DevOps Metrics 2025: The Complete Guide to Successfully Measuring Dev Operations

Summary

“DevOps metrics measure the efficiency and effectiveness of development and operations teams, helping organizations optimize software delivery.”

Jonathan Singer Senior Product Marketing Manager

DevOps metrics measure the effectiveness of development and operations teams.

They are used to detect bottlenecks, track efficiency and drive continuous improvement.

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By measuring devops metrics and optimized operations according to the results, enterprises can deliver high-quality software faster and more reliably. In this article, we dive into the top DevOps metrics in use, DORA metrics, tools for implementation, how ASPM helps improve DevOps metrics, and more.

What are DevOps Metrics?

DevOps metrics are KPIs that measure DevOps performance within the organization. They provide quantitative data that helps teams monitor operations, identify bottlenecks and continuously improve their processes. This is done to achieve faster and more reliable software delivery that meets SLAs, customer expectations and business goals. Essentially, they provide a solution to the question of “How to measure DevOps?”.

Examples of the top DevOps metrics include:

Deployment Speed Metrics

Deployment Frequency – How often new code is deployed to production.
Cycle Time – The time it takes for a task to move through the entire development process.
Change Lead Time – The time it takes for a code change to go from commit to production.

Code Quality Metrics

Change Failure Rate – The percentage of deployments that result in a failure, such as a bug or outage.
Mean Time to Recovery (MTTR) – The average time it takes to recover from a failure in production.
Test Coverage – The percentage of code covered by tests.

Operational Quality Metrics

System Availability (Uptime) – The percentage of time a system is available and operational.
Incident Volume – The number of incidents occurring over a specific period.

Performance and Load Metrics

Throughput – The volume of work completed in a given timeframe (e.g., features or fixes).
Latency – The time it takes for a system to respond to a request.
Error Rate – The percentage of failed requests out of total requests.
Response Time – The total time taken by a system to process a request.

What is DORA?

The DORA (DevOps Research and Assessment) organization was founded to conduct research on DevOps best practices and high-performance IT teams. One of its most popular outputs is a framework used to measure software delivery and DevOps performance.

The DORA framework covers four key DevOps metrics: Deployment Frequency, Lead Time for Changes, Change Failure Rate and Time to Restore Service. These metrics help DevOps teams achieve faster deployments, lower failure rates and quicker recovery times, leading to more efficient software delivery.

In 2018, Google acquired DORA, integrating its research into Google Cloud.

What are DORA Metrics?

The four DORA metrics are:

Deployment Frequency – How often an organization releases to production. Frequent deployments indicate a team’s agility and ability to deliver updates quickly.
- High-performing teams deploy multiple times per day.
- Lower-performing teams may deploy monthly or less.
Lead Time for Changes – Tracks the time it takes for a code change to go from being committed to being deployed in production. A shorter lead time reflects streamlined processes and reduced bottlenecks.
- High-performing teams have a lead time measured in hours.
- Lower-performing teams may take weeks or more.
Mean Time to Recovery (MTTR) – Measures how quickly a team can recover from a failure or an incident in production. Fast recovery minimizes downtime and its impact on users, ensuring reliability.
- High-performing teams aim to recover within minutes or hours.
- Low-performing teams may take days.
Change Failure Rate – The percentage of changes that lead to a failure in production (e.g., bugs, outages). Lower failure rates indicate better testing, monitoring, and deployment practices.

Why You Need DevOps and DORA Metrics

DevOps metrics, and DORA as a DevOps metrics framework, drive efficiency, reliability and continuous improvement. By measuring these metrics and optimizing operations according to them over time, teams can enjoy the following benefits:

Higher quality code and faster issue resolution
Better agility – faster code updates, accelerated bottleneck fixes and new features shipped to customers much quicker.
System and infrastructure stability
Continuous improvement and enhancement of processes and workflows
Better communication between teams and less silos and finger-pointing
Alignment of technical operations with business objectives
Faster innovation leading to a stronger competitive edge

Tools for Measuring DevOps and DORA Metrics

Measuring DevOps and DORA metrics requires a combination of tools for data collection, visualization and analysis. Here are the types of tools commonly used to track and improve these metrics:

CI/CD Platforms – Provide data on deployment metrics: how often new code is deployed and the time it takes to do so.
Monitoring and Observability – Monitor application performance and can help calculate MTTR and change failure rates.
Version Control and Issue Tracking – Measure delivery cycles and lead time.
Incident Management Tools – Track recovery times and post-incident analytics.
Custom Dashboards – All. They import data from CI/CD pipelines, monitoring tools and incident systems for advanced visualization.

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DevOps and DORA Metrics Implementation Challenges

Implementing DevOps and DORA metrics can significantly enhance an organization’s software delivery performance. However, this doesn’t come without challenges. Here’s an overview of common challenges and how to address them:

Implementation Challenge	Description	Solution
Cultural Resistance	Fear of change, lack of understanding, or perceived threats to job security	Encouraging shared membership and emphasizing the benefits of delivery speed, reliability and reduced burnout
Tooling and Integration	Legacy environments make integrations complicated and buggy	Start with tools that integrate well with your existing stack (e.g., Git, Jenkins, Jira) and automate as many processes as possible
Data Accuracy and Availability	Inconsistent workflows, incomplete automation, or poor data hygiene impact data quality	Define clear workflows and standard operating procedures while regularly auditing and validating data sources
Misaligned Goals	Focus on vanity metrics or metrics that do not align with business objectives	Tie DORA metrics to broader business goals (e.g., customer satisfaction, time-to-market)
Lack of Leadership Buy-in	Failure to gain traction	Engage leadership in defining success criteria and metrics and keep leaders updated on progress and challenges
Siloed Teams	Hindered collaboration between development, operation and QA	Foster cross-functional teams where developers, operations and testers work together. Use collaborative platforms and dashboards.

DevOps and DORA Metrics Best Practices

DevOps metrics help streamlining software development and operations processes to improve speed, quality and reliability. Here are best practices for implementing them effectively:

Automate metrics tracking, collection and visualization. This will reduce friction and help focus on the productivity impact.
Choose DevOps tools that integrate seamlessly with your existing stack. This too will reduce friction and ensure accuracy.
Set clear benchmarks for performance and compare against industry standards. This will ensure you are on a path of continuous and relevant improvement. It also helps with reporting to management and getting buy-in.
Review metrics regularly in retrospectives to identify areas for improvement.
Visualize results to easily identify areas for improvement and progress.

How to Improve your DevOps Metrics with AppSec Security/ASPM

Application security and ASPM integrated into DevOps workflows can help you improve your DevOps metrics.

By implementing security throughout the software delivery lifecycle, teams can produce higher-quality code that meets SLAs and customer expectations—all with minimal friction.

To achieve this, implement the following security measures:

Integrate automate Static (SAST) and Dynamic (DAST) security testing into CI/CD pipelines to identify vulnerabilities early.
Leverage ASPM for visibility and gain real-time insights into application security posture across development and production.
Use developer-friendly AppSec tools to ensure security measures align with developer workflows. This will reduce friction and improve adoption.

Implementing these practices leads to measurable improvements in DevOps performance:

Lead Time for Changes – Faster vulnerability detection reduces remediation time, speeding up deployments.
Change Failure Rate & MTTR – Quick feedback loops help address security issues before they escalate, reducing failure rates and mean time to recovery.
Uptime & System Reliability – Secure code results in fewer security incidents, minimizing disruptions and outages.

Learn more about Checkmarx’s ASPM solution. Get a demo here.