fit Managed. A requirement for a customer-owned control plane points toward Private, with the accompanying operating work.
Review self-hosted AI agents beyond the model key
Bringing an approved model does not move every supporting service into the customer network. Factory's sovereign software development whitepaper distinguishes Managed control-plane and session-data hosting from Private's customer-hosted placement.
Review session handling separately from inference. Record where context is stored, which model service receives requests, and what the gateway logs. Include retries, fallbacks, and support diagnostics in the diagram.
Factory's deployment documentation describes the runtime and traffic differences between cloud-managed, hybrid, and airgapped patterns. Use the intended pattern to test the complete workflow, not just its first successful model response.
Budget for the responsibilities that move
Operating a private control plane requires a release and recovery process. Identify who monitors it, promotes updates, handles certificate changes, and restores service after a failed change.
The model service needs an owner too. A new model version can alter task behavior even when the agent build stays fixed. Keep representative validation tasks and review the results before promotion.
Estimate the operating work using the proposed architecture. More execution machines will not fix an overloaded inference service or an unavailable internal package mirror. Record those dependencies during the pilot.
Support access also belongs in the plan. Agree on which diagnostics may leave the environment, how sensitive content is removed, and who approves release. Avoid making troubleshooting an undocumented exception to the boundary.
Keep disconnected and federal requirements separate
Airgapped operation adds a no-runtime-connectivity requirement. Factory's airgap build uses customer-configured models and disables Factory-bound services. Cloud-dependent features such as Slack integration and hosted analytics are unavailable.
A GovCloud requirement raises a different evaluation. Factory FedRAMP remains described as authorization in progress as of September 18, 2026. Neither customer hosting nor GovCloud placement should be treated as proof of completed service authorization.
Finish the comparison with a bounded task, approved permissions, and a known test suite. Compare accepted output and review effort alongside operator intervention. The useful choice is the deployment the organization can explain, maintain, and validate.
