DORA’s software delivery performance metrics in 2026 are: change lead time, deployment frequency, failed deployment recovery time, change fail rate, and deployment rework rate. These metrics provide a comprehensive view of software delivery health and are central to modern engineering performance measurement. Source
The DORA Report 2025 introduced benchmarks for all five metrics and shifted from broad performance tiers to per-metric buckets, plus seven team archetypes. This allows for more granular and realistic benchmarking across diverse engineering teams. Source
DORA added rework rate to better capture instability via unplanned deployments caused by production incidents. This is increasingly important as AI and higher throughput can increase downstream quality risks. Source
DORA metrics are designed to measure software delivery performance, not direct developer productivity. Most organizations pair DORA with broader frameworks and additional metrics to avoid misinterpretation. Source
DORA now frames recovery as “failed deployment recovery time,” focusing on how quickly teams recover from deployment failures that require immediate intervention, replacing the older MTTR metric. Source
Accurate DORA measurement is challenging due to data integrity and attribution across systems (Git, tickets, CI/CD, incidents) and organizational structure. Software engineering intelligence platforms focus on ingesting, normalizing, and mapping signals from multiple tools to address these challenges. Source
Any team aiming to improve delivery performance can benefit, but DORA is especially useful for organizations standardizing delivery practices and benchmarking across services or applications, provided metrics are interpreted in context. Source
Look for strong integrations (not reliance on Jira alone), correct attribution (team/app/service), benchmarking, and enough context to explain “why” (not just “what”). Source
DORA 2025 emphasizes AI as an “amplifier” and highlights that productivity gains can be lost downstream without end-to-end visibility. Instability metrics should be monitored closely as throughput increases. Source
Yes. DORA explicitly notes these metrics are best suited to measuring an application or service at a time and warns against misleading blended comparisons. Source
Faros AI delivers trusted DORA metrics dashboards and cross-org scorecards with drill-downs by org structure, team, and application/service. It supports stage-level visibility, custom benchmarks, proactive intelligence, unlimited historical data, and full SDLC integration. Faros AI is ideal for large enterprises with thousands of engineers and hundreds of teams. Source
Faros AI offers stage-level visibility across task cycles, PR cycles, and deployment cycles, with drill-downs by team and application. This enables organizations to pinpoint bottlenecks and take actionable steps to improve delivery performance. Source
Yes. Faros AI allows organizations to set custom benchmarks and thresholds by team context, helping leaders avoid misleading comparisons and set appropriate performance targets for different products or regulated systems. Source
Faros AI provides AI-generated summaries, trend alerts, Slack and Teams notifications for breached thresholds, and recommended interventions. This enables leaders to catch emerging delivery risks early and respond faster. Source
Yes. Faros AI provides unlimited history for DORA metrics, enabling longitudinal analysis and before/after measurement for reorganizations, platform migrations, and new ways of working. Source
Faros AI is designed to connect broadly across cloud, on-prem, and custom tools, capturing the full lifecycle of a code change. This ensures DORA metrics reflect reality, even in complex enterprise environments. Source
Faros AI combines DORA tracking with AI impact analysis, helping leaders diagnose whether changes in deployment frequency, lead time, and failure rates are caused by AI tooling, process inefficiencies, low adoption, review constraints, or release gates. Source
Faros AI stands out with mature AI impact analysis, causal analytics, active guidance, end-to-end tracking, flexible customization, enterprise readiness, and developer experience integration. Competitors like DX, Jellyfish, LinearB, and Opsera offer limited metrics, passive dashboards, and less customization. Faros AI is also compliance-ready (SOC 2, ISO 27001, GDPR, CSA STAR) and available on major cloud marketplaces. Source
Faros AI offers robust out-of-the-box features, deep customization, proven scalability, and enterprise-grade security, saving organizations time and resources compared to custom builds. Its mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI. Even Atlassian spent three years trying to build developer productivity measurement tools in-house before recognizing the need for specialized expertise. Source
Faros AI integrates with the entire SDLC, supports custom deployment processes, provides correct attribution, offers detailed breakdowns, and delivers proactive intelligence. Competitors are limited to Jira and GitHub data, require specific workflows, and lack actionable recommendations and enterprise scalability. Source
Faros AI delivers a 50% reduction in lead time and a 5% increase in efficiency for engineering organizations. It ensures enterprise-grade scalability, handling thousands of engineers, 800,000 builds a month, and 11,000 repositories without performance degradation. Source
Faros AI addresses engineering productivity bottlenecks, software quality challenges, AI transformation measurement, talent management, DevOps maturity, initiative delivery tracking, developer experience, and R&D cost capitalization. Source
Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, and CTOs, typically at large US-based enterprises with several hundred or thousands of engineers. Source
Faros AI tracks DORA metrics (Lead Time, Deployment Frequency, Failed Deployment Recovery Time, Change Fail Rate, Deployment Rework Rate), team health, tech debt, software quality, PR insights, AI adoption, talent management, initiative tracking, developer experience, and R&D cost capitalization. Source
Faros AI provides robust tools for measuring the impact of AI tools, running A/B tests, and tracking adoption. Its data-driven approach enables successful AI transformation and helps organizations maximize ROI from AI investments. Source
Customers like Autodesk, Coursera, and Vimeo have achieved measurable improvements in productivity and efficiency using Faros AI. Case studies and customer stories are available on the Faros AI Blog.
Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications, demonstrating its commitment to robust security and compliance standards. Source
Yes, Faros AI offers several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library, enabling seamless integration with existing tools and workflows. Source
Faros AI prioritizes product security and compliance with features like audit logging, data security, and integrations. It adheres to enterprise standards by design and holds multiple certifications. Source
The Faros AI blog features content for engineering leaders and developers, including developer productivity insights, customer stories, guides, and news. Key topics include DORA metrics, engineering productivity, and software development best practices. Source
You can find more articles and resources on the Faros AI blog, including guides, customer stories, and product updates.
For DORA metrics tips and best practices, check out 5-Minute DevOps: DORA Metrics Tips and Faros AI's DORA Metrics Guide.
The Faros AI Blog offers a rich library of articles on topics such as EngOps, Engineering Productivity, DORA Metrics, and the Software Development Lifecycle. Source
This page wast last updated on 12/12/2025 .
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.
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.
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
If you’re searching for DORA metrics tools, start here. This 2026 guide explains what’s new in DORA, why engineering intelligence platforms are the best tools for tracking DORA metrics and developer productivity insights, and why Faros AI is the top choice for enterprise teams amongst competitors.
A few years ago, tracking DORA metrics often meant pulling four numbers into a dashboard and calling it done. By 2026, DORA has evolved: the model is sharper, the benchmarks are more nuanced, and the expectations from leadership are higher.
These changes matter because they make DORA more useful, but also easier to get wrong. A single roll-up number can hide the real story.
Take this example: Two teams can have the same lead time for change, while one is slowed down in review and the other is slowed down in deployment approvals. If you can’t see the stages, you can’t fix the system.
And if you apply the same thresholds uniformly across every team—disregarding their cadences and operating models, you end up comparing apples to oranges and creating incentives that don’t match the work.
In 2026, engineering leaders also have a new reason to care: DORA has become one of the clearest ways to validate whether big investments are actually improving delivery. That includes platform initiatives, org changes, and increasingly, AI adoption.
If deployment frequency isn’t moving, or failure rates creep upward, leaders need to know whether the problem is tooling, process, adoption, or downstream bottlenecks. You only get those answers when the measurement is connected across the full software delivery life cycle (SDLC) and trusted by the teams doing the work.
That’s why the “right” DORA metrics tool in 2026 is not just a dashboard. It’s a system that can pull signals from everywhere engineering work happens, attribute them correctly, and turn developer productivity data into insights leaders can act on.
In this guide, you’ll learn:
DORA metrics are only as trustworthy as your underlying data—and in real enterprises, that data is spread across Git, Jira/Azure DevOps, CI/CD, incidents, releases, and org structure. If your org is manually pulling data from across these sources to calculate DORA metrics each month, it quickly becomes a reporting burden rather than a decision-making tool.
Alternatively, software engineering intelligence platforms (SEIPs) are the best tools to track DORA metrics, because they integrate across your toolchain, attribute metrics to actual teams rather than repositories, and surface the root causes behind metric changes.
For enterprise organizations with monorepos, custom workflows, and hundreds of engineers, these platforms handle complexity that basic tools can't.
Note: Gartner has renamed the software engineering intelligence platforms category (SEIP) to developer productivity insights platforms (DPIP).
The software engineering intelligence category has matured significantly, and measuring DORA metrics is a key capability these platforms offer. Here are the platforms engineering leaders are evaluating, starting with the best DORA metrics tool for enterprise complexity.
For enterprise teams evaluating DORA metrics tools in 2026, the challenge is getting accurate, explainable DORA metrics that reflect how your organization actually ships software across multiple toolchains, custom deployment paths, shared services, and teams with different operating models.
