The outcome

Run reproducible inference without exposing credentials, datasets, management surfaces, or unbounded cloud spend.

Step by step

A workflow you can repeat.

  1. 01

    Define model and data rights, region, endpoint contract, GPU and memory needs, threat model, ingress, storage, scaling, observability, SLO, budget, and teardown evidence.

  2. 02

    Separate environments and roles, create scoped service credentials, restrict network access, encrypt storage, pin the image and model digest, and scan dependencies before deployment.

  3. 03

    Deploy privately with explicit health checks, timeouts, concurrency, replica bounds, secret mounts, non-sensitive logs, quotas, and no unnecessary management or shell exposure.

  4. 04

    Load-test cold start, queueing, autoscaling, memory, malformed input, model failure, key rotation, regional failure, output parity, privacy, and cost under representative traffic.

  5. 05

    Canary changes, retain a prior deployment and recovery manifest, export approved outputs, delete retired endpoints and disks, revoke credentials, and reconcile billing.

Working standard

What good use looks like.

  • Pin images and model revisions.
  • Restrict ingress and project roles.
  • Verify teardown and stopped billing.

Official references

Check the current product documentation.