DORA Metrics Tools: ULTIMATE Guide to Choose the Best Option

You're on the right page if you’re looking for the best DORA metrics tools.

After we share our five-step framework, which includes metrics measurement, developer friendliness, integrations, and more, we'll analyze six top contenders.

Feel free to use it if you’re doing your own shortlist.

Here’s a sneak peak:

What Are DORA Metrics?

DevOps Research and Assessment (DORA) metrics are performance indicators for multidisciplinary teams (i.e., development and operations teams). You can use them to gain valuable insights into your software development lifecycle.

Following DORA metrics allows engineering teams, software leaders, and other stakeholders to make data-driven decisions based on team performance. The end goal, of course, is to improve operational efficiency, and, as a result, customer retention and satisfaction.

1. Deployment Frequency

Deployment frequency measures the number of times a team successfully releases to production over a given period. Frequent deployments indicate a smooth and efficient deployment process, which can lead to faster feedback and improvement cycles.

• Good averages: According to the DORA State of DevOps Report 2024, top-performing DevOps teams deploy on-demand or multiple times per day. Top-performing teams deploy once per day to once per week, while low performers, who face common challenges and failure in production, deploy only once per month to once every six months.
• Challenges in measurement: One challenge is having multiple sources of deployment data across different environments, such as different teams, tools, or automated systems. All this can make it challenging to get a unified view of all deployments. Additionally, tracking the production environment can be harder for on-premise applications or mobile apps, where deployments may occur differently from cloud-based systems.

2. Lead Time for Changes

Lead time for changes measures the period it takes for a committed code change to reach production.

Shorter lead times indicate a more efficient development process because teams respond quickly to changes and feedback. That improves user and developer experience.

• Good averages: Elite teams often have lead times of less than a day, while medium performers might take up to a month (more details below).
• Challenges in measurement: The main challenge lies in standardizing the starting point, such as the first commit, and accounting for different Git workflows. Long-lived branches complicate tracking, while trunk-based development simplifies it.

The DORA Accelerate State of DevOps Report 2021 showed that elite DevOps teams achieve lead times for changes in less than 1 hour, which dramatically boosts the speed of delivery and organizational performance. High performers deliver updates within a period of time ranging from 1 hour to 1 week, while average teams take up to 1 month.

However, the 2024 DORA report now considers elite time for changes being less than one day. Another interesting change is that both high and medium-performance teams have a lead time for changes between 1 week and one month.

3. Change Failure Rate

Change failure rate measures the percentage of deployments causing failures in production requiring remediation (e.g., hotfix, rollback, patch).

This metric shows whether you have good code quality and a reliable deployment process. A lower change failure rate suggests better code and processes, which lead to higher customer satisfaction.

• Good averages: High-performing teams have a change failure rate of 0-15%, while lower-performing teams might experience rates above 30%.
• Challenges in measurement: Determining what qualifies as a failure and accurately tracking these incidents can be complex, especially if you don’t have a standardized incident management system. Additionally, it’s challenging to capture all incidents because they have many different sources to account for. Another challenge is attributing the failure to the correct deployment or team.

4. Failed Deployment Recovery Time

This metric measures the time it takes for a team to recover from a production failure and restore service to users. It helps you understand the effectiveness of incident management and response workflows.

Faster recovery times are better because they enhance operational performance and minimize customer impact.

• Good averages: According to the Accelerate State of DevOps 2024 report, the average time to recovery for elite teams is less than 1 hour, while high performers recover within 1 day. Low performers can take anywhere from 1 day to 1 month, and they miss out on exciting opportunities to improve. Tracking recovery time is a critical DevOps metric for boosting resilience and speed.
• Challenges in measurement: Accurately tracking recovery time can be complex due to incident severity and response efficiency variability. For accurate measurement, you need consistent incident reporting and management tools. Another challenge is having different incident sources to support.

How to Choose a Good DORA Metrics Tool

As you can see, all the metrics above have their own measurement challenges. Getting an accurate read on your current situation can be difficult because of factors like inconsistent data, variability in how metrics are defined across teams, or gaps in your toolchain integration. These challenges can lead to incomplete insights and skewed results, making it harder to drive meaningful improvements.

