The outcome

Serve a private or customized model predictably while controlling GPU utilization and rollout risk.

Step by step

A workflow you can repeat.

  1. 01

    Define the model, license, traffic profile, latency objective, region, scaling range, idle policy, and maximum GPU budget.

  2. 02

    Create a staging deployment with the minimum viable hardware and store dashboard and CLI credentials outside source control.

  3. 03

    Query the deployment using its explicit identifier and verify output parity, context, structured responses, and error behavior.

  4. 04

    Load-test concurrency, autoscaling, cold starts, capacity failure, and recovery, then compare measured cost with serverless inference.

  5. 05

    Shift traffic gradually, monitor replicas, latency, errors, tokens, and spend, and retain a tested rollback and deletion procedure.

Working standard

What good use looks like.

  • Start with staging capacity.
  • Set an explicit scale-down policy.
  • Monitor GPU cost continuously.

Official references

Check the current product documentation.