The outcome

Ship a tested model integration with pinned behavior, protected keys, regional routing, and measurable usage.

Step by step

A workflow you can repeat.

  1. 01

    Define the task, data classification, latency and quality targets, required modalities, region, budget, and evaluation cases before choosing a model.

  2. 02

    Add limited credits, create a project-specific API key, store it in an environment secret, and select the closest approved AI Hub endpoint.

  3. 03

    Configure the OpenAI-compatible base URL and an explicit supported model ID, then send a minimal non-sensitive request and handle API errors.

  4. 04

    Benchmark candidate models on the same factual, safety, structured-output, latency, and cost cases while recording model IDs and settings.

  5. 05

    Set application limits and fallbacks, monitor usage history for anomalies, rotate keys, and rerun the benchmark before changing models or regions.

Working standard

What good use looks like.

  • Use one key per application.
  • Pin and benchmark model IDs.
  • Monitor credits and usage history.

Official references

Check the current product documentation.