Deployment Time: What It Is, How to Measure & Improve

Your code is ready. The tests pass, and the feature works – but the deployment? That’s where things slow down. Delays, approvals, and bottlenecks turn a simple release into a long waiting period.

This all comes down to deployment time, which tells how long it takes to push code from development to production. A slow process means missed opportunities, while a fast, reliable one keeps your team moving.

So, what exactly is this metric, why does it matter, and how can you speed it up without sacrificing stability? This guide breaks it all down. Let’s get started.

What Is Deployment Time?

Deployment time is the duration between merging a pull request (PR) and when the changes are successfully deployed to production environments.

This metric tracks just one part of the bigger software development lifecycle. Still, shorter deployment time is crucial in efficiently getting changes from development to users. This metric is treated as a subset of lead time for changes, which is broken down into four key stages:

• Coding time: Time spent writing the application code before it's ready for review.
• Pickup time: The period between opening a pull request and when a reviewer starts looking at it.
• Review time: The time spent reviewing and approving the code.
• Deployment time: Measures the duration of the last step where the application executable is deployed to production.

You may also wonder what a good deployment time is.

While there aren’t any reliable benchmarks, we can check out the DevOps Research and Assessment (DORA) 2024 report. Since deployment time is a subset of lead time for changes, we can look at what’s expected from organizations from that point of view:

• Elite performers: Under 24 hours
• High performers: 1 day to 1 week
• Medium performers: Between a week and a month
• Low performers: 1 month to 6 months

Similarly, a good deployment frequency for Elite performers is on-demand or multiple times per day. That brings us to the next point:

Deployment Time Vs. Deployment Frequency

Deployment time is the time required to deploy a change once it's ready, while deployment frequency is the frequency with which your code hits production.

The two are closely connected.

If your deployments are slow, frequent updates are nearly impossible. Improving deployment time is the key here, as it allows you to release more consistently and keep code moving.

Key Benefits of Tracking Deployment Time

Now that you know how deployment time differs from deployment frequency, it’s time to understand why tracking this metric matters. It brings in benefits like:

• Exposes dev-to-deployment gaps: Many teams complete pull requests in days, yet outdated processes, approvals, and legacy infrastructure delay deployment timelines to weeks. Tracking deployment time makes these gaps visible so you can push for improvements and remove bottlenecks that slow down releases.

• Identifies pipeline bottlenecks: Lagging deployments aren’t always about coding speed. CI/CD pipelines can introduce friction with slow builds, inefficient automated testing, or excessive manual steps. Tracking deployment time highlights delays, but teams need deeper analysis to pinpoint and fix the underlying issues. That would mean looking at additional metrics, logs, or process bottlenecks.
• Improves software reliability: Shorter, well-optimized deployments reduce errors (you can see that reflected in a lower change failure rate, for example). This cleaner process, in turn, leads to smoother, more reliable releases.
• Boosts team productivity: Developers perform best when their work reaches production quickly because they experience less frustration from slow or failed deployments. A faster deployment time reduces disruptions and keeps engineers focused on building instead of waiting.
• Supports continuous delivery goals: Tracking deployment time helps you move closer to continuous delivery by identifying inefficiencies and enabling more frequent, automated, and reliable deployments. This accelerates the delivery pipeline and ensures smoother releases.

Why Improve Deployment Time?

Why should you focus on improving deployment time? Let’s break down the key reasons that make it a game-changer for your team and your software:

• Faster iterations → better innovation: The quicker you deploy, the faster you can get new features into users' hands. This speeds up the feedback loop, which helps your team innovate and iterate much faster.
• Reduced downtime and rollbacks: An optimized deployment process means you can respond to issues quickly and keep downtime minimal. If something goes wrong, recovery is quick, which reduces the need for time-consuming rollbacks.
• Higher developer efficiency: The less time your team spends waiting for deployments, the more time they have to iterate on features, address feedback, and improve the product more efficiently. This leads to better productivity overall.
• Better user experience: Optimizing deployment time speeds up the delivery of updates, bug fixes, and new features to customers, improving their experience and engagement with your product.

What Impacts Deployment Time?

Improving deployment time comes with many benefits, but it’s equally important to understand that a few things could be slowing it down without you even realizing it, such as:

• Build and test duration: Slow test suites and inefficient builds can really slow down the deployment process. Every change has to undergo a long testing phase before it can be deployed, and that can feel like forever.
• CI/CD pipeline bottlenecks: A bottlenecked CI/CD pipeline filled with manual approvals and slow automation disrupts deployment speed. Delays stack up, which blocks faster deployments and frustrates the development team.
• Infrastructure constraints: When using cloud infrastructure, slow auto-scaling can delay deployments, especially if instances take too long to spin up. In on-prem environments, scaling is even harder as resources are fixed, which causes issues when demand spikes. Both scenarios lead to longer deployment times.

