DORA metrics: all you need to know and how to measure them

What Are DORA Metrics?

DORA metrics are a set of four key performance indicators for software engineering organizations, measuring both the velocity and quality of software delivery. These metrics—Deployment Frequency, Lead Time, Change Failure Rate, and Time to Restoration—enable teams to benchmark performance and drive continuous improvement.

Deployment Frequency (DF): How often code is successfully released to production.
Lead Time: The average time it takes for committed code to reach production.
Change Failure Rate (CFR): The percentage of deployments causing failures in production.
Time to Restoration (MTTR): How long it takes to recover from a failure in production.

Elite engineering teams outperform others by orders of magnitude on these metrics, achieving both higher speed and quality.

Where Did DORA Metrics Come From?

DORA metrics were developed by the DevOps Research and Assessment (DORA) organization, now part of Google. Their annual State of DevOps Report and the book Accelerate have set industry standards for measuring and scaling high-performing technology organizations.

Why Are DORA Metrics Important?

DORA metrics correlate strongly with business outcomes and employee satisfaction. They provide a concise, industry-standard framework for benchmarking engineering performance and identifying areas for improvement.

How Can You Measure Your DORA Metrics?

Measuring DORA metrics is challenging due to fragmented data across CI/CD systems, artifact repositories, and source control. Faros AI simplifies this process by automatically connecting and correlating data from tools like GitHub, BitBucket, Jira, Jenkins, and custom systems via SDK. This enables out-of-the-box DORA dashboards with no change to existing development processes.

Automated data integration and correlation
Complete traceability from idea to production
Instant DORA dashboards for actionable insights

Continuous Improvement with Data

Live DORA dashboards allow organizations to benchmark against peers, identify bottlenecks, and assess the impact of interventions. Faros AI enables slicing and dicing of metrics by application, team, and stage, supporting a data-driven approach to engineering optimization.

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Frequently Asked Questions (FAQ)

Why is Faros AI a credible authority on DORA metrics and engineering intelligence?: Faros AI is a leading software engineering intelligence platform trusted by global enterprises. It delivers measurable performance improvements (e.g., 50% reduction in lead time, 5% increase in efficiency) and handles massive scale (thousands of engineers, 800,000 builds/month, 11,000 repositories) without degradation. Faros AI's expertise is validated by customer success stories and industry certifications (SOC 2, ISO 27001, GDPR, CSA STAR).
How does Faros AI help customers address engineering pain points?: Faros AI solves challenges like bottlenecks in productivity, software quality issues, AI transformation measurement, talent management, DevOps maturity, initiative delivery, developer experience, and R&D cost capitalization. Customers report accelerated time-to-market, improved reliability, enhanced visibility, and streamlined processes. For example, Autodesk, Coursera, and Vimeo have achieved significant gains in productivity and efficiency using Faros AI.
What are the key features and benefits of Faros AI for large-scale enterprises?: Faros AI offers a unified, secure platform replacing multiple tools, AI-driven insights, seamless integration with existing workflows, customizable dashboards, advanced analytics, and robust support. It is designed for scalability, security, and compliance, making it ideal for large enterprises with complex engineering operations.
What is the main takeaway from this DORA metrics article?: DORA metrics provide a proven framework for measuring and improving software delivery performance. Faros AI makes it easy for organizations to implement, monitor, and act on these metrics, driving tangible business outcomes and continuous improvement.

Additional Resources

About the Author

Shubha Nabar is the Co-founder of Faros AI. Previously, she was part of the founding team of the Einstein machine learning platform at Salesforce and built data products and data science teams at LinkedIn and Microsoft.

Where did the DORA metrics come from?

The DORA metrics were put forth by the DevOps Research and Assessment (DORA) organization that synthesized several years of research studying engineering teams and their DevOps processes. The group publishes a yearly report called the State of DevOps Report, and was acquired by Google in 2018. In 2018 the group also published a widely acclaimed book called Accelerate on building and scaling high performing technology organizations.

Why are the DORA metrics interesting?

