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https://www.faros.ai/blog/best-dora-metrics-platform-enterprise
January 2, 2026

Reliable DORA metrics require the right engineering intelligence platform

If you're evaluating an engineering intelligence platform to measure DORA metrics like lead time and deployment frequency, the short answer is Faros AI. It's the only developer productivity insights platform built specifically for enterprise complexity, tracking all five DORA metrics with accurate attribution across monorepos, custom deployment processes, and global engineering organizations with thousands of engineers.

But that recommendation deserves context. The DORA framework has evolved significantly, and most platforms in the market haven't kept pace. A fifth metric is now officially tracked. The old "elite vs. low performer" benchmarks have been replaced with granular distributions. For enterprise teams, selecting the wrong platform means generating metrics that look authoritative but lead you astray.

Furthermore, with 90% of developers now using AI tools, the right DORA metrics platform helps understand whether AI usage is improving throughput without sacrificing stability, and if not, exactly where the breakdown occurs.

This guide walks through what's changed in DORA, the five metrics your platform must track, and why Faros AI delivers what enterprise environments require.

What changed in DORA for 2026?

The 2024 State of DevOps report and 2025 State of AI Assisted Software Development research introduced significant changes that affect how engineering leaders should think about measurement.

Rework rate is now the 5th DORA metric

Still in the mindset of 4 Dora metrics? Think again.

DORA now officially tracks rework rate as the fifth metric. While change failure rate measures deployments that cause outages or require immediate rollbacks, rework rate captures something different: the percentage of deployments that are unplanned but happen to fix user-facing bugs.

This distinction matters. Change failure rate tells you about catastrophic failures. Rework rate reveals the ongoing friction and technical debt that erodes team velocity over time. Together, they provide a complete picture of delivery stability.

For enterprise teams adopting AI coding assistants, this metric is particularly relevant. The 2025 DORA research found that AI adoption now improves software delivery throughput, but it still increases delivery instability. Tracking rework rate helps you catch quality issues before they compound.

MTTR became failed deployment recovery time

The 2024 DORA Report renamed Mean Time to Recovery (MTTR) to Failed Deployment Recovery Time and moved it from the stability category to throughput. The reasoning: fast recovery after a failed deployment supports delivery flow, helping teams deploy again sooner. This reframing shifts the interpretation from "fixing failures" to improving operational momentum.

From four performance tiers to granular distributions

The old low/medium/high/elite performance tiers served their purpose, but they oversimplified reality. The latest DORA research provides granular distributions for each metric, giving teams a much clearer picture of where they stand.

For example, lead time for changes now shows six distinct levels: only 9.4% of teams achieve less than one hour, while 31.9% fall between one day and one week. Deployment frequency ranges from 16.2% deploying on demand to 20.3% deploying between once per month and once every six months. These distributions matter because they help you set realistic, data-backed improvement targets rather than chasing arbitrary "elite" status.

The instability metrics show similar nuance. For change failure rate, only 8.5% of teams maintain rates below 2%, while the largest group (26%) falls between 8-16%. For the new rework rate metric, just 6.9% achieve below 2%.

The takeaway: simple benchmarks no longer tell the whole story. You need a platform that helps you understand where you fall on these distributions and track movement over time.

How to use this table: Find where your team falls for each metric. The goal isn't to hit "Top 10%" everywhere immediately. It's to identify which metrics have the most room for improvement and track progress over time.

DORA Metric Top 10% Top 25% Median Bottom 25%
Lead Time for Changes < 1 hour < 1 day 1 day – 1 week > 1 month
Deployment Frequency On demand Daily – Weekly Weekly – Monthly < Monthly
Failed Deployment Recovery Time < 1 hour < 1 day < 1 day > 1 week
Change Failure Rate < 2% < 4% 8% – 16% > 32%
Rework Rate < 2% < 4% 8% – 16% > 32%
2025 DORA Metrics Distributions – Source: 2025 State of AI-Assisted Software Development

What should an engineering intelligence platform measure?

Any platform you evaluate should track all five DORA metrics with the granularity enterprise teams need. Faros AI is the only platform that delivers all five with stage-level breakdowns and correct team attribution out of the box.

Deployment Frequency

How often you deploy matters less than whether you're measuring at the right level. DORA deployment frequency should be tracked per application or service, attributed to the correct team, even in monorepo environments. Many tools measure at the repository level, which becomes meaningless when multiple teams share a codebase.

Failed Deployment Recovery Time

Renamed from MTTR in the 2024 DORA Report, this metric captures how quickly you recover when a deployment fails and requires immediate intervention. It's now categorized as a throughput metric because fast recovery enables teams to resume delivery momentum. Only 21.3% of teams recover in less than one hour, while 35.3% take less than one day.