You must select the right tool to build a comprehensive DORA metrics dashboard and avoid these mistakes.

From our experience at Axify, we encourage you to consider the following criteria to make an informed choice.

1. Metrics Measurement

A good tool should cover all essential metrics and provide actionable insights to accurately measure your team's DevOps performance. Here are some questions to help assess a tool’s metrics measurement capabilities:

• Does the tool cover all four DORA metrics? Ensure the tool provides comprehensive coverage of the four indicators we discussed above.
• How are the metrics visualized and reported? Look for intuitive dashboards that make it easy to interpret data and make informed decisions.
• Does the tool provide organization-wide insights? Some tools, like Axify, allow you to view DORA software metrics across the entire organization. This lets you see performance metrics for all projects or teams simultaneously, making monitoring and comparing performance across your organization easier.
• Can we trust the data’s accuracy? Check if the tool allows you to quickly drill down into the data, view historical data, and validate its accuracy. A tool that can do this is more reliable because it helps you make data-driven decisions based on accurate insights.

2. Developer Friendliness

A developer-friendly tool ensures your team adopts and uses it effectively. Consider the following questions:

• Is the tool easy to use and understand? The interface should be intuitive, allowing developers to quickly access the information they need without a steep learning curve.
• Does it provide valuable insights without overwhelming users? Look for a balance between detail and simplicity. You need actionable insights that aren’t overly complex.
• How does the tool support continuous improvement? Ensure the DORA metrics tool facilitates a culture of continuous integration and improvement by providing feedback loops and highlighting opportunities for improvement.

3. Integrations

The tool should offer flexible integrations to fit seamlessly into your existing workflows. Here are some questions to consider:

• Does it integrate with our existing tools and platforms? Check for compatibility with your code repository, project management tools, incident management systems, and other DevOps tools.
• How easy is it to set up and maintain integrations? Assess the tool’s setup process and ongoing maintenance requirements to ensure it won’t burden your engineering teams.
• How customizable is it to fit your workflow? Look for a tool that seamlessly integrates with your team's workflow without requiring your team to adapt.
• Does it support multiple sources? Ensure the tool can handle numerous repositories, deployment, and incident sources.
• Can you filter views by teams or repositories? Check if the tool allows you to filter and customize views, enabling you to focus on specific teams or repositories as needed.
• How does the tool handle incident tracking? Many tools naively track incidents solely through issue trackers like Jira, but a solid tool should support multiple impact sources. Look for solutions that go beyond bug tracking and support a variety of incident sources to provide a more complete picture of your production issues. As a side note, this is an area where tools like Axify outperform competitors like LinearB.

4. Data Reliability and Performance

Consider how reliable the tool is for syncing data and performance. Here are key questions to evaluate.

• How reliable is the data? Ensure the tool syncs regularly and consistently with minimal delays or issues.
• How long does it take to synchronize for the first time? Investigate how quickly the tool can sync your data from when it's connected. Fast and efficient syncing is crucial, especially for teams managing large codebases.
• Does the tool ensure data security? Some tools, like LinearB, clone repositories to calculate metrics, which raises security concerns. Look for solutions that extract only the necessary metadata, such as pull request metadata, to reduce security risks without compromising performance.

5. Support and Community

Reliable support and a tight user community can significantly impact your experience with the tool. Here’s what to look for:

• What support options are available? Check for available support channels like chat, email, or phone and whether there’s a dedicated support team.
• Are there comprehensive resources available? Look for documentation, tutorials, and other learning resources to help your team get the most out of the tool.
  

DORA Software Tools List

Let’s jump into the part you’re here for.

1. Axify: Best for Tracking Metrics that Matter

Axify is a powerful platform designed to provide teams with a comprehensive view of their software development process. Axify helps engineering teams enhance their performance and deliver reliable software efficiently by tracking key metrics – from DORA to resource allocation and team morale.

Axify is an intuitive tool for different roles: engineering leaders and managers, product managers, and product operations professionals.

