DevOps Metrics & KPIs to Measure Your Team Success

Team leaders know that the DevOps strategy is not a plug-and-play approach that guarantees better and faster deliverables; instead, you must track the metrics to streamline operations and deliver successful products.

Tracking DevOps metrics provides control over the workflow and helps managers run processes and assign tasks efficiently. Measuring performance indicators also helps to understand the impact of individual engineers and interdependent teams within the product development pipeline.

Moreover, this process allows for scaling operations while maintaining optimum customer satisfaction across the board. These DevOps KPIs will also provide a better estimation of the expected ROI on every project so that you know where to cut costs and where to invest more.

Engineering managers can track DevOps KPIs and metrics with advanced tools and automation that help the organization gather crucial information about deployment speed, application performance and other essential project health aspects.

Key DevOps metrics to track

Here are the core DevOps KPI examples to track for your organization:

Deployment speed

This is the time it takes to start a project from scratch and release it. This metric is important because it determines the efficiency of the DevOps methodology.

Deployment success rate

Tracking the deployment success rate can determine how many deployments are successful or result in failure. This means you must first establish criteria for success. For instance, the rollback procedure could be deemed successful even though it means the deployment didn’t go through. Alternatively, the deployment could be deemed unsuccessful since a variable (rollback procedure) is hindering progress.

Cycle time

Cycle time tracks the amount of time taken from pushing a commit to deploying it into production. This metric tells team managers how to improve productivity in the development cycle. By reducing the cycle time for individual projects, managers can align their teams’ goals with the stakeholders' expectations.

Lead time (for changes)

Lead time for changes is a velocity metric, measuring the time that elapses for committing new code (from initiating a commit to getting it into production) when it is already in a deployed state.

This metric relies on automation to determine how long it takes a DevOps team to implement new changes, especially for products that require constant tweaks to meet consumer needs. Long lead times often signify frustrating bottlenecks, while short lead times signify efficient delivery and fast-paced innovation.

Change volume

This tracks the number of changes the codebase undergoes before deployment. By tracking the change volume, you can keep an eye on the overall progress of the development process. High change volumes signify that engineers are making frequent errors — or that the process is still in the initial stages. Subsequently, the change volume should decrease as the release is broken into small sets.

Change failure rate (CFR)

The change failure rate shows the number of changes that failed to meet expectations. This metric is black-and-white because it relies on established outcomes to determine if the process is a success or failure.

To calculate a project’s change failure rate, divide the problematic deployments by the total number of deployments. If the resulting figure is in the 0-15 percentile, the engineers are elite performers.

Defect escape rate

This metric reveals how frequently engineers push buggy code into production. The defect escape rate shows managers the effectiveness of their testing and debugging processes.

Defect volume

Like the defect escape rate, the defect volume focuses on issues resulting from problematic QA processes. However, the defect volume measures the actual number of errors and bugs rather than the frequency at which they occur.

Mean time to recovery (MTTR)

This metric refers to the average duration of any effort to fix an issue during the software development lifecycle. If your mean time to recovery (MTTR) is less than an hour, your team is high performing. 

Mean time to failure (MTTF)

This metric tracks the time it takes from the last instance of the code functioning properly to the first detection of the issue.

Mean time to detection (MTTD)

This metric outlines your DevOps team’s ability to detect problems in the development pipeline. The mean time to detection (MTTD) is similar to the mean time to failure in the way both KPIs focus on problems and errors. But the key difference is that the MTTD highlights the effectiveness of your monitoring mechanisms and testing practices in detecting issues before they cause your product to fail.

Mean time between failure (MTBF)

As the name suggests, this KPI measures the average time that elapses between software failures. In essence, the mean time between failure (MTBF) determines the stability of components in production.

Unplanned work rate (and volume)

This metric tracks all miscellaneous expenses in time and resources outside the project's preliminary budget. Unplanned work can take the form of process optimization and unprecedented changes to the codebase.

Customer ticket volume

This performance indicator highlights the frequency at which customers create tickets to address problems with the product.

Real-life examples of applying DevOps metrics on projects

In my role as a Cloud Engineering Manager, these metrics have been crucial in guiding my teams toward success. For example, on past projects we have used deployment speed and cycle time to identify bottlenecks and streamline our processes. By doing so, we reduced our average deployment time from hours to minutes and significantly enhanced our productivity and alignment with stakeholders' expectations.

I vividly recall a project where we faced high change failure rates. By closely monitoring this metric and analyzing the root causes, we implemented more rigorous testing and improved our CI/CD pipeline. This effort reduced our change failure rate by 50%, which in turn increased our deployment success rate and overall product stability.

In another instance, while leading a team through a critical system upgrade, we focused on metrics such as mean time to detection (MTTD) and mean time to recovery (MTTR). By fostering a culture of continuous monitoring and rapid response, we were able to detect and resolve issues swiftly, maintaining system stability and performance. This proactive approach ensured our team was consistently high performing, achieving an MTTR of less than an hour.

If you’re excited about developing your career in DevOps management, take a look at our open DevOps jobs at EPAM and apply today.