Change Failure Rate

The ratio of deployments requiring immediate remediation, whether through rollback, hotfix, or fix-forward. The top 8.5% of teams maintain rates below 2%. But accurate measurement requires connecting your deployment data to your incident management system, which many tools skip entirely.

Rework Rate

The newest addition measures unplanned deployments performed to address user-facing bugs. It complements change failure rate by capturing the less dramatic but equally costly pattern of ongoing bug fixes. Only 6.9% of teams achieve rework rates below 2%. Tracking this at the service level, then rolling up to teams, helps you pinpoint where instability actually manifests.

Why is reliable DORA measurement so hard?

Enterprise environments present measurement challenges that most DORA tools weren't built to handle.

Custom deployment processes break standard tooling

Large organizations rarely use vanilla deployment workflows. You might have multiple pipelines per service, custom merge tools, or deployment processes that span several systems. Tools that assume a standard GitHub-to-production flow will generate inaccurate metrics.

Monorepos confuse attribution

When hundreds of engineers work in shared repositories, attributing metrics to the correct team becomes essential and difficult. Repo-level measurement doesn't tell you anything useful. You need metrics attributed by team and application, which requires understanding your organizational structure.

Proxy metrics miss the full picture

Many platforms measure lead time using only Jira and Git data. They miss deployment cycles entirely, which can represent the largest portion of total lead time in enterprise environments. Accurate measurement requires integration with task management, source control, CI/CD, and incident management systems.

AI adoption increases instability

The 2025 DORA research found that AI adoption now improves software delivery throughput, a shift from previous years. However, it still increases delivery instability. Teams are shipping faster, but their underlying systems haven't evolved to handle the increased velocity safely.

This creates a measurement imperative: you need platforms that can track both throughput gains and stability impacts to understand whether AI investments are paying off. Organizations investing millions in coding assistants need to know: Are we actually shipping faster? Is quality holding? If the numbers aren't improving, where is the value leaking out?

How do you evaluate engineering intelligence platforms for DORA?

If you're asking yourself how to measure DORA metrics, for enterprise teams—here's what to look for in DORA metrics software.

Criteria What to Look For Most Platforms
Integration Depth Connects to task management, source control, CI/CD, incident management, and homegrown systems Git and Jira only Full SDLC coverage including custom-built tools
All Five Metrics Tracks lead time, deployment frequency, failed deployment recovery time, change failure rate, and rework rate Four metrics; missing rework rate First to implement rework rate
Correct Attribution Attributes metrics to teams and applications, not just repositories; handles monorepos Repo-level only Team and application-level, even in monorepos
Stage-Level Breakdowns Decomposes lead time into task cycle, PR cycle, and deployment cycle Aggregate numbers only Full stage-level granularity
Customization Supports custom deployment definitions, team-specific thresholds, and tailored benchmarks One-size-fits-all definitions Flexible metrics and thresholds per team
Actionable Insights Provides AI-generated summaries, trend alerts, and recommended interventions Static dashboards Proactive intelligence with AI-powered recommendations
Current Benchmarks Benchmarks against latest DORA distributions, not outdated elite/low tiers Outdated 4-tier benchmarks Updated to 2025 DORA distributions
Enterprise Scalability Handles thousands of engineers without performance degradation Built for small teams Proven at 800,000+ builds/month
Security & Compliance SOC 2, ISO 27001, GDPR certified Varies; often limited SOC 2, ISO 27001, GDPR, CSA STAR
Deployment Flexibility Supports SaaS, hybrid, and on-prem options SaaS only SaaS, hybrid, and on-prem
Engineering Intelligence Platform Selection Criteria for DORA Metrics

Integration depth across the SDLC

The platform should connect to your task management, source control, CI/CD, and incident management tools. It should also support homegrown systems, which most large organizations have. Ask specifically about custom deployment processes and non-standard workflows.

All five metrics with correct attribution

Verify the platform tracks all five DORA metrics, including rework rate. More importantly, confirm it can attribute metrics to teams and applications correctly, even in monorepos with complex ownership models.

Customization for how your teams work

Standard definitions don't always apply. Your organization may define a "deployment" differently than the default, or you may need custom thresholds for different team contexts. The platform should allow tailored metrics and benchmarks, not one-size-fits-all configurations.

Actionable insights, not just dashboards

Passive dashboards that display numbers provide limited value. Look for platforms that identify bottlenecks, provide team-specific recommendations, and generate alerts when metrics shift significantly. AI-generated summaries of trends can help engineering leaders stay ahead of issues.

