The outcome

Serve a custom model reliably without public management exposure or runaway workers and storage.

Step by step

A workflow you can repeat.

  1. 01

    Define model license, container trust, traffic, GPU, memory, latency, concurrency, cold start, availability, storage, secrets, health, observability, cost, and rollback targets.

  2. 02

    Pin and scan the container, restrict registry credentials, use least-privilege model tokens, expose only the inference port, and separate source, model, and output storage.

  3. 03

    Create a test endpoint with a real health path, explicit command, minimum and maximum workers, queue or concurrency scaling, idle timeout, request timeout, and budget alerts.

  4. 04

    Load-test initialization, queueing, scale-to-zero, replicas, GPU memory, malformed input, timeouts, log redaction, model output, regional failure, and per-task cost.

  5. 05

    Canary production traffic, monitor workers and spend, preserve a prior image and alternate path, rotate secrets, and verify deleted endpoints and volumes stop billing.

Working standard

What good use looks like.

  • Pin and scan container images.
  • Bound minimum and maximum workers.
  • Verify resource deletion and billing.

Official references

Check the current product documentation.