Container Strategy Template for Engineering Teams

No. Some workloads are better served by other models. Event-driven functions with bursty, low-frequency traffic often fit better on serverless (Lambda, Cloud Functions). Large batch processing jobs may run more cost-effectively on dedicated VMs. Legacy monoliths with deep OS dependencies may need significant refactoring before they can run in containers. Containerize the services where portability, scaling, and deployment frequency are priorities.

For a single service that is already [12-factor compliant](/glossary/microservices), expect 1-2 sprints including CI/CD pipeline changes and production validation. For a service that needs refactoring (externalize state, add health checks, remove host dependencies), add 2-4 sprints. For a full platform migration of 10-20 services, plan 2-3 quarters with a phased wave approach.

Not necessarily. Kubernetes adds significant operational complexity. For teams running fewer than 10 containers, Docker Compose (for development) plus a managed container service like ECS Fargate or Cloud Run may be simpler and cheaper. Kubernetes becomes valuable when you need sophisticated scheduling, service mesh, custom operators, or multi-cloud portability. See the [Kubernetes adoption template](/templates) for an evaluation framework.

Use connection pooling (PgBouncer for PostgreSQL, ProxySQL for MySQL) as a sidecar or standalone service. Containers scale up and down frequently, and each new container opens database connections. Without pooling, you can exhaust database connection limits quickly. Inject database credentials via a secrets manager (Vault, AWS Secrets Manager), never via environment variables in the Dockerfile. ---