1. Start With the Right Abstractions
Pods, Deployments, and Services aren't interchangeable concepts — they're layers. Deployments manage the desired state of your Pods; Services give those Pods a stable network identity as they're replaced. Teams that skip straight to YAML without understanding this hierarchy end up debugging problems that a clearer mental model would have prevented.
2. Resource Requests and Limits Aren't Optional
Without CPU and memory requests, the scheduler is guessing where to place your workloads. Without limits, one noisy container can starve everything else on the node. Set both deliberately, based on observed usage — not defaults copied from a tutorial.
3. Design for Zero-Downtime Deployments
Rolling updates only protect you if readiness and liveness probes accurately reflect whether a Pod can serve traffic. A container that reports "ready" before it's actually warmed up will cause real user-facing errors during every deploy — the opposite of what Kubernetes promises.
4. Secrets and Config Don't Belong in the Image
Baking credentials or environment-specific config into a container image couples your artifact to a single environment and makes rotation painful. Externalize configuration through ConfigMaps and Secrets — or better, a dedicated secrets manager — so the same image can be promoted safely across environments.
5. Observability Before Scale
A cluster running dozens of microservices without centralized logging, metrics, and tracing is a cluster nobody can debug at 2am. Instrument workloads from day one rather than retrofitting observability once something breaks in production — the second time is always more expensive than the first.
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