Getting an application running in a Docker container on a laptop takes an afternoon. Keeping that same container running reliably in production, for months, under real traffic, is a different problem — and it's where a lot of teams run into trouble.
Image bloat
A container built without multi-stage builds often ships build tools, source maps, and dev dependencies into production, inflating image size and slowing every deploy. Multi-stage Dockerfiles that compile in one stage and copy only the final artifacts into a minimal runtime image fix this, but it's a step teams often skip under deadline pressure.
No resource limits
A container with no memory or CPU limits set can consume the entire host's resources if something goes wrong — a memory leak in your application becomes a host-wide outage instead of a contained problem. Setting explicit resource limits per container is a small config change that prevents a large class of incidents.
Treating containers as pets, not cattle
If your deployment process involves SSHing into a running container to fix something, you've lost the main benefit of containerization: reproducibility. Containers should be disposable — if something's wrong, you rebuild and redeploy from the image definition, not patch a running instance by hand.
Skipping health checks
Without a proper health check defined, your orchestration layer (Docker Swarm, Kubernetes, or even a simple restart policy) has no way to know a container is actually unhealthy versus just running. A container that's up but not responding to requests looks "healthy" without one.
No log strategy
Container logs that only exist inside the container are lost the moment it restarts. Shipping logs to a centralized destination (rather than relying on docker logs after the fact) is the difference between debugging an incident and not being able to reconstruct what happened.