Continuous Delivery vs Continuous Deployment: The Difference and Which to Choose

Both terms abbreviate to “CD,” both start with “continuous” and both describe automated pipelines. No wonder teams use them interchangeably. But they describe different operating models, and the difference matters for how you staff releases, design tests and handle risk.

The short version: the only difference is who presses the final button. In continuous delivery, a person does. In continuous deployment, the pipeline does. This guide explains that gap, what it takes to automate it away and which teams should and should not.

The difference in one table

Both practices share everything up to the production release. Code is integrated continuously, built automatically, tested at several levels and deployed to staging. Then they split.

AWS states the distinction plainly: the difference is the presence of a manual approval to update production. With continuous deployment, production updates happen automatically without explicit approval.

Where the manual approval gap sits

Here is the end of a typical pipeline:

... -> Build artifact -> Deploy to staging -> Automated tests | Continuous delivery: [Human approval] -> Production Continuous deployment: ----(automatic)-> Production

That approval step can look different depending on the team:

• A product manager clicks “Approve and deploy” in the CI/CD tool. In GitHub Actions, for example, environments can require reviewers, and a listed reviewer approves or rejects the waiting deployment job.
• A release manager batches several changes into a scheduled release window.
• A change advisory board reviews a change record before a release.
• For mobile apps, the “approval” includes app store review, which the team does not control.

Each version adds safety in some cases and delay in all of them.

What the manual gate is good for

A human approval step is not a failure of automation. It is the right choice when:

• Release timing is a business decision. Marketing launches, customer communication or contractual dates may require releasing at a set moment.
• You cannot roll back quickly. Firmware, embedded devices, desktop software and mobile apps are hard or impossible to roll back once users install them.
• Tests are not yet trustworthy. If the team does not trust the test suite, a human check is a reasonable bridge while coverage improves.
• Regulation or contracts require explicit sign-off. Some environments require documented approval for certain changes. Often, a recorded peer review or pipeline approval satisfies this, but check your specific obligations. This is not legal advice.
• High-risk changes. Database migrations, pricing logic or security-sensitive changes may deserve extra eyes even in teams that deploy most changes automatically.

DORA’s guidance supports this view of continuous delivery as the broadly applicable practice. It describes continuous deployment as suited to web services but not to software such as firmware or mobile apps, and recommends teams start with continuous delivery even if they never adopt continuous deployment.

What the manual gate costs

The cost is less obvious, but real.

• Batching. When releases wait for approval, changes pile up. Larger batches are harder to test, review and roll back.
• Slower feedback. Developers find out about production problems days after writing the code, when context is gone.
• Rubber-stamping. Approvers who see dozens of changes rarely understand each one, so the approval adds delay without adding much safety.
• Release-day stress. Scheduled releases concentrate risk into a few tense events.

DORA’s research on change approval is worth knowing here. Its guidance reports that heavyweight external approvals, such as change advisory boards or senior manager sign-off, were associated with worse software delivery performance, with no evidence that they lowered change fail rates. It recommends peer review during development, supplemented by automation, instead.

Key takeaway: The question is not whether a human approves, but whether the approval adds real information. If approvers cannot judge the change better than your tests and reviewers already did, the gate is mostly delay.

What it takes to automate the gate away

Teams that run continuous deployment safely usually have these in place first. Use this as a readiness checklist.

Feature flags deserve special mention. They let you deploy code continuously while still letting the business decide when customers see a feature. That keeps the business control people want from a manual gate, without slowing every code change.

Which teams should choose which

Choose continuous delivery (keep the gate) if you:

• Ship mobile apps, desktop software, firmware or anything users install
• Work in an environment where your obligations require explicit release approval you cannot meet through recorded peer review
• Have limited automated test coverage or monitoring today
• Need releases tied to launches, customer notices or contractual dates
• Are early in your CI/CD journey

Move toward continuous deployment (remove the gate) if you:

• Run web services, APIs or SaaS products where rollback is fast
• Have trusted tests, peer review on every change and strong monitoring
• Use feature flags to control what customers see
• Find that approvals have become rubber stamps that cause batching
• Want to reduce lead time and make releases routine

Mix both. Many teams use continuous deployment for most services and keep a manual gate for a few high-risk ones, such as billing or authentication. Others auto-deploy to production but keep a manual step for database migrations. You do not need one rule for everything.

How to move from delivery to deployment

1. Measure first. Track DORA’s delivery metrics, such as change lead time, deployment frequency, change fail rate and failed deployment recovery time, so you can see the effect of changes.
2. Audit what approvers actually catch. Look at the last 3 months. How many releases did approvers stop, and why? If the answer is “almost none,” the gate is adding little.
3. Strengthen automated checks where approvers did catch issues. Turn each human check into a test, scan or alert.
4. Add feature flags and progressive rollout so a bad change reaches few users and can be turned off fast.
5. Practice rollback until it is boring.
6. Remove the gate for one low-risk service. Watch the metrics for a few weeks.
7. Expand gradually, keeping manual gates only where they add real information.

Our step-by-step guide to building a CI/CD pipeline shows how to add an approval gate in practice, and our explainer on what CI/CD is covers the earlier pipeline stages. If you are reworking how your IT team approves changes in general, our guide to ITSM explains standard, normal and emergency changes. Larger organizations often roll these patterns out through shared templates; see platform engineering.

Conclusion

Continuous delivery and continuous deployment share the same automated pipeline and differ by one decision: whether a person approves each production release. Continuous delivery fits nearly every kind of software and is the right foundation. Continuous deployment is worth pursuing for web services with strong tests, monitoring, feature flags and fast rollback, where a manual gate mostly adds delay. Decide service by service, based on what the approval actually catches.

Sources

• AWS, What is continuous delivery? — definitions and the manual approval difference
• DORA, Continuous delivery capability — when continuous deployment fits and the recommendation to start with continuous delivery
• DORA, Streamlining change approval — research on heavyweight change approval and peer review
• GitHub Docs, Reviewing deployments — how manual deployment approvals work in GitHub Actions