Phi Self-Hosted

The server hummed low in the rack. You pushed the deployment. Phi Self-Hosted booted without asking for your trust—it earned it.

Phi Self-Hosted is the autonomous deployment of the Phi AI stack on your own infrastructure. No public endpoints. No shared tenancy. Every model, every inference, every dataset runs inside a container you control. It’s built for speed, reliability, and total ownership of your machine learning workflows.

Installation is simple. Clone the repository. Set your environment variables for GPU and storage. Launch the stack with Docker or Kubernetes. Within minutes, the core Phi engine is serving models over a local API. Latency drops. Privacy is absolute.

Phi Self-Hosted supports fine-tuning models without touching external services. You can import pre-trained weights, run batch inference jobs, or chain models inside the same cluster. REST and gRPC endpoints are available out of the box. Logging and metrics stream directly to your choice of Prometheus, Grafana, or ELK stack. Versioning is built in, making rollback and upgrade paths clean and predictable.

Integration is minimal friction. Connect Phi Self-Hosted to existing CI/CD pipelines. Use secret management tools to bind it to credential stores. Deploy edge nodes for distributed inference. The architecture is modular, so swapping components or adding accelerators takes minutes instead of days.

Security is a core function, not an afterthought. Every process is namespaced. Networking is locked down by default. You set the access rules—every port, every request—without relying on third-party controls.

Phi Self-Hosted is for teams that need AI power without surrendering their data. It keeps computation local, eliminates vendor lock-in, and scales horizontally as hardware allows. Between custom models, reproducible builds, and offline operation, it is the shortest path to owning your AI pipeline end-to-end.

See Phi Self-Hosted running in minutes. Go to hoop.dev and bring it online now.