Key Features

• DORA metrics dashboard: This dashboard provides real-time insights into all four DORA metrics, allowing teams to measure software delivery performance and make data-driven decisions. Pair this with Axify’s Value Stream Mapping and objective tracking to track all essential metrics related to your business goals.
• Value Stream Mapping: Axify helps you visualize and optimize the flow of value from development to delivery, providing a clear picture of bottlenecks and areas for improvement.
• Organization-wide insights: Axify enables you to track DORA Metrics across all teams and projects, offering a comprehensive view of your organization's performance. This feature allows you to compare performance metrics across different teams and identify areas for improvement.
• Integration with DevOps tools: Axify seamlessly integrates with popular DevOps tools, such as code repositories and project management systems. This ensures smooth data flow and streamlined processes across all your metrics. However, other tools allow more integrations.
• Software delivery forecast: Axify calculates precise delivery estimates based on your historical data. Predictable software delivery is essential for maximizing your budget and offering top-notch solutions for your customers.

What Makes Axify Different

Axify offers a holistic view of DORA and additional development metrics like flow metrics and software delivery forecasts. It can also accommodate different teams, making it a versatile choice for organizations looking to optimize their software delivery process.

2. LinearB: Best for Workflow Automation and Developer Productivity

LinearB is a software engineering intelligence platform that offers deep insights into workflows and automates routine tasks.

LinearB allows for team-level rather than individual metrics, which people see as more reasonable and practical. Users appreciate that LinearB can help identify trends or bottlenecks without micromanaging individual developers.

However, LinearB has limitations in incident management. It relies mostly on issue trackers like Jira, which many users feel doesn’t provide a complete picture. Axify, in contrast, handles incident tracking more comprehensively because it supports multiple incident sources, giving teams deeper visibility into production issues.

Moreover, LinearB clones the entire repository to calculate its metrics, which raises significant security concerns. Axify only extracts the necessary pull request metadata, providing a secure and streamlined process. Users may question why a tool would need to scan the entire codebase, as it increases the risk of exposing sensitive code.

Key Features

• Workflow automation: LinearB’s key strength lies in its ability to automate daily tasks, reduce manual effort, and allow developers to focus on high-impact work. The platform offers programmable workflows that optimize PR reviews and other key processes.
• DORA metrics tracking: LinearB integrates DORA metrics into its broader performance analytics, providing teams with actionable insights to streamline their software delivery.
• Resource allocation and project forecasting: The platform offers tools to track resource utilization and predict project outcomes. This helps teams manage risks and costs effectively.

What Makes LinearB Different

LinearB stands out due to its strong focus on automating workflows and enhancing developer productivity. Using tools like WorkerB and gitStream for policy-as-code allows teams to optimize their processes and significantly reduce cycle time and PR review time.

3. Swarmia: Good for Aligning Engineering Work with Business Objectives

Swarmia is a work management tool that bridges the gap between engineering work and business goals. It focuses on helping teams prioritize their tasks to deliver business value more effectively.

Axify offers similar capabilities with its Executive Dashboard, helping leadership measure the ROI of engineering efforts while maintaining a holistic view of the entire value stream. However, Axify goes a step further, providing tools for the whole team so that everyone, from software engineers to product managers and CTOs, can access valuable, actionable insights.

As for Swarmia, many users are concerned metrics can be misused by management to unfairly compare team performance or punish developers, especially if the metrics are applied rigidly or without context.

There’s a worry that focusing too much on metrics like time from the first commit to merge might lead to gaming the system, where developers might delay commits or merge code without proper review to meet metrics.

Besides, Swarmia only supports a few integrations (e.g., Jira/Linear and GitHub). By contrast, Axify offers more flexibility and integrations.

Key Features

• Healthy team-level metrics: Swarmia tracks DORA and SPACE metrics, giving teams the insights they need to identify and eliminate bottlenecks while maintaining a healthy work environment.
• Automated feedback loops: The platform enhances collaboration and code quality by integrating with tools like Slack for real-time notifications and feedback. This helps teams adopt and maintain best practices.
• Developer experience and productivity: Swarmia combines system metrics with developer feedback to offer a comprehensive view of the development process. Therefore, it ensures that both quantitative data and qualitative insights drive improvements.

What Makes Swarmia Different

Swarmia stands out for its focus on aligning engineering tasks with broader business objectives. Axify is better for teams looking for a holistic approach with unique metrics like team morale and advanced software delivery forecasts. Besides, our Executive Dashboard gives leadership valuable insights.

