Google Associate Cloud Engineer: Cloud Run Deployment Patterns

Cloud Run makes container deployment simple, but reliable delivery still requires a release pattern. Every deployment or configuration change creates an immutable revision. Traffic can move to the new revision immediately, remain on the existing revision, or be split across revisions for a gradual rollout. That revision model gives teams enough control to separate deployment from exposure, which is the foundation for safer production changes.

The architecture question is not whether Cloud Run supports rollouts; it is how the team will use them consistently. A low-risk internal service may accept direct deployment to 100 percent of traffic. A customer-facing API may require a tagged revision, production validation at zero traffic, a small canary, and automated rollback if service-level indicators degrade. The runtime choice described in Cloud Run or GKE should therefore be followed by an equally deliberate operating model.

Treat the revision as the unit of release

Cloud Run revisions are immutable snapshots of a service configuration and container image. That is useful because a team can identify exactly which revision received a request and can shift traffic without modifying the revision itself. Use revision names, image digests, deployment metadata, and source-control references to make the release traceable from production behavior back to the change that created it.

Choose direct rollout only when the blast radius is acceptable

Sending all traffic to the newest revision is operationally simple. It can be appropriate for low-risk services, development environments, or changes with strong preproduction coverage and easy rollback. Simplicity itself is valuable because every additional rollout stage adds automation and observation requirements.

Deploy at zero traffic for production validation

Cloud Run can deploy a revision without immediately sending normal user traffic to it. Revision tags can provide a stable URL for testing a specific revision before it joins the traffic split. This pattern is useful for production smoke tests, dependency checks, and validation of configuration that cannot be reproduced exactly in staging.

Use gradual traffic migration for risky changes

Traffic splitting lets a service send a small percentage of requests to a new revision while the previous revision continues serving the rest. A canary might begin at one or five percent, then increase as latency, error rate, saturation, and business metrics remain healthy. The percentages should be chosen around the number of requests needed to reveal problems, not around a ritual sequence.