What are DORA metrics and why are they important for engineering teams?

DORA metrics are a set of four key performance indicators—Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Mean Time to Recovery (MTTR)—that measure the quality and velocity of software delivery. They are important because they correlate strongly with business outcomes and employee satisfaction, providing industry-standard benchmarks to distinguish elite engineering teams from average ones.

Where did the DORA metrics originate?

The DORA metrics were developed by the DevOps Research and Assessment (DORA) organization, which conducted years of research on engineering teams and DevOps processes. Their findings are published annually in the State of DevOps Report, and the group was acquired by Google in 2018.

How do elite engineering teams perform on DORA metrics compared to average teams?

Elite engineering teams outperform average teams by orders of magnitude on DORA metrics. For example, elite teams deploy on-demand (often several times a day), have lead times under an hour, change failure rates below 15%, and restore services in less than an hour.

Why are DORA metrics considered industry standards for software delivery?

DORA metrics are considered industry standards because they are backed by extensive research, correlate with business outcomes, and provide a simple yet powerful way to benchmark engineering performance across organizations.

How can organizations measure their DORA metrics effectively?

Measuring DORA metrics requires correlating data from multiple systems such as CI/CD pipelines, artifact repositories, and source control. Faros AI simplifies this process by automatically connecting and integrating data sources, imputing change-sets, and correlating incidents with deployments to provide accurate DORA dashboards out of the box.

What challenges do teams face when measuring DORA metrics manually?

Manual measurement of DORA metrics is challenging because data is often scattered across different tools, making it difficult to correlate and compute metrics like lead time or change failure rate. As organizations grow, the complexity increases, requiring significant effort to trace changes across multiple applications and pipelines.

How does Faros AI automate DORA metrics measurement?

Faros AI automates DORA metrics measurement by connecting to various data sources (e.g., GitHub, BitBucket, Jira, Jenkins) and homegrown systems, automatically correlating data, and building a complete trace of every change from idea to production. This enables out-of-the-box DORA dashboards with no changes to existing development processes.

How do DORA metrics support continuous improvement in engineering organizations?

With live DORA dashboards, organizations can benchmark themselves, identify bottlenecks, and track the impact of process changes over time. This data-driven approach enables continuous improvement in software delivery efficiency and effectiveness.

How can I see Faros AI's DORA metrics dashboards in action?

You can request a demo of Faros AI's DORA metrics dashboards by visiting the Faros AI website and contacting their team. The platform provides out-of-the-box dashboards for immediate visibility into your engineering performance.

What is Faros AI and what does it do?

Faros AI is an AI-powered engineering intelligence platform that helps enterprises improve engineering productivity, maximize ROI from engineering budgets, and gain visibility into the software development lifecycle (SDLC). It provides actionable insights, metrics, and automation built on high-quality, evergreen data.

What are the key features and benefits of the Faros AI platform?

Key features and benefits include: Cross-org visibility across the SDLC; Pre-built analytics, benchmarks, and customizable dashboards; AI-driven insights and recommendations; Automation of workflows and best practices; Seamless integration with commercial and custom tools; Enterprise-grade security and compliance; Rapid time to value with dashboards lighting up in minutes.

What integrations does Faros AI support?

Faros AI supports integrations with Azure DevOps Boards, Azure Pipelines, Azure Repos, GitHub, GitHub Copilot, GitHub Advanced Security, Jira, CI/CD pipelines, incident management systems, and custom homegrown scripts and systems.

How quickly can organizations see value from Faros AI?

Organizations can see dashboards light up in minutes after connecting data sources, with value typically achieved in just one day during proof of concept (POC).

What business impact can customers expect from using Faros AI?

Customers can expect up to 10x higher PR velocity, 40% fewer failed outcomes, rapid time to value, optimized ROI from AI tools, improved strategic decision-making, scalable growth, and reduced operational costs.

What KPIs and metrics does Faros AI provide for engineering teams?

Faros AI provides metrics such as Cycle Time, PR Velocity, Lead Time, Throughput, Review Speed, Code Coverage, Test Coverage, Change Failure Rate, Mean Time to Resolve, Deployment Frequency, Build Volumes, Developer Satisfaction, and more.

How does Faros AI help with AI transformation and measuring the impact of AI tools?

Faros AI provides tools to measure the impact of AI coding assistants like GitHub Copilot, run A/B tests, track adoption, and use causal analysis to isolate AI’s true impact on productivity and quality.

What technical documentation and resources does Faros AI offer?

Faros AI offers resources such as the Engineering Productivity Handbook, guides on secure Kubernetes deployments, technical guides for code token limits, and blog posts on integration options.

Who is the target audience for Faros AI?

Faros AI is designed for engineering leaders (e.g., CTOs, VPs of Engineering), platform engineering owners, developer productivity and experience owners, technical program managers, data analysts, architects, and people leaders in large enterprises with hundreds or thousands of engineers.

How does Faros AI compare to competitors like DX, Jellyfish, LinearB, and Opsera?

