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Continuous Delivery vs Continuous Deployment: Which Do You Need?
Delivery leaves the production push to a person; deployment ships every passing build. The difference is who owns release risk.
Quick answer
- Continuous delivery produces a release candidate on every passing build and leaves the production push to a person.
- Continuous deployment removes that person: every build that passes the full pipeline reaches production automatically.
- The gap between them is not automation effort but detection and reversal — can you notice a bad release and undo it faster than a customer can complain?
- Feature flags let you separate deploy from release, which is what makes automatic deployment survivable for user-facing changes.
- Most teams should adopt continuous delivery first and move to deployment only after rollback is boring.
What is the difference between continuous delivery and continuous deployment?
In continuous delivery, the pipeline ends with an artifact that is ready to deploy and has been proven in a production-like environment. A human then chooses when to release it. The release may be daily or quarterly; the defining property is that the choice exists and the artifact does not need rebuilding.
In continuous deployment, there is no choice point. A commit that passes every gate is live within minutes. The pipeline must therefore include the judgement a human would have supplied: comprehensive tests, canary analysis, automatic rollback, and alerts that fire on user-visible symptoms rather than on process exit codes.
| Attribute | Continuous delivery | Continuous deployment |
|----------------------------|----------------------------------|-------------------------------------|
| Who releases? | A person | The pipeline |
| Gate before production | Manual approval | Automated checks only |
| Prerequisite | Deployable artifact, staging | Detection + automatic rollback |
| Release cadence | On demand | Every passing commit |
| Blast radius control | Batch releases, chosen timing | Flags, canaries, small changes |
| Typical blocker | Manual steps and environment drift| No reliable way to detect regressions|What has to be true before you can deploy continuously?
Four things. Tests must catch the regressions that matter, because no human is reviewing the change before it ships. Deploys must be reversible without a database restore, which means backward-compatible migrations and the previous version still runnable. Production must be observable at the level of user actions, not just process health. And the team must be able to deploy during working hours, because deploying only when everyone is awake defeats the purpose.
If any one of these is missing, continuous deployment converts a bad afternoon into an outage. That is why the honest prerequisite list is shorter than the tooling list and much harder to satisfy.
When is continuous delivery the better choice?
Choose delivery when releases need coordination outside engineering: a regulated change window, a mobile app waiting on store review, a pricing change that marketing announces, or a schema migration that needs a quiet period. In those cases a human release gate is not bureaucracy, it is the control that makes the change safe.
Delivery is also the right stopping point for small teams without production observability. A weekly release that someone consciously approves, with a known rollback procedure, is better than an automatic deploy nobody is watching. The goal is not maximum frequency; it is minimum batch size at a risk level you can actually detect.
How do you move from delivery to deployment?
Do it one change class at a time. Start by auto-deploying internal tools or a low-traffic service, and measure how often a release causes a problem and how long it takes to notice. If the answer is rarely and quickly, widen the scope. If a rollback takes an hour, that is the work to do next, not more automation.
Use flags to decouple deploy from release even after you automate. Automatic deployment then becomes a logistics event — the code is in production but dark — while the release decision stays deliberate. That combination gives most of the batch-size benefit without betting the user experience on the test suite.
Where this bites vibecoders
Vibe-coded apps on Vercel, Netlify, or Railway usually practise continuous deployment whether or not anyone decided to: a push to main is live. The dangerous part is the database migration that runs on boot, because the old and new version cannot coexist and rollback is no longer a redeploy. Before calling it continuous deployment, check that the previous version still works against the current schema.
Where AI coding assistants get this wrong
- Auto-deploying every push while migrations run at startup with no backward compatibility.
- Removing the manual approval gate without adding automated rollback.
- Deploying continuously but only monitoring process uptime.
- Assuming a passing test suite replaces a canary when the tests do not cover the changed path.
- Deploying only outside business hours, so nobody is present to detect a regression.
- Treating feature flags as a substitute for reversible database changes.
Checklist
- Decide which of the two practices you actually run, and write it down.
- Confirm the previous release can run against the new schema.
- Make rollback a single command and test it on a real deploy.
- Add user-facing monitoring before removing the manual gate.
- Introduce automatic deployment on the lowest-risk service first.
- Use feature flags to separate deploy from release.
FAQ
Is continuous deployment riskier than continuous delivery?
It has a higher release frequency, so it amplifies whatever detection you have. With strong tests, canaries, and rollback it is often safer than batching changes into a large monthly release, because each change is small enough to diagnose. Without those controls it is simply faster at shipping mistakes.
Do feature flags count as continuous deployment?
They are a tool that makes it practical. The code is deployed automatically but the feature is dark until a flag is enabled, so the release decision stays human. Many teams call that continuous deployment with controlled release, which is accurate as long as the deployed default is safe.
How many deploys per day should we aim for?
There is no universal number. The useful target is the smallest batch you can review and reverse confidently. If one change per day keeps review quality high, that is enough. Increasing frequency while review or rollback degrades is not progress.
Can a mobile app use continuous deployment?
Only partially, because the app store review is outside your control. Mobile teams typically use continuous delivery for the binary and continuous deployment for the backend and remote config. Over-the-air update mechanisms have their own store rules and should be treated as a separate release channel.
Related topics
- What Is Continuous Delivery?
- Continuous Integration vs Continuous Delivery: What’s the Difference?
- What Is a Feature Flag?
- What Is a Canary Deployment?
- How to Roll Back a Bad Deploy
- What Are the DORA Metrics (and How Do You Measure Them)?