• Merge conflicts and code integration issues: Long-lived branches and complex merges make version control a nightmare. The longer changes sit unmerged, the harder they are to integrate, which leads to deployment delays and unpredictable failures.
• Deployment strategies: Deployment strategies like canary deployments, blue-green deployments, or feature flags can shake up your deployment time. While they ensure safer rollouts, mismanaging them can introduce extra validation steps and traffic control measures, ultimately prolonging deployment duration.

How to Calculate Deployment Time?

Deployment time measurement tracks how long it takes for code to go from “Merged” to “Live.” In Axify, this starts when a pull request (PR) is merged and stops when the change is fully deployed in production environments.

Deployment time is typically measured in seconds, minutes, or hours. For example, a team’s deployment time is 300 minutes or 5 hours.

Other Crucial Metrics to Consider

Tracking deployment time is great, but it doesn’t tell the whole story. To really understand DevOps performance, here are a few more metrics worth keeping an eye on:

• Deployment frequency: Deployment frequency tells you how frequently your team pushes code to production. High-performing teams aim for multiple daily deployments, while slower teams might struggle with just a few per month. If your releases feel like rare events, there’s room for improvement.
  
  
• Lead time for changes: This metric is all about speed, i.e., how long it takes for a change to move from the first commit to being live in production. When it’s quick, your development process is smooth. However, if it’s slow, something’s clogging the pipeline. Maybe it’s manual steps, long test cycles, or too many approvals.

• Failed deployment recovery time: Also known as time to restore service or mean time to recovery (MTTR), it tells how long it takes to roll back or patch a failed release. A quick recovery keeps downtime low and reduces the impact of issues on users. If it takes too long, inefficient processes or weak rollback strategies might be to blame.

• Mean Time to Deploy (MTTD): This metric reflects the average time it takes for a deployment to go live. A low MTTD means your deployment process is efficient, and your team is shipping fast. When deployments stretch into an extended period, something’s slowing things down.
• P95 deployment time: With P95 deployment time, you can know how long 95% of deployments take to complete. It’s a great way to spot inconsistencies in your deployment process. For instance, if most deployments finish in 10 minutes but a few take hours, there’s likely a bottleneck slowing things down.

How to Reduce Deployment Time?

Ever wondered how top-performing DevOps teams keep releasing every day? It’s not magic. It is all about optimizing deployment time by using the right strategies. And we’ll analyze the best ones below:

1. Streamline CI/CD Pipelines

If your Continuous Integration and Continuous Deployment (CI/CD) pipeline takes forever, unnecessary approvals, redundant tests, or inefficient scripts may be creating bottlenecks. Identifying and streamlining these steps can significantly speed up deployments.

But how do you figure out where the bottleneck is?

Check your key metrics, such as time to deployment, change failure rate, and the number of times per day you ship code. If your team spends more time waiting on approvals or rerunning unnecessary steps than actually deploying, it’s time to clean up the process.

What you can do:

• Cut out manual processes wherever possible. DevOps tools like Jenkins and GitLab can automate repetitive tasks and save your team up to 10-50% of time.
• Keep your testing smart. Don’t run everything every time. Instead, focus on what matters for a reliable deployment.
• Reduce unnecessary approvals. If security or compliance demands a sign-off, fine. But if it’s just “how we’ve always done it,” rethink that.

2. Parallelize Builds and Tests

If your pipeline runs tests one by one, you're wasting time. Distributed computing addresses this by running tasks in parallel.

For example, a test suite that takes 30 minutes to complete on a single machine can be split across five machines using tools like Buildkite or GitHub Actions. Each machine runs a portion of the tests simultaneously, reducing the total time to just 6 minutes.

The result?

Quicker deployments, faster feedback, and fewer delays.

3. Optimize Infrastructure and Cloud Deployment

If your infrastructure can’t keep up with demand, deployments slow down.

Too much traffic? Servers struggle.

Low traffic? Resources sit idle and burn costs.

Neither is ideal.

Auto-scaling options like AWS Auto Scaling, Kubernetes Horizontal Pod Autoscaler, and Google Cloud Managed Instance Groups can instead scale up or down based on real-time demand.

Serverless options such as AWS Lambda or Google Cloud Functions take it further—there are no servers to manage; you can just deploy and go.

4. Encourage Trunk-Based Development

The longer code stays separate, the more complex it gets to merge, test, and deploy without introducing problems. Trunk-based development keeps things moving by merging small changes directly into the main branch multiple times daily.

For example, instead of working on a new checkout button in a separate branch for weeks, you can break it into smaller updates and merge continuously:

• First, add the button’s basic UI and merge it into the main.
• Next, connect it to the payment API and merge again.