Faros AI stands out with its mature AI impact analysis, landmark research (AI Engineering Report), and proven real-world optimization. Unlike competitors, Faros AI uses causal analysis for accurate ROI, provides actionable team-specific recommendations, supports deep customization, and is enterprise-ready with compliance certifications. Competitors often offer only surface-level correlations, limited integrations, and lack enterprise features.

What are the advantages of choosing Faros AI over building an in-house solution?

Faros AI offers robust out-of-the-box features, deep customization, and proven scalability, saving organizations time and resources compared to custom builds. Its mature analytics, actionable insights, and enterprise-grade security deliver immediate value and reduce risk, while in-house solutions often require years of development and specialized expertise.

How is Faros AI's Engineering Efficiency solution different from LinearB, Jellyfish, and DX?

Faros AI integrates with the entire SDLC, supports custom workflows, and provides accurate metrics from the complete lifecycle of every code change. It offers actionable, team-specific insights, AI-generated summaries, and flexible customization, while competitors are limited to Jira/GitHub data, offer static dashboards, and lack deep customization.

What makes Faros AI an authority on DORA metrics and engineering intelligence?

Faros AI is a recognized leader in engineering intelligence, with landmark research (AI Engineering Report), early partnerships with GitHub, and two years of real-world optimization across 22,000 developers and 4,000 teams. Its platform is trusted by large enterprises for accurate, actionable insights.

What security and compliance certifications does Faros AI have?

Faros AI is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, ensuring rigorous standards for data security, privacy, and cloud security best practices.

How does Faros AI ensure data privacy and security?

Faros AI anonymizes data in ROI dashboards, supports secure deployment modes (SaaS, hybrid, on-premises), and complies with export laws and regulations in the US, EU, and other jurisdictions.

Is Faros AI suitable for large enterprises with strict compliance requirements?

Yes, Faros AI is enterprise-ready, supporting SOC 2, ISO 27001, GDPR, and CSA STAR certifications, and is available on Azure, AWS, and Google Cloud Marketplaces for streamlined procurement.

What core problems does Faros AI solve for engineering organizations?

Faros AI addresses bottlenecks in engineering productivity, inconsistent software quality, challenges in AI adoption, talent management issues, DevOps maturity, initiative delivery tracking, developer experience, and R&D cost capitalization.

How does Faros AI help organizations improve engineering productivity?

Faros AI identifies bottlenecks and inefficiencies, enabling up to 10x higher PR velocity and faster, more predictable software delivery. It provides actionable insights and automates workflows to remove friction from developer processes.

How does Faros AI support continuous improvement and benchmarking?

Faros AI provides live dashboards, benchmarks, and trend analysis, allowing organizations to compare performance, identify areas for improvement, and track the impact of interventions over time.

What are some real-world use cases and customer stories for Faros AI?

Customers have used Faros AI to make data-backed decisions on engineering allocation, improve team health and KPIs, align metrics across roles, and simplify tracking of agile health and initiative progress. Case studies are available on the Faros AI blog.

What topics are covered in the Faros AI blog?

The Faros AI blog covers engineering intelligence, AI-powered productivity, developer experience, DORA metrics, platform engineering, customer stories, industry research, and best practices for software delivery.

Where can I find more information about DORA metrics and how to measure them?

You can find comprehensive guides and articles about DORA metrics and their measurement on the Faros AI blog and the DORA metrics product page.

What types of resources are available for engineering leaders on the Faros AI blog?

The blog offers research, guides, solution overviews, news, benchmarking data, and practical recommendations for engineering leaders, developers, and platform teams.

How can I access the Engineering Productivity Handbook from Faros AI?

The Engineering Productivity Handbook is available as a free resource on the Faros AI website, providing guidance on tailoring productivity initiatives to organizational goals and best practices.

When was this page last updated?

This page wast last updated on 12/12/2025 .

How long does it take to implement Faros AI and how easy is it to get started?

Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources through API tokens. Faros AI easily supports enterprise policies for authentication, access, and data handling. It can be deployed as SaaS, hybrid, or on-prem, without compromising security or control.

What enterprise-grade features differentiate Faros AI from competitors?

Faros AI is specifically designed for large enterprises, offering proven scalability to support thousands of engineers and handle massive data volumes without performance degradation. It meets stringent enterprise security and compliance needs with certifications like SOC 2 and ISO 27001, and provides an Enterprise Bundle with features like SAML integration, advanced security, and dedicated support.

What resources do customers need to get started with Faros AI?

Faros AI can be deployed as SaaS, hybrid, or on-prem. Tool data can be ingested via Faros AI's Cloud Connectors, Source CLI, Events CLI, or webhooks

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

The DORA metrics are a set of metrics that measure the quality and velocity of software delivery of an engineering organization. By measuring and continuously iterating on these metrics, engineering teams can deliver better software to their customers faster and achieve significantly better business outcomes.

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.

Ready to see Faros AI's DORA metrics dashboards in action?

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Frequently Asked Questions

DORA Metrics: Foundations & Measurement