The essential DevOps metrics to track

Taylor Bruneaux


It takes time and effort to build a thriving DevOps culture and practice. However, without a means to measure the results, you won’t have a way to determine whether the initial implementation succeeds.

DevOps metrics can help provide insight into how your DevOps program is functioning and whether it’s meeting its expressed goals. DevOps metrics can also help guide future improvements by pinpointing ongoing challenges in your software development process.

In this article, we’ll discuss which high-value metrics you can focus on to ensure continuous improvement and foster a culture of operational excellence.

Understanding DevOps metrics

A DevOps metric is any metric that shows how well your DevOps pipeline is functioning through each stage of the software development lifecycle. They measure how effective your current process is at enabling developers to release new features, improve release quality, resolve issues, and hit your application’s performance goals.

A good DevOps metric should enable your team to set meaningful Key Performance Indicators (KPIs) for your application pipeline that show you’re delivering:

  • Increased business value (additional revenue or revenue savings for the company, features for the end user)
  • Better system performance (your application/service delivers more value with fewer computing resources)
  • Greater developer effectiveness (developers can do more in less time)

For example, deployment frequency—the number of releases in a given period—will be higher when you have a fully automated Continuous Integration/Continuous Delivery (CI/CD) pipeline that’s reliable and easy to use. Similarly, lead time for changes—the time it takes for committed code to be released to production—goes down when you have an efficient code review process and are shipping high-quality code with few defects.

Tracking DevOps metrics enables you to:

  • Improve decision-making. Using DevOps metrics, you can identify where investing time in improving your tools or processes will yield the most significant impact. For example, a high defect escape rate tells you to focus energy on changes preventing bugs from entering production, such as implementing automated testing.
  • Align DevOps practices with business objectives. If the business’ overall goal is to drive new revenue, you can bring the DevOps process in line with this goal through improvements that accelerate the rate of feature release. Alternatively, if the goal is to reduce costs, you can focus on metrics like time to restore service and lead time.
  • Improve efficiency. Use DevOps metrics to gauge how you can enable each developer on your team to do more with less or get more extraordinary performance out of your applications while maintaining or even reducing costs, such as cloud computing spend.
  • Increase the success rate of DevOps initiatives. Some organizations struggle to identify why their DevOps initiatives fail to meet expectations. Establishing reliable metrics and well-defined KPIs allows them to pinpoint specific obstacles and focus on resolving issues rather than relying on guesswork, ultimately leading to more effective DevOps implementation and better outcomes.

The DevOps metrics to track

Tailor the DevOps metrics you to your company and its goals. However, some standard metrics will provide most organizations with a clear baseline. Here are the ones we’ve used or seen used at companies with successful DevOps practices.

Deployment frequency

What it measures: Deployment frequency is how often you deploy changes—either new features or fixes—to production in a given time period (e.g., monthly).

Why it’s essential: Deployment frequency is one of the critical metrics in DORA (DevOps Research and Assessment), an industry framework for measuring the performance of DevOps teams. A high deployment frequency means new features end up more quickly in end users’ hands. It also means development teams can deploy fixes more rapidly, which minimizes the lost revenue and customer dissatisfaction endured due to an application error.

Note that “high deployment frequency” is relative to each time and application. For one time, shipping several times a day is considered high; for another, a few times a week will qualify.

Lead time for changes

What it measures: Another DORA metric, lead time for changes, measures when an engineer checks code into source control and when it is available in production to end users. Lead time for changes encompasses the time it takes to review, test, and deploy the change through the various stages of a DevOps pipeline (dev, test/QA, stage, prod).

Why it’s important: A shorter lead time enables your teams to bring new features to market faster, accelerating your responsiveness to market changes and allowing you to win (or keep) end users by being first to market with critical new functionality.

A long lead time indicates an inefficiency somewhere in your DevOps processes. You can shorten lead times by shipping smaller change sets, adding automated testing to your DevOps pipelines, freeing up more time for code reviews, or adding more automation to your CI/CD pipeline (e.g., moving to Infrastructure as Code to automate the creation of new environments).

Change failure rate

What it measures: How often a deployment results in a failure in production.

