Kubernetes Cost Optimisation: Measure Before You Change
Right-sizing, targeted Spot usage, traffic-shaped autoscaling, and storage tiering — a practical cost review
A useful deployment pipeline produces repeatable evidence that a particular version can be released. Review the path from source code to production, including failures and recovery.
A dependency cache speeds up a build; it is not the record of what was deployed. Use cache keys that reflect relevant dependencies and tooling. Preserve identifiable release artifacts and avoid placing credentials or other secrets in caches.
Connect deployment jobs to the tests and checks they require. Document any emergency path and its follow-up checks. Treat flaky tests as defects to investigate rather than silently ignoring their results.
Use environments, restricted credentials and approval rules appropriate to the project. Prevent overlapping production deployments when they would interfere with one another.
Keep the previous known-good artifact available and test redeployment. Database changes may require a separate recovery plan: reverting application code alone may not reverse data changes.
Link the deployed version to its build, test results and release notes. Agree on who responds to a failed release and what signals trigger rollback.
Sergey Lapidus
CEO
Sergey leads Sparkler Soft and works with clients on product direction and delivery.
LinkedIn →Right-sizing, targeted Spot usage, traffic-shaped autoscaling, and storage tiering — a practical cost review
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