When was the last time you looked at how long it actually takes to ship code once it’s ready? Not from idea to production, not from first commit, but from approved to deployed to production. That short window is your time to deploy, and it quietly controls how fast your team can move.
In modern DevOps, time to deploy sits alongside classic DORA metrics such as lead time for changes, deployment frequency, and time to restore service. If you care about delivering value quickly and safely, you should care about time to deploy.
What Is Time to Deploy?
Time to deploy measures how long it takes for a change to move from the moment a deployment is triggered to the moment that change is fully running in the target environment, most often production. It’s a narrow slice of the delivery pipeline, focused only on the deployment phase, not the entire development cycle.
Teams usually define it in one of a few ways, as the period from:
- merge to production
- clicking the deploy button to the new version receiving traffic
- pipeline start to successful completion in production
The exact definition is less important than the consistent use of a single definition. What matters is having a clear, stable way to understand how long a deployment truly takes.
Time to deploy is also different from other delivery metrics. It doesn’t measure coding time, issue cycle time, or the full lead time for changes. Instead, it highlights the specific deployment window-the final step before users see an update.
Why Time to Deploy Matters More Than You Think
You might already track deployment frequency-how often you push to production. That’s good. But frequency and time to deploy are tightly linked: if each deployment takes hours, your deployment frequency will always hit a ceiling.
Shorter time to deploy helps you:
1. Ship more often
- If a production deployment takes 5-10 minutes, multiple deploys per day are realistic. If it takes 3 hours, they’re not.
2. Recover faster from incidents.
- When something breaks in production, your fix still needs to go through the same pipeline. A long deployment time means a long time to restore service.
3. Reduce stress on engineers.
- Long, fragile deployments create “release anxiety.” Short, boring deploys make shipping feel routine.
4. Support real-time deployment patterns
- If your pipeline is fast and reliable, you can move toward near real-time deployment of small changes, not literally every millisecond, but fast enough that deployment no longer blocks your product decisions.
Put simply: slow deployments turn every change into a mini-project. Fast deployments make change cheap.
How to Measure Your Time to Deploy Today
You don’t need a fancy tool to start. Begin with whatever you already have:
- CI/CD logs (i.e., GitHub Actions, GitLab CI, CircleCI, etc.)
- Your deployment tool (e.g., Argo CD, Spinnaker, Octopus, etc.)
- Git hooks or deployment webhooks
Track at least:
- Start Timestamp: when deployment is triggered/pipeline begins
- End Timestamp: when the change is successfully deployed to production
- Status: success or failure
From there, compute:
- Mean Time to Deploy (MTTD): the average duration across successful deploys
- P95 time to deploy: how long the slowest 5% of deploys take (this is typically where your ugly bottlenecks hide)
Look at both. A low average with occasional horror-story deploys is a risk, not a win.
Time to Deploy vs. Deployment Frequency
These two metrics should be read together:
- Deployment frequency tells you how often code is shipped.
- Time to deploy tells you how painful each shipment is.
Some common patterns:
- High frequency, low time to deploy: This is where elite teams live, with small, frequent, low-drama production deploys.
- Low frequency, high time to deploy: This leads to big-batch releases, long change queues, and a huge blast radius when something goes wrong.
- Low frequency, low time to deploy: You could ship more often, but habits, approvals, or culture are holding you back.
To improve, don’t just push for “more deployments.” Focus on making each deployment fast and reliable; frequency will follow.
What’s Slowing Down Your Production Deploys?
When teams finally measure their deployment time, they’re often surprised by where it goes. Common culprits include:
- Manual QA gates: Long regression runs after the code is “ready” but before it’s deployed.
- Approval bottlenecks: Change Advisory Boards, manual sign-offs, or Slack approvals that sit for hours.
- Fragile pipelines: Flaky tests, unreliable environments, or slow artifact builds.
- Environment drift: Works in staging but not in production, resulting in additional checks and manual fixes.
- Big-bang releases: Huge batches of changes are deployed at once, requiring extra validation and rollback planning.
Your goal isn’t to eliminate risk. It’s to move risk earlier and leave production deploys as dull as possible.
Final Thoughts
Improving your team’s time-to-deploy isn’t about chasing vanity metrics. It’s about creating a delivery process where shipping changes happen all the time, rather than being risky. When deployments are quick, predictable, and automatic, teams ship more often, fix problems faster, and feel more confident in the entire release pipeline.
Start by figuring out where you are now. Then, eliminate one bottleneck at a time. Over time, those small changes will add up to a delivery process that makes deploying to production feel almost easy. And when it’s easy to deploy, it’s also easy to provide value.
