Deploying Guardrails Self-Hosted: Take Control of Your AI and Software Workflows

Guardrails gives you a framework for enforcing policies directly in your AI and software workflows. Self-hosted deployment ensures the entire system runs inside your infrastructure, with no data leaving your network. This is critical for organizations with strict compliance, internal security requirements, or custom control needs.

The process is straightforward but demands precision. Start with a secure environment—Kubernetes or Docker on machines you fully own or trust. Obtain the latest Guardrails package from the official repository. Configure environment variables for your model endpoints, authentication, logging, and storage. Ensure your secrets are stored in a secure vault and loaded at runtime.

Deploy the Guardrails API as a persistent service inside the cluster. Mount configuration files with validation rules and response schemas. Run initial tests against controlled prompts to verify models are producing outputs that meet policy requirements. Use monitoring tools to track latency, performance, and potential rule violations in real time.

Scaling is simple: adjust replicas for the API service, place load balancers in front, and configure autoscaling policies. Integrate Guardrails with CI/CD pipelines so new rules deploy automatically alongside application updates. Version control everything—your policy definitions, schema files, and test cases—so changes can be audited and rolled back without breaking production.

Self-hosting lets you decide exactly how Guardrails operates. You set the limits. You control the data. You lock down the system. Deployment is not an abstract ideal—it is a concrete, inspectable process that can be tuned down to every variable.

See Guardrails self-hosted live in minutes with hoop.dev and start running it inside your own stack today.