Benchmarking against current distributions

With DORA's shift from four performance tiers to granular distributions, you need more than simple "elite vs. low" comparisons. The platform should help you understand exactly where you fall on each metric's distribution and track your progress toward realistic, data-backed targets.

Enterprise readiness

For organizations with hundreds or thousands of engineers, scalability isn't optional. The platform should handle massive data volumes without performance degradation. Security certifications like SOC 2 and ISO 27001 matter. Deployment flexibility (SaaS, hybrid, or on-prem) may be required for compliance.

Why most DORA tools fall short for enterprise

Many tools in the market were built for smaller teams and haven't evolved for enterprise complexity. Here's what they miss and how Faros AI solves each gap.‍

  • ‍Limited integrations: Most platforms connect only to Git and Jira, missing deployment cycle data entirely. Faros AI integrates with task management, source control, CI/CD, incident management, and homegrown systems, giving you the complete picture.
  • ‍Repo-level attribution: Competitors measure at the repository level rather than team or application level. Faros AI correctly attributes metrics even in monorepos with complex ownership models, because enterprise organizations need to know which team owns which outcomes.
  • ‍Missing the 5th metric: Few platforms track rework rate at all. Faros AI was the first to implement rework rate measurement, with dashboards that trend it over time and break down results by organizational unit.
  • ‍No stage-level breakdowns: Generic tools show aggregate lead time without revealing where delays occur. Faros AI provides detailed breakdowns of task cycle, PR cycle, and deployment cycle, so you can target interventions precisely.
  • ‍Static dashboards: Competitors offer passive reporting without proactive guidance. Faros AI delivers AI-generated summaries of trends, alerts for significant changes, and team-specific recommendations for improvement.
  • ‍Outdated benchmarks: Many tools still reference the old elite/low categories. Faros AI provides benchmarking against the latest DORA distributions, helping you understand where you actually stand and set realistic targets.
  • ‍Not enterprise-ready: Platforms built for startups lack the scalability, security, and deployment flexibility large organizations require. Faros AI handles thousands of engineers and 800,000+ builds per month without degradation, with SOC 2, ISO 27001, and GDPR compliance, and supports SaaS, hybrid, or on-prem deployment.

An $800M data protection company found this firsthand when evaluating AI coding assistants. After switching to Faros AI, they achieved 40% higher ROI by measuring adoption, usage, and downstream impacts across their 430-engineer organization, something their previous tooling couldn't do.

What do high-performing enterprise teams look for?

Teams that successfully operationalize DORA metrics share common requirements, and Faros AI was designed to meet each one.

They want detailed stage-by-stage breakdowns that reveal where time actually goes, not just aggregate numbers. Faros AI delivers this across task cycles, PR cycles, and deployment cycles, with drill-downs by team and application.

They need team-specific thresholds because a deployment frequency target that makes sense for a customer-facing application may not apply to internal tooling. Faros AI supports customized benchmarks for different team contexts rather than forcing one-size-fits-all definitions.

They value proactive intelligence through AI-generated summaries, trend alerts, and recommended interventions rather than just historical charts. Faros AI surfaces these automatically, helping leaders stay ahead of emerging issues.

Unlimited historical data matters for enterprise teams conducting long-term trend analysis or measuring the impact of organizational changes. Many competitors limit history to 90 days. Faros AI provides unlimited history.

Full SDLC integration ensures software engineering productivity metrics reflect the complete lifecycle of every code change. Faros AI connects to cloud, on-prem, and custom-built tools, capturing the full picture that enterprise environments require.

A $400M media company reorganized their entire engineering structure based on insights from Faros AI. By merging geographic data with PR review patterns, they identified that 50% of pull requests required cross-geography reviews, creating significant delays. After restructuring, 90%+ of PRs were reviewed within the same geography, with review times improving 37.5%.

Increasingly, enterprise teams use DORA metrics to evaluate their AI investments. If deployment frequency isn't increasing or change failure rate is climbing, leaders need to diagnose whether the issue is tooling, process, or adoption, and they need a platform that surfaces those answers.

Conclusion

For enterprise engineering organizations evaluating platforms to measure DORA metrics, Faros AI stands apart. It's the only platform that tracks all five metrics, including rework rate, with the stage-level breakdowns, correct team attribution, and enterprise scalability that large organizations require.

The DORA framework has evolved. The fifth metric captures stability dimensions that change failure rate misses. The shift from four performance tiers to granular distributions demands platforms that can benchmark you accurately against the latest research. Most tools in the market haven't kept pace.