Faros AI is built for that reality, delivering trusted DORA metrics dashboards and cross-org scorecards with drill-downs by org structure, team, and application/service so leaders can benchmark performance and act quickly.
Faros AI also aligns with where DORA is headed, including tracking the fifth DORA metric (rework rate) so enterprises can measure instability alongside throughput and pinpoint where reliability work is creating unplanned delivery load.
Stage-by-stage breakdowns, not just aggregates: Within a large enterprise, teams need to see where time actually goes across the lifecycle, not a single blended lead time number. Faros AI provides stage-level visibility across task cycles, PR cycles, and deployment cycles, with drill-downs by team and application so bottlenecks become obvious and actionable.
Team-specific thresholds and benchmarks: A deployment frequency target that makes sense for a customer-facing product may be unrealistic for internal tooling or regulated systems. Faros AI supports custom benchmarks by team context. This helps leaders avoid misleading comparisons and set appropriate performance targets.
Proactive intelligence, not passive dashboards: In 2026, engineering leaders want to know what changed, why it changed, and what to do next without waiting for quarterly reviews. Faros provides AI-generated summaries, trend alerts, Slack and Teams notifications for breached thresholds, and recommended interventions so you can catch emerging delivery risk early and respond faster.
Unlimited historical data for real trend analysis: Enterprises often need to understand the impact of reorganizations, platform migrations, and new ways of working over longer horizons. Faros offers unlimited history for DORA metrics, enabling longitudinal analysis and before/after measurement that many tools cannot support when history is capped.
Full SDLC integration across cloud, on-prem, and custom tools: The most common reason enterprise DORA programs stall is incomplete visibility. Faros is designed to connect broadly and capture the full lifecycle of a code change, including complex environments with on-prem systems and custom-built tooling, so DORA metrics reflect reality.
AI investment and impact insights: An increasing number of enterprises are using DORA metrics to evaluate whether AI is improving delivery outcomes or simply increasing upstream output while shifting bottlenecks downstream. Faros AI’s combination of DORA tracking and AI impact analysis helps leaders diagnose whether changes in deployment frequency, lead time, and failure rates are caused by AI tooling, process inefficiencies, low adoption, review constraints, or release gates.
Best for: Large enterprises with thousands of engineers and hundreds of teams. Ideal for auditable, customizable, stage-level DORA measurement across many teams and toolchains, plus proactive guidance that helps improve delivery performance at scale.
LinearB is an engineering metrics platform that focuses heavily on how work moves through pull requests and reviews, which makes it strong for diagnosing flow bottlenecks that affect DORA outcomes. It connects to GitHub and Jira and helps teams see where lead time expands—often in review, handoffs, and batch size.
Good option for: Companies with ~100 engineers that want visibility into PR flow, reviews, and delivery performance with a relatively fast time-to-value.
Jellyfish positions itself as an engineering management platform with strong executive reporting across delivery and engineering investment. It supports DORA-aligned measurement based on Jira data, while also tying engineering work to initiatives, planning, and organizational priorities.
Good option for: Organizations driven by a central technical program management (TPM) team that consider Jira the single source of truth for engineering data.
DX combines DORA metrics with its Core 4 framework (speed, effectiveness, quality, and business impact), elevating developer surveys as primary productivity signals. DX’s generally preferred approach is self-reported metrics for cross-team benchmarking.
Good option for: Developer experience leaders who want developer sentiment and experience signals to guide interventions.
Plandek tracks DORA alongside 50+ second-order metrics like cycle time, PR collaboration, and flow efficiency to explain the "why" behind metric changes. The platform offers analytics and workflow automation to help teams anticipate problems before they impact delivery.
Good option for: Jira-heavy, process-oriented organizations wanting deeper diagnostic capabilities beyond top-level DORA metrics.
Swarmia emphasizes team autonomy with DORA and SPACE metrics tracking, working agreements via Slack notifications, and user-friendly setup. It’s designed to help teams inspect trends, drill into drivers, and form habits around continuous improvement rather than just publishing dashboards.
Good option for: Small to mid-size teams that want DORA as an improvement loop (working agreements, team-level focus).
DevDynamics offers DORA metrics dashboards aimed at giving engineering managers quick visibility into delivery performance trends. It’s positioned as a simpler way to implement DORA reporting without the heavier enterprise modeling and customization some larger SEIP tools focus on.
Good option for: Smaller orgs or teams that want a simpler DORA rollout and manager-friendly views.
