Summary
“DevOps metrics provide quantitative insight into the efficiency and reliability of software delivery processes. But to use DevOps metrics effectively, each must decide which specific metrics to track, how to collect them, and how to interpret them based on its unique priorities. ”
DevOps aims to increase the speed, efficiency, and reliability of software development processes. But how can DevOps teams actually determine whether they’re succeeding? How can they measure the effectiveness of their software delivery practices over time?
The answer is DevOps metrics. DevOps metrics are quantitative data points that teams can use to measure the effectiveness of the processes that take place within the software development lifecycle (SDLC). The goal of DevOps metrics is to provide a consistent, quantitative means of tracking how much value DevOps is creating for an organization.
Key DevOps metrics for 2025
The most common approach to DevOps metrics focuses on four key performance indicators (KPIs):
- Deployment frequency: Tracks how often a team releases software updates, which is useful for measuring overall software development velocity.
- Lead time for changes Measures the time between an initial code commit and production deployment of that code. This metric helps teams track how efficiently they move code down the software delivery pipeline.
- Change failure rate: Evaluates how frequently code changes trigger problems in a production environment, such as performance issues or security vulnerabilities. Higher change failure rates are a sign that DevOps processes are unreliable or prone to errors.
- Mean time to recovery (MTTR): Tracks how long it takes to restore an application or system to normal functionality following an outage. MTTR reflects how efficiently DevOps teams work efficiently, especially in the context of incident response.
These four KPIs are sometimes called DORA metrics because they were popularized by the DevOps Research and Assessment Group, or DORA, a team inside Google Cloud that develops thought leadership related to DevOps. The DORA metrics are the most widely used framework for measuring DevOps.
That said, additional metrics and KPIs exist that can also help to monitor the efficiency and effectiveness of DevOps practices, such as:
- Defect escape rate: Measures how many bugs or other defects make it into a production environment due to the failure of the DevOps team to catch the issues during pre-deployment testing. Defect escape rate reflects the effectiveness of DevOps testing processes.
- Code coverage: The percentage of an application’s codebase that is subject to regular performance and/or security tests. Code coverage is another KPI for tracking software testing effectiveness.
- Software availability: The overall percentage of time that an application or system is available to end-users. Availability problems do not necessarily stem from issues with DevOps processes (they could also be caused by problems like unreliable infrastructure), but they can result from challenges like ineffective testing practices that fail to detect critical bugs, leading to application outages.
- Mean Time to Detect (MTTD): Tracks how long it takes teams to identify an issue (as opposed to how long it takes to recover or resolve, which is measured by MTTR). MTTD reflects the effectiveness of software monitoring operations.
Organizations need not limit themselves to the DevOps metrics described above. Any KPI that provides insight into a business’s software delivery practices can serve as a DevOps metric, so long as the organization can track and evaluate it effectively.
How to collect DevOps metrics
Once you’ve decided which DevOps metrics your team will measure, you have to begin the process of actually collecting the metrics data.
To do this, you’ll first need to decide exactly which conditions to track for each metric. For instance, to measure MTTR, you need to define what qualifies as an outage and when to consider an outage resolved. Likewise, to measure defect escape rate, you have to determine what counts as a defect. Establishing these criteria is somewhat subjective because different teams will have different perspectives on questions like how significant a problem has to be to qualify as an outage or defect.
After deciding precisely how to define the DevOps metrics you want to track, the next step in collecting them is to deploy automations that can systematically report metrics data over time. This can be challenging because most core DevOps tools (such as Continuous Integration servers and release automation software) don’t have native features for DevOps metrics tracking or reporting. However, a limited selection of third-party tools are available for this purpose – such as Sleuth and a GitHub Action for tracking DORA metrics.
If you can’t find a DevOps metric tracking tool that fits your needs, you can likely implement your own by writing scripts that pull data from sources like Continuous Integration server logs, and then correlate and format it in ways that align with the metrics you want to track.
How to measure DevOps success using metrics
Collecting DevOps metrics is only worthwhile if you also measure and evaluate them. There are two ways of going about this.
Tracking your DevOps success over time
First, you can monitor your team’s DevOps metrics over time to determine where you’re improving and where you might be losing ground.
For instance, you might find that your deployment frequency is increasing, which is generally good because it means your team is able to push new features into production faster. But if your change failure rate is also increasing (meaning you’re experiencing higher rates of problems in production), it could be a sign that higher release velocity comes at the expense of lower code quality. In this case, you might want to strengthen your test automation processes so that you can catch problems more reliably, without slowing down your software delivery pipeline.
Comparing your team to others
You can also use DevOps metrics to compare how your organization’s software delivery processes compare to those of other companies based on data published in sources like Google’s State of DevOps report.
In general, it’s a best practice not to put too much stock in these comparisons because every organization’s software delivery processes and needs are unique. If other companies seem to be doing better than yours at DevOps, it could be because you face special challenges (like having to work with legacy codebases, for instance) that they don’t.
Still, comparing your DevOps metrics to those of other organizations can be helpful for establishing a general baseline of where your organization stands. DORA groups organizations into three classes – high, medium, and low – based on their DevOps performance, and it’s useful to know where you stand. (Historically, DORA also defined an “elite” performer class, but it no longer tracks that category.)
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