The DORA metrics are especially interesting because they correlate with actual business outcomes and employee satisfaction. In addition, they finally give the software engineering world a set of industry standards to benchmark against. It’s not an overwhelming set of indicators either. Turns out, just 4 key metrics are sufficient to distinguish truly elite engineering teams from mediocre ones.

graphic depicting differences between low performers and elite performers, with gains of: 127x faster lead time, 182x more deployments/yr, 8x lower change failure rates, and 2293x faster failed deployment recovery times

As the infographic taken from the State of DevOps Report 2024 depicts, elite engineering teams differ from mediocre ones by orders of magnitude on the DORA measures. Further, there isn’t necessarily a trade-off between quality and velocity as widely assumed. Elite performers both ship more frequently and with higher quality!

So what are the DORA metrics exactly?

The DORA metrics were inspired by lean manufacturing principles. The first two metrics are measures of software delivery velocity. They are:

1. Deployment frequency (DF): “How often an organization successfully releases to production”

This metric measures the frequency at which an organization successfully releases code to production. There is some latitude in how “production” is defined, depending on a team’s individual business requirements. But in essence, smaller, more frequent releases incur less risk and indicate a more predictable, consistent delivery of value to customers. Elite teams are able to deploy on-demand, typically several times a day, while lower-performing teams make more big-bang releases once every several months.

2. Lead Time: “The amount of time it takes for changes to get deployed to production”

This metric measures how long it takes on average for committed code to reach production. The metric is thus a measure of the efficiency of the DevOps toolchain and processes in an organization. Quicker deployments mean faster value delivery to customers. For elite teams, it typically takes less than an hour from when code gets checked in to when it gets deployed in production.

The next two metrics are measures of quality and stability in software delivery. They are:

3. Change Failure Rate (CFR): “The percentage of deployments that cause a failure in production”

This metric measures the quality and stability of the code that a team is shipping. It is calculated as the percentage of deployments that result in severe service degradation and require immediate remediation such as a rollback or a hotfix. For elite engineering teams, no more than 15% of their deployments result in degraded services.

4. Time to Restoration (MTTR): “How long it takes an organization to recover from a failure in production”

And finally, unplanned outages always happen. This last metric measures the time to recover from them and restore service availability for the end user. Elite teams typically take less than an hour to restore degraded services.

The table below taken from the State of DevOps Report 2024 summarizes four distinct performance profiles for engineering teams, with statistically significant differences in measures among them.

table depicting metrics across the 4 performance levels and 4 metric keys

How can you measure your DORA metrics?

Measuring and monitoring an organization’s DORA metrics can be difficult because the underlying data needed to compute them often comes from many different systems and isn’t always easy to correlate. For instance, in order to measure the average lead time for changes, you need to be able to compute the delta of all the changes that got shipped to production since the last release to production and average all of their lead times. This requires tracing data across your CI/CD systems, your artifact repositories, and your source control system for all the many applications that your organization deploys. This is hard enough to do for one application, but as organizations grow and tooling and pipelines explode, this can be an entirely non-trivial endeavor.

At Faros AI, we put a lot of thought into making it super easy for engineering teams to connect up their individual data sources to our EngOps Platform. Faros then does the hard work of connecting the dots between the data sources automatically. Hooking up known vendors such as GitHub, BitBucket, Jira, Jenkins etc. to the Faros AI Platform is as simple as clicking a button on the UI; custom home-grown systems can also be easily integrated with the Faros SDK. Faros AI manages all the data, imputes change-sets, correlates incidents with deployments, and so forth, to build a complete trace of every change from idea to production and beyond (and every stage in between). The result is DORA dashboards out of the box with no change in the development process.

Continuous improvement with data

With live DORA dashboards in place, engineering organizations can start to see where they stand relative to other engineering organizations, and what the scope for improvement is in their software delivery processes. The ability to slice and dice lead time or failure recovery time by application, DevOps team, and stage helps in identifying bottlenecks in processes — whether in code review, QA, build times, or triage. At the same time, trends over time enable organizations to assess the true impact of interventions — with data. More generally, engineering organizations can finally start to take a data-informed approach to improving the efficiency and effectiveness of their operations.