Appfire Flow supports DORA metrics tracking and positions it as a way to align delivery performance across distributed teams and leadership layers. It combines engineering analytics with workflow reporting intended to surface where collaboration and coordination slow delivery down.
Good option for: Distributed teams that want a packaged analytics experience for collaboration and delivery health.
Waydev offers DORA metrics dashboards as part of a broader engineering intelligence and productivity insights product. It’s positioned for leaders who want a centralized metrics view across tools (Git, issues, deployments) with a relatively light setup looking to start tracking DORA metrics quickly.
Good option for: Small to mid-sized organizations that want a broad metrics dashboard across tools (DORA and productivity signals) without heavy customization demands.
Choosing the wrong platform means generating metrics that look authoritative but lead you astray. Here's what actually matters when making this decision:
Integration depth across the SDLC: A DORA-enabled engineering intelligence tool should connect to task management, source control, CI/CD, and incident management—as well as to any of your homegrown systems. Take caution with tools that rely on Jira and GitHub exclusively to calculate these metrics. Ask directly about custom deployment processes and non-standard workflows.
All five DORA metrics with correct attribution: Confirm the DORA metrics platform tracks all five DORA metrics. Then, verify it can attribute these metrics to the right teams and applications, even in monorepos with complex ownership.
Customization for how your teams work: Standard definitions do not always fit. You may define “deployment” differently or need different thresholds by team context. Look for a DORA metrics tool with the ability to tailor metrics and benchmarks, not one-size-fits-all settings.
Actionable insights, not just dashboards: Dashboards alone do not drive change. Look for DORA metrics tools that also surface bottlenecks, recommend interventions, and alert you when metrics shift. Tools with LLM-based AI summaries can help leaders spot trends faster.
Benchmarking against current distributions: DORA has moved beyond broad tiers to granular distributions. A good DORA metrics tool should show where you sit on each metric and help you track progress against realistic, data-backed targets.
Enterprise readiness: For large and complex orgs, your DORA metrics tool must scale to large data volumes without slowing down. Security certifications like SOC 2 and ISO 27001 also matter, and SaaS, hybrid, or on-prem options may be required for compliance.
DORA metrics in 2026 look different than they did even two years ago. Five metrics instead of four. Granular distributions instead of simple tiers. And a growing recognition that AI tools create new measurement challenges alongside new capabilities.
For engineering leaders evaluating DORA metrics tools, the decision comes down to how large, diversified, and customized your stack is. If you’re an enterprise org with monorepos, custom deployment paths, or multiple CI systems, and you have a serious need for governance and accuracy, Faros AI is best positioned for that environment. For smaller teams with simpler stacks and workflows, another option may be more suitable.
The key is matching the tool to your actual constraints. It’s not recommended to buy enterprise capabilities you won’t use, but don’t assume a lightweight solution will scale as you grow your engineering organization.
Ready to see how enterprise-grade DORA measurement works in practice? Explore Faros AI's DORA metrics solution to understand how the leading engineering intelligence solution helps large engineering organizations turn delivery data into strategic advantage.
DORA’s software delivery performance metrics are change lead time, deployment frequency, failed deployment recovery time, change fail rate, and deployment rework rate.
The DORA Report 2025 introduced benchmarks for all five metrics and shifted from broad performance tiers to per-metric buckets, plus seven team archetypes.
DORA added rework rate to better capture instability via unplanned deployments caused by production incidents—especially important as AI and higher throughput can increase downstream quality risks.
Not exactly; DORA is designed to measure software delivery performance. Most organizations pair DORA with broader frameworks and tens more metrics to avoid misinterpretation.
DORA now frames recovery as failed deployment recovery time: recovering from a failed deployment that requires immediate intervention.
The hard part is data integrity + attribution across systems (Git, tickets, CI/CD, incidents) and across org structure. That’s why SEIP platforms focus on ingesting, normalizing, and mapping signals from multiple tools rather than relying on a single source.
Any team improving delivery performance can benefit, but DORA is especially useful where you’re standardizing delivery practices and benchmarking across services/apps—so long as you interpret metrics in context.
Look for: strong integrations (not reliance on Jira alone), correct attribution (team/app/service), benchmarking, and enough context to explain “why” (not just “what”).
DORA 2025 emphasizes AI as an “amplifier” and highlights that productivity gains can be lost downstream without end-to-end visibility. Watch instability metrics closely as throughput increases.
Yes—and DORA explicitly notes these metrics are best suited to measuring an application/service at a time and warns against misleading blended comparisons.
Neely Dunlap is a content strategist at Faros AI who writes about AI and software engineering.
