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For each application: runtime requirements, external dependencies, configuration and secrets access patterns, and any platform-specific constraints that affect containerization (file system assumptions, network binding, signal handling).
Dockerfiles written and image builds validated. Base image selected with security and size considerations. CI pipeline for automated image build and push to Artifact Registry.
GKE cluster or Cloud Run environment provisioned. Base platform components deployed: ingress controller (if GKE), cert-manager for TLS, and monitoring agent.
Bring us the constraint. We'll bring the team.
Containers are not a trend — they are the current standard unit of application deployment for good reasons. A container image is immutable: the version deployed to staging is byte-for-byte identical to the version deployed to production. Rollbacks are instant: the previous image tag is already in the registry. Resource usage is defined: CPU and memory limits are specified in the workload manifest, not discovered empirically in production.
The value proposition is consistency and reproducibility — and it compounds when combined with Kubernetes orchestration, which adds automated scheduling, health-based restarts, horizontal scaling, and rolling deployments on top of the container runtime.
We containerize existing applications using Docker: Dockerfile authoring with multi-stage build patterns to minimize image size and attack surface, base image selection (distroless or minimal base images where appropriate), and non-root user configuration for security compliance. For applications with external dependencies (databases, message queues, third-party APIs), we document and configure the runtime environment variables and secrets injection pattern.
GKE cluster configuration for the appropriate workload type: Standard (for full Kubernetes API control) or Autopilot (for managed node pools). Node pool design: machine type selection, autoscaling configuration, and regional vs. zonal cluster decision. Workload Identity configuration for pod-to-GCP-service authentication without static service account key files. Network policy configuration for inter-pod traffic control. Binary Authorization policy for image provenance verification.
Deployment manifests, StatefulSet configurations (for stateful workloads), ConfigMaps and Secrets, resource requests and limits, liveness and readiness probes, Pod Disruption Budgets, and Horizontal Pod Autoscaler configuration. Each workload configured with production-appropriate settings — not tutorial defaults that break under real load.
Helm charts for applications that need to be deployed across multiple environments with configuration differences. Charts designed for maintainability: values files per environment, named templates for DRY chart logic, and chart tests for basic deployment validation.
For applications that are HTTP-triggered, stateless, and don't require full Kubernetes capabilities, Cloud Run is often the better platform choice — lower operational overhead, automatic scaling to zero, and simpler configuration. We make this recommendation explicitly when it is appropriate rather than defaulting to Kubernetes for every workload.
Kubernetes manifests or Helm charts written for each application. Workloads deployed to non-production, validated against acceptance criteria, then promoted to production.
Monitoring dashboards for workload health, scaling events, and resource utilization. Runbooks for common operations: image updates, scaling adjustments, pod restarts, and rollbacks. Platform documentation handed over with the engineering team trained.