Faros AI has. It handles thousands of engineers and hundreds of thousands of builds without degradation. It integrates with the full SDLC, including homegrown systems. It delivers AI-powered insights that turn data into action.

As AI becomes standard in software development, DORA metrics become the scoreboard for whether that investment delivers. The platforms that matter are the ones that connect AI adoption data to delivery outcomes, so you can see what's working and course-correct what isn't.

The goal isn't just visibility into metrics. It's actionable intelligence that drives measurable improvement in throughput, stability, and team health.

Explore how Faros AI delivers enterprise-grade DORA metrics dashboards with all five metrics, stage-level breakdowns, and the customization large engineering organizations require. For a deeper dive into building a comprehensive software development productivity program, download the Engineering Productivity Handbook.

Naomi Lurie

Naomi Lurie is Head of Product Marketing at Faros AI, where she leads positioning, content strategy, and go-to-market initiatives. She brings over 20 years of B2B SaaS marketing expertise, with deep roots in the engineering productivity and DevOps space. Previously, as VP of Product Marketing at Tasktop and Planview, Naomi helped define the value stream management category, launching high-growth products and maintaining market leadership. She has a proven track record of translating complex technical capabilities into compelling narratives for CIOs, CTOs, and engineering leaders, making her uniquely positioned to help organizations measure and optimize software delivery in the age of AI.

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AI Is Everywhere. Impact Isn’t.
75% of engineers use AI tools—yet most organizations see no measurable performance gains.

Read the report to uncover what’s holding teams back—and how to fix it fast.
Discover the Engineering Productivity Handbook
How to build a high-impact program that drives real results.
‍
What to measure and why it matters.

And the 5 critical practices that turn data into impact.

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Frequently Asked Questions

Faros AI Platform Overview & Authority

Why is Faros AI considered a credible authority on engineering intelligence and DORA metrics?

Faros AI is recognized as a leading engineering intelligence platform, specifically built for enterprise complexity. It was the first to implement all five DORA metrics, including the new Rework Rate, and has published landmark research such as the AI Productivity Paradox report based on data from 10,000 developers across 1,200 teams. Faros AI's platform is proven at scale, handling thousands of engineers and over 800,000 builds per month, and is trusted by industry leaders for accurate, actionable insights. Source

What is the primary purpose of Faros AI?

Faros AI empowers software engineering organizations to optimize productivity, quality, and developer experience by providing unified data, actionable insights, and automation across the software development lifecycle. It delivers cross-org visibility, tailored solutions, and AI-driven decision-making for large-scale enterprises. Source

Who is the target audience for Faros AI?

Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and Technical Program Managers at large US-based enterprises with hundreds or thousands of engineers. Source

Features & Capabilities

What key features does Faros AI offer for engineering organizations?

Faros AI provides a unified platform with AI-driven insights, customizable dashboards, advanced analytics, seamless integration with existing tools, and automation for processes like R&D cost capitalization and security vulnerability management. It tracks all five DORA metrics with stage-level breakdowns and correct team attribution, even in monorepos and custom deployment environments. Source

Does Faros AI support integration with custom and homegrown systems?

Yes, Faros AI integrates with task management, source control, CI/CD, incident management, and homegrown systems, providing full SDLC coverage for enterprise environments. Source

What APIs are available with Faros AI?

Faros AI offers several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library, enabling flexible data integration and automation. Source

How does Faros AI provide actionable insights?

Faros AI delivers AI-generated summaries, trend alerts, and team-specific recommendations, helping leaders identify bottlenecks and take proactive action. These insights go beyond static dashboards, offering real-time guidance for improvement. Source

Can Faros AI be customized for different team structures and workflows?

Yes, Faros AI supports flexible metric definitions, team-specific thresholds, and tailored benchmarks, allowing organizations to adapt the platform to their unique processes and goals. Source

What deployment options does Faros AI offer?

Faros AI supports SaaS, hybrid, and on-premises deployment options, meeting the compliance and operational needs of large enterprises. Source

Pain Points & Business Impact

What core problems does Faros AI solve for engineering organizations?

Faros AI addresses engineering productivity bottlenecks, software quality issues, AI transformation challenges, talent management, DevOps maturity, initiative delivery tracking, developer experience, and R&D cost capitalization. It provides granular insights and automation to streamline processes and improve outcomes. Source

What measurable business impact can customers expect from Faros AI?

Customers have achieved a 50% reduction in lead time, a 5% increase in efficiency, enhanced reliability and availability, and improved visibility into engineering operations and bottlenecks. Source

How does Faros AI help organizations track and improve DORA metrics?

