The outcome

Expose a stable AI endpoint without leaking credentials or allowing unbounded requests, output, retries, or spend.

Step by step

A workflow you can repeat.

  1. 01

    Define the task, model requirements, input and output schema, abuse cases, latency target, traffic limits, retention, and budget.

  2. 02

    Create a development Worker with an AI binding or least-privilege REST token, keeping secrets in bindings and separate from code and clients.

  3. 03

    Pin a catalog model and validate request size and type, authentication, generation limits, response schema, errors, and timeouts in the Worker.

  4. 04

    Test quality, safety, streaming, concurrency, throttling, retries, and model changes with fixtures before exposing the endpoint publicly.

  5. 05

    Add AI Gateway observability and appropriate caching, rate and spend limits, redact logs, deploy gradually, and keep a tested fallback.

Working standard

What good use looks like.

  • Keep inference behind a validated Worker.
  • Pin and benchmark catalog models.
  • Set rate and spend limits before launch.

Official references

Check the current product documentation.