The outcome

Keep production inference observable, bounded, recoverable, and isolated across environments.

Step by step

A workflow you can repeat.

  1. 01

    Separate projects, teams, and keys by environment and tenant, define roles, model rights, data handling, retention, logging, SLOs, budgets, and incident ownership.

  2. 02

    Store keys in a secret manager, restrict deployment operations, pin model revisions, cap input, output, batch, and replicas, and require reviewed version comments for updates.

  3. 03

    Monitor endpoint phase, replicas, GPU memory, queue, tokens, latency, errors, content-logging state, availability, and spend using non-sensitive metadata.

  4. 04

    Test sleeping and wake-up, failed download, invalid token, memory exhaustion, scale limits, provider outage, bad version, key rotation, rollback, and termination.

  5. 05

    Canary every update, retain a prior version and alternate endpoint, audit access and costs, delete retired resources, and reconcile that termination stopped billing.

Working standard

What good use looks like.

  • Separate deployment and inference credentials.
  • Canary every endpoint version.
  • Verify termination stops billing.

Official references

Check the current product documentation.