4. Jellyfish: Best for Engineering Management and Alignment

Jellyfish is designed to help engineering leaders manage their teams and ensure that their work aligns with company strategy. It provides deep insights into how resources are being used and where improvements can be made.

However, Jellyfish is unsuitable for other roles, especially those directly involved in creating the software. Besides, you need organizational-wide admin configuration to install Jellyfish. You may need to change your Jira practices to accommodate this new dashboard.

Key Features

• Resource allocation: Jellyfish offers a comprehensive view of how engineering resources are utilized. Therefore, leaders can ensure their teams are focused on the highest-priority initiatives that maximize business impact.
• Engineering metrics and reporting: The platform integrates data from various tools and contextualizes it with business data. Engineering leaders can track progress, forecast delivery timelines, and communicate effectively with stakeholders.
• Scenario planning and delivery management: Jellyfish allows teams to model different delivery scenarios. You can identify potential risks and ensure projects stay on track and within budget.

What Makes Jellyfish Different

Jellyfish is tailored for engineering leadership and finance teams. By comparison, Axify can help other team members beyond the C-suite.

5. Allstacks: Best for Predictive Analytics in Software Delivery

Allstacks is a predictive forecasting and risk management platform for software delivery. It integrates with the tools your team already uses to provide insights into potential risks and delays.

However, Allstacks offers a wide range of metrics to select from in a customizable dashboard, which can create unnecessary noise. In contrast, Axify focuses on delivering only the most valuable metrics, eliminating the clutter and linking engineering performance to business outcomes.

Key Features

• Forecast and estimations: Uses historical data to predict software delivery estimates and identify risks before they become issues.
• Risk management: Helps teams proactively manage risks, reducing the likelihood of delays and other problems.
• Comprehensive dashboards: Offers customizable dashboards that provide visibility into the health and progress of projects.

What Makes Allstacks Different

Allstacks excels in its predictive analytics capabilities, allowing teams to manage risks more effectively than most other tools. By comparison, Axify’s more streamlined dashboard links performance metrics to business outcomes.

6. Sleuth: Best for Deployment Tracking and Automation

Sleuth is a deployment tracking tool designed to help engineering teams measure and improve their performance, mainly through DORA metrics. It provides real-time insights into the software delivery process and leverages AI and automation to enhance efficiency.

Instead of primarily relying on ticketing systems or git data, Sleuth focuses on deploys and impact metrics, then connects these to issues, git data, builds, and more. This method ensures a more accurate representation of what is being shipped and its impact.

Key Features

• DORA software metrics tracking: Sleuth excels at monitoring the four key DORA metrics—deployment frequency, lead time for changes, change failure rate, and failed deployment recovery time. These metrics help teams understand and optimize their software delivery performance.
• Automations marketplace: Sleuth offers a good set of no-code automations that can be easily integrated into existing workflows to reduce manual tasks, improve consistency, and enable faster deployments.
• Real-time deployment insights: The platform provides detailed insights into each deployment, helping teams identify and address bottlenecks before they impact delivery timelines.

What Makes Sleuth Different

Sleuth helps teams optimize their DevOps practices without extensive manual intervention. It offers various engineering review templates and AI summaries for each metric. You can also write your edits and reviews on the dashboard for extra visibility. Finally, you can track action items and meeting outcomes straight from the platform.

7. Cortex: Best for Driving Continuous Engineering Excellence

Cortex is an Internal Developer Portal built to help engineering teams achieve operational excellence faster. It connects tools, services, and teams into a unified platform that simplifies complexity and keeps critical information always up to date.

Developers gain quick access to ownership data, standards, and real-time statuses. Basically, Cortex helps organizations improve engineering outcomes without building and maintaining a custom platform themselves.

However, some users note gaps in RBAC and day-two operations like temporary permissions and ephemeral environments.

Key Features

• Developer onboarding automation: Automatically assign accounts, secrets, and onboarding tasks to new developers, which helps you speed up ramp-up time by a lot.
• Production readiness standards: Define and enforce production requirements for services, APIs, models, and more. The point is to make sure everything meets your standards before deployment.
• Self-service software scaffolding: Developers can create consistent, high-quality projects quickly through templated software scaffolding and one-click workflow automation.

