The outcome

Keep GPU inference observable, cost-bounded, recoverable, and isolated across workspaces and environments.

Step by step

A workflow you can repeat.

  1. 01

    Separate workspaces and credentials by environment, assign least privilege, and define access, data handling, retention, SLOs, replica and resource ceilings, budgets, and incident ownership.

  2. 02

    Create an endpoint from a reviewed Photon ID or pinned container, attach scoped secrets, keep access private, expose one required port, and configure startup allowance and replica bounds.

  3. 03

    Monitor status, events, logs, replicas, GPU utilization, queue, latency, errors, output parity, storage, and spend using redacted metadata and actionable alerts.

  4. 04

    Test bad artifacts, slow startup, memory exhaustion, malformed requests, scale limits, provider failure, secret rotation, access revocation, rollback, stop, and removal.

  5. 05

    Canary an endpoint update instead of rerunning production in place, retain the prior revision, audit access and cost, remove retired resources, and verify billing cessation.

Working standard

What good use looks like.

  • Use immutable Photon or image versions.
  • Update production with a canary.
  • Verify endpoint removal stops billing.

Official references

Check the current product documentation.