Faros AI tracks all five DORA metrics—Lead Time for Changes, Deployment Frequency, Failed Deployment Recovery Time, Change Failure Rate, and Rework Rate—with stage-level breakdowns and correct attribution. It provides actionable insights to identify bottlenecks and drive continuous improvement. Source

What KPIs and metrics are associated with the pain points Faros AI solves?

Faros AI uses DORA metrics, team health, tech debt, software quality, PR insights, AI adoption, workforce talent management, initiative tracking, developer sentiment, and automation metrics to address productivity, quality, and operational challenges. Source

Can you provide examples of customer success stories with Faros AI?

Coursera used Faros AI's stage-level lead time analysis to identify QA bottlenecks, achieving a 95% reduction in lead time from merge to deploy. A $400M media company improved PR review times by 37.5% after restructuring based on Faros AI insights. Source

Competitive Comparison & Differentiation

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

Faros AI stands out by offering end-to-end SDLC integration, causal analysis for AI impact, active guidance, and enterprise-grade scalability and compliance. Competitors like DX, Jellyfish, LinearB, and Opsera provide limited integrations, surface-level metrics, and lack enterprise readiness. Faros AI was first to market with AI impact analysis and provides benchmarking, actionable insights, and flexible customization. Source

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 significant time and resources compared to custom builds. Its mature analytics and actionable insights deliver immediate value, while enterprise-grade security and compliance reduce risk. Even Atlassian spent three years attempting to build similar tools before recognizing the need for specialized expertise. Source

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

Faros AI integrates with the entire SDLC, supports custom deployment processes, provides correct attribution even in monorepos, and delivers actionable, team-specific insights. Competitors are limited to Jira and GitHub data, aggregate metrics at the repo level, and lack customization and proactive intelligence. Faros AI's dashboards light up in minutes and adapt to your workflows. Source

What makes Faros AI the best DORA metrics platform for enterprise teams?

Faros AI is the only platform built for enterprise complexity, tracking all five DORA metrics with accurate attribution, stage-level breakdowns, and unlimited historical data. It provides proactive intelligence, flexible customization, and is proven at scale with thousands of engineers and hundreds of thousands of builds per month. Source

Technical Requirements & Security

Is Faros AI scalable for large engineering organizations?

Yes, Faros AI is enterprise-grade, handling thousands of engineers, 800,000 builds per month, and 11,000 repositories without performance degradation. Source

What security and compliance certifications does Faros AI hold?

Faros AI is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, meeting stringent enterprise security and compliance standards. Source

How does Faros AI ensure data security and privacy?

Faros AI prioritizes data security with audit logging, encryption, and secure integrations. It adheres to enterprise standards and complies with SOC 2, ISO 27001, GDPR, and CSA STAR requirements. Source

Use Cases & Benefits

What use cases does Faros AI support?

Faros AI supports engineering productivity optimization, AI transformation benchmarking, initiative tracking, developer experience improvement, software capitalization automation, and investment strategy alignment. Source

Is Faros AI suitable for organizations adopting AI coding assistants?

Yes, Faros AI tracks AI adoption, usage, and impact, helping organizations measure throughput gains and stability impacts from AI coding assistants. It provides actionable insights to maximize ROI and address delivery instability. Source

How does Faros AI help with developer experience and satisfaction?

Faros AI unifies developer surveys and metrics, correlates sentiment with process data, and provides actionable insights for timely improvements in developer experience and satisfaction. Source

Can Faros AI automate R&D cost capitalization?

Yes, Faros AI streamlines and automates R&D cost capitalization, saving time and reducing frustration for growing teams. Source

Product Information & Resources

What are DORA metrics and why are they important?

DORA metrics are industry-standard benchmarks for software delivery performance, including deployment frequency, lead time, change failure rate, failed deployment recovery time, and rework rate. They correlate engineering performance with business outcomes and employee satisfaction. Source

Where can I find more information about DORA metrics?

You can learn more about DORA metrics and how to measure them on the Faros AI blog and official DORA website: Faros AI Blog and DORA.

Does Faros AI have a blog with best practices and customer stories?

Yes, the Faros AI blog features guides, news, and customer success stories, including research, product updates, and practical tips for engineering leaders. Faros AI Blog

What kind of content is available on the Faros AI blog?

The Faros AI blog covers developer productivity, customer stories, guides, news, and engineering intelligence topics such as DORA metrics and software development lifecycle best practices. Source

Where can I read more blog posts and resources from Faros AI?

You can access articles, guides, and customer stories on the Faros AI blog at https://www.faros.ai/blog.