What Makes It Different

Cortex stands out because it integrates deeply into the daily engineering workflows, constantly enforcing standards and giving developers clear, actionable insights. Unlike traditional DORA tools that only measure results, Cortex actively helps you achieve better outcomes because it embeds improvement into the system itself.

8. CTO.ai: Best for Streamlining DevOps Through Developer-Centric Workflows

CTO.ai is a DevOps as a Service platform designed to simplify and automate software delivery for engineering teams. It helps organizations integrate DevOps practices without the heavy lift of building complex internal platforms.

It provides containerized workflows, real-time analytics, and easy cloud deployment options. Plus, it makes it faster and easier for teams to ship code confidently while measuring performance with meaningful metrics. The platform is optimized for developer experience, and it offers a low-code environment that blends ChatOps, GitOps, and Infrastructure as Code (IaC).

Still, some users report that CTO.ai’s low-code approach and Slack-centric model may limit flexibility for more complex, large-scale enterprise needs. It's optimized for smaller, fast-moving teams, not heavy enterprise workflows.

Key Features

• Instant PR previews: Developers can instantly generate preview environments for pull requests without provisioning new servers.
• Slack-based deployment triggers: Teams can deploy applications directly from Slack, which brings DevOps workflows into everyday communication tools.
• Containerized CLI workflows: Create container-based command-line tools that integrate into your development workflow.

What Makes It Different

CTO.ai stands out because it deeply integrates event-driven workflows and communication platforms like Slack into the DevOps process. CTO.ai brings actionability into the tools developers already use, which helps teams move faster while still tracking meaningful metrics in real time.

9. Jira: Best for Managing Complex, Cross-Team Projects at Scale

Jira by Atlassian is one of the world’s most popular project management platforms. It was originally designed for tracking software development tasks and has now expanded into a full-service work management tool for all kinds of teams.

Jira offers flexible ways to organize, assign, and monitor tasks, using boards, lists, timelines, and customized workflows. It is widely used for agile project management, and it is a go-to solution for both startups and enterprise-level organizations needing structured work tracking.

Here’s the problem. Originally, Jira was built as an issue and bug tracker for software teams. Over time, Atlassian expanded it with plugins, templates, and marketplace add-ons to turn it into a full project management platform for all kinds of teams (agile boards, roadmaps, OKR tracking, etc.).

But fundamentally, at its heart, Jira is still an issue tracking system that evolved into broader project management.

That’s why it’s so strong for task-level granularity (stories, bugs, tasks, epics), but it can feel heavy or overengineered if you just want lightweight project management.

Key Features

• Goal alignment tracking: Easily map team tasks and projects to higher-level company goals.
• Dependency mapping across teams: Visualize project dependencies across multiple teams in one timeline view.
• Real-time collaborative editing: Allow multiple team members to edit tasks, stories, or projects simultaneously.

What Makes It Different

Jira’s biggest difference lies in its unmatched flexibility and marketplace integrations. Unlike typical DORA-focused tools that mainly track engineering performance, Jira creates a centralized hub where all teams (technical and non-technical) can plan, track, and connect work to business outcomes.

10. GitLab: Best for End-to-End DevSecOps and AI-Powered Delivery Pipelines

GitLab is an all-in-one DevSecOps platform that brings together source code management, CI/CD, security, and operations into a single application. It’s built to help teams deliver software faster and more securely by minimizing toolchain complexity and maximizing visibility across the entire development lifecycle.

Companies of all sizes, from startups to Fortune 100 giants, use GitLab to boost productivity, speed up releases, and strengthen their software supply chain. What sets GitLab apart is its strong focus on AI-driven workflows and full-cycle DevSecOps automation. This makes it easier for teams to collaborate without switching between disconnected tools.

On the downside, GitLab isn’t perfectly seamless for every use case. Some users report that for complex, highly customized workflows, GitLab can still require workarounds or extra setup.

GitLab’s AI (GitLab Duo) is strong but not as advanced yet as standalone tools like GitHub Copilot for pure coding (though GitