CI/CD · Ship safely, repeatedly

What is the difference between continuous delivery and continuous deployment?

Mediumcontinuous deliverycontinuous deploymentreleaseUpdated Oct 2026
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Short answer

Continuous delivery keeps every passing change releasable but may require a human decision to ship. Continuous deployment automatically sends every change that passes the pipeline to production.

Both practices require the same foundation: a reliable pipeline, a deployable artifact, automated tests and the ability to roll back. The difference is who or what makes the final production decision.

  • Continuous delivery: the artifact is always deployable, but a person or business process chooses when to release
  • Continuous deployment: no manual gate, because a passing pipeline deploys to production automatically
  • Continuous delivery is common where regulation, customer communication or coordinated launches matter
  • Continuous deployment demands strong observability, fast rollback and confidence in the test suite
yaml
deploy_production:
  stage: deploy
  when: manual
  environment: production
  script:
    - ./deploy.sh production
A manual production job is continuous delivery, not continuous deployment.
Continuous deployment is not automatically better. If the team cannot detect a bad release quickly, automating the final step just makes failures faster and wider.

Follow-ups you should expect

Interviewers use these to check whether the first answer was understanding or memorisation.

  1. 1Which model would you choose for a regulated product?
  2. 2What has to be true before removing the production approval?
  3. 3How do feature flags interact with continuous deployment?
  4. 4Is a manual approval gate an anti-pattern?

Say it in the interview

Continuous delivery means every change that passes the pipeline is ready to ship, but a person may still decide when it goes out. Continuous deployment removes that decision and ships every passing change automatically. The technical foundation is the same, but continuous deployment raises the bar on observability, rollback and test confidence. I would move toward it gradually, starting with low-risk services and measuring change failure rate.

Use this as a model, not a script. Saying it in your own words is what makes it survive follow-up pressure.

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Reading is step one

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