Why it’s important: A failure in production - e.g., a secret that you failed to rotate, an HTTP 500 server error due to a missing environment variable - is often an “all hands on deck” moment in which dev team members must stop working on new features to get the system back up and running.

A lower change failure rate results in a more stable and reliable application. That boosts revenue by freeing developers to work on new, value-added changes.

Time to restore service

What it measures: Time to restore service is the time it takes to restore application service after a failed deployment, also known as mean time to recovery (MTTR) or failed deployment recovery time.

Why it’s essential: Application errors and system downtime result in lost revenue and decreased user trust. Teams with a low time to restore usually have efficient incident detection and response procedures that enable them to shorten the recovery process, keeping losses to a minimum.

Defect escape rate

What it measures: Defects that teams could have detected in development or a pre-production environment made their way into production.

Why it’s essential: Defects are often costlier to fix in production than in pre-production. Beyond the negative business impact on end users, it often takes longer to perform root cause analysis and develop a fix due to the complexity and security restrictions involved in debugging in production.

Teams can lower their defect escape rate through increased automated and manual testing and other quality assurance processes, such as code linting and automated scans.

Version control and build success rate

What it measures: Version control and build success rate calculate the number of builds that succeeded or failed in a given period.?

Why it’s important: A change in a DevOps pipeline often needs to be built multiple times—once for each deployment stage—before arriving in production. A build failure anywhere in this process requires applying a fix, starting the deployment from scratch, increasing lead time, and reducing deployment frequency.

Teams can improve their overall build success rate by giving developers better tools for code linting and local testing, such as the ability to easily set up a dev environment using Infrastructure as Code.

Automation test coverage

What it measures: How much of your codebase is verified using automated tests with every new change checked in.

Why it’s important: Higher test coverage reduces the time developers and Software Reliability Engineers (SREs) spend on manual testing. Additionally, adding tests that check for past failures (regression testing) eliminates failure in production or long lead times caused by known issues.

Application performance and usage metrics

What it measures: How well your application performs against usage goals and operational standards.

Why it’s important: Application performance metrics show whether the changes you shipped are gaining traction with your user base—the ultimate measure of success for a feature launch. Performance metrics also confirm that your changes continue to result in a fast, responsive application that processes user requests promptly.

Tracking DX25 KPIs for developer experience

DevOps metrics aren’t the only way to track the efficiency and effectiveness of your DevOps process. Developer experience measures how developers feel, think about, and value their work.

Having clear project goals and time to concentrate on deep work boosts developer satisfaction, leading to higher developer productivity, better job satisfaction, and higher employee retention. Conversely, frustration with buggy or hard-to-use tools or a lack of voice in product roadmaps can lead to lower overall productivity due to longer lead times, burnout, and developer attrition.

At DX, we created the DX25 to help companies understand the metrics and KPIs they can use to measure, evaluate, and improve developer experience. The DX25 model defines three key KPIs to track:

  • Speed. How quickly can developers ship changes through their DevOps pipeline and deliver them to users?
  • Ease of delivery refers to how easy it is for developers to use the tools provided to ship new features or fixes throughout the entire software development lifecycle.
  • Quality. How robust is the final software product, and is user satisfaction with the changes shipped?

Developer experience (DevEx) metrics complement DORA metrics and other DevOps metrics by providing insights into the human aspects of the software development process. While DORA metrics focus on the efficiency and effectiveness of the DevOps pipeline, DevEx metrics shed light on how developers perceive and value their work. By measuring factors such as developer satisfaction, productivity, and retention, organizations can identify areas for improvement that may not be captured by traditional DevOps metrics.

The DX25 model, which includes speed, ease of delivery, and quality metrics, helps companies understand the broader context of their development process and make data-driven decisions to optimize both the technical and human aspects of their DevOps practices. Combining DevEx metrics with DORA and other DevOps metrics provides a comprehensive view of the software development lifecycle, enabling organizations to create a more efficient, effective, and satisfying development environment.

Tracking the DevOps metrics above gives you a solid baseline for measuring how your DevOps practice is currently performing. With this information, you can identify weak points and make improvements in line with your business objectives. By adding developer experience metrics into the mix, you can capture the voice of developers at your company and directly address their pain points to create a more efficient, reliable, and easier-to-use development process.

May 10, 2024

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