The outcome

Operate a resilient inference endpoint with least-privilege access, bounded spend, observable failures, and human control over consequences.

Step by step

A workflow you can repeat.

  1. 01

    Map every existing dependency on model names, tokenization, tools, JSON schema, streaming, errors, moderation, retries, logging, and provider-specific behavior.

  2. 02

    Create a named server-side token with allowed IPs where appropriate, separate environments, minimize submitted data, and review partner-model and bulk-retention exceptions.

  3. 03

    Change the base URL in a test environment, pin the chosen model, enforce input and output limits, timeouts, backoff, idempotency, concurrency, and budget alerts.

  4. 04

    Run contract, load, prompt-injection, privacy, outage, rate-limit, malformed-output, model-removal, and fallback tests while recording request metadata without sensitive content.

  5. 05

    Canary to limited traffic, compare production metrics, keep consequential tools behind authorization and validation, document rollback, and rotate tokens and reevaluate models regularly.

Working standard

What good use looks like.

  • Treat compatibility as interface only.
  • Use separate scoped environment tokens.
  • Canary before full traffic.

Official references

Check the current product documentation.