Writing a service is one part of shipping software. Engineers also need to know how that service becomes a release, where its configuration comes from, and how to recover when a deployment behaves differently than expected.
A useful delivery architecture makes those answers visible. The connection between code and infrastructure should be easy to follow, from a reviewed change to the image running in production.
Build an artifact you can identify
Start with an application that has an explicit dependency manifest, documented startup command, and clear configuration boundaries. Package it into a Docker image in CI, record the source revision, and publish it to a registry.
Use a multi-stage build to keep compilers and build dependencies out of the runtime image. Choose a maintained base image and schedule dependency updates. These practices keep the runtime smaller and easier to understand; Docker’s build guidance explains the tradeoffs in image construction and version pinning.
Promote the same image digest through environments. If each environment rebuilds the application independently, it becomes harder to know whether a difference comes from configuration or from the artifact itself.
Separate building from deployment
CI should produce a traceable artifact and useful diagnostics. The deployment configuration should describe which artifact belongs in an environment, along with its runtime settings and resource requirements.
A practical flow is:
- Review the application change and run the repository’s required checks.
- Build and publish an image with a recorded source revision and digest.
- Propose an update to the environment’s deployment configuration.
- Review that update and let the deployment controller apply it.
- Observe the rollout and the service’s production behavior.
Argo CD uses Git as the source of desired application state and reconciles Kubernetes resources against it. Its declarative configuration also allows application definitions to be reviewed alongside other manifests. Decide explicitly which environments use automatic synchronization and who can change that policy.
Make the runtime contract explicit
For a stateless service, a Kubernetes Deployment describes the desired workload and manages updates to its Pods. Define resource requests, readiness behavior, shutdown handling, and rollout settings around the service’s actual needs. Kubernetes documents the lifecycle and recovery controls in its Deployment guide.
The same design questions apply on GKE: which identity can access a dependency, where secrets are supplied, how traffic reaches the service, and who owns the response to an alert. A cluster does not remove those application responsibilities.
Design recovery before the first incident
Record how to restore the previous application version, and consider whether a database migration is compatible with it. Reverting an image cannot undo an incompatible data change. Keep recovery steps close to the deployment definition so that someone other than the original author can use them.
Production feedback should connect a deployment to user experience. Latency, traffic, errors, and saturation are a useful starting point for service monitoring, as described in Google’s SRE guidance. Attach release identifiers to diagnostics, give alerts an owner, and keep a short runbook for common failure modes.
The result is a delivery system an engineer can explain: what changed, what is running, how it is behaving, and what to do next.