5. Invest in Deployment Automation

Manually deploying code is slow, error-prone, and a waste of time: the more manual processes involved, the higher the change failure rate.

Deployment automation takes care of everything, including versioning, testing, rollbacks, and releases, without requiring constant human input. You can use tools like Spinnaker, ArgoCD, and GitHub Actions to streamline this, so deployments are always smooth, fast, and reliable.

6. Monitor Deployment Metrics Regularly

When deployments slow down or fail too often, guessing won’t fix the problem. You need data. With DevOps dashboards, tools like Axify deliver real-time insights into application performance by tracking key metrics such as:

• Lead time for changes
• Deployment frequency
• Time to restore service
• Change failure rate
• And more

7. Make Smaller, Incremental Changes

Big releases are a gamble. Ship too much at once, and you’re stuck debugging a giant mess when something breaks.

Smaller changes are much safer and quicker to deploy.

For instance, when migrating a database, instead of making a massive schema change all at once, you can:

• First, deploy the new schema alongside the old one and keep both active.
• Next, update the application to write to both schemas while monitoring for issues.
• Once everything looks stable, switch to the new schema and remove the old one.

If something fails, rolling back is easy, and there’s no impact on users.

8. Shift-Left on QA

Are you frequently catching bugs right before deployment?

That’s a nightmare. Fixing them so late disrupts workflows and turns deployments into chaos. Plus, it costs up to 30 times more. You can avoid this by implementing shift-left testing that moves automated testing earlier in the software development process so problems get caught before they escalate.

Combining shift-left on QA with other approaches, Axify helped BDC Canada reduce its product delivery by up to 51%. As the issues were detected and fixed earlier in the pipeline, the deployment time was reduced, allowing faster and more efficient releases.

9. Give the Team Autonomy

If a team has to jump through hoops for every little change, deployments slow down to crawl. Too many approvals and too much red tape kill momentum.

Your team needs the freedom to ship updates without waiting for management to say yes every time. If automated testing and key metrics show a release is good to go, why hold it back?

10. Achieve Data Parity Across Environments

Ever had code that runs perfectly in staging but crashes in production?

This happens when environments don’t match. Data parity prevents this and keeps development, staging, and production as consistent as possible.

Say you're working on a payment processing system. If staging only has mock transactions, you might miss real-world edge cases. But with properly masked live data, you catch issues before they hit production, which leads to quicker deployments and fewer surprises.

11. Separate Deployment From Release

Deployment and release are not the same. Just because code is deployed doesn’t mean it has to go live immediately. Frequent deployments keep the system in a releasable state, but the actual release should happen when it makes sense for the business.

Feature flags make this possible. You can deploy frequently to keep updates ready in production while giving product managers control over when to release.

One example includes deploying a new checkout flow behind a feature flag and keeping it inactive until the business is ready. When the time comes, you can flip the switch with no extra deployments or delays—just a fast, low-risk release.

12. Blue/Green Deploys

Deploying straight to production can be risky. If something breaks, rolling back takes time, and downtime hurts users and the business. Blue/green deployments solve this by keeping two environments: one live (blue) and one idle (green).

When a new update is ready, it’s deployed to the green environment while users continue using blue. Once everything checks out, traffic is switched to green instantly. If something goes wrong, rolling back is just as fast.

Wrapping It Up: Reduce Deployment Time with Axify

In this guide, we’ve explored the crucial role deployment time plays in the SDLC and why speeding it up can lead to better productivity, faster feedback loops, and more reliable releases.

If you’re serious about cutting deployment time, Axify can help with features like:

• Real-time workflow tracking: With detailed Visual Stream Mapping (VSM) of your entire software development process, Axify helps identify inefficiencies, which allows your team to optimize workflows for faster deployments.

VISUEL HS : Axify value stream mapping-1 Alt tag: Axify VSM

• Deployment performance analysis: Axify also offers smooth tracking and insights into DORA metrics across your organization. As a result, you can monitor trends and make data-driven decisions that enhance deployment speed.

VISUEL HS : Axify DORA metrics dashboard 4 key metrics Alt tag: DORA metrics dashboard

• Software delivery forecast tool: Axify's forecasting tool predicts delivery dates by analyzing historical data and current progress. That means you can manage schedules effectively to maintain a steady deployment pace.

VISUEL HS : software delivery forecast-1 Alt tag: Delivery forecasting by Axify

• Resource allocation: Lastly, Axify also helps optimize resource allocation by providing real-time insights into team activities, task alignment with corporate objectives, and resource usage. Allocating resources correctly is a critical step toward optimizing your activities, which will be reflected in all your engineering metrics, including deployment time.

Let’s work together to transform your release pipeline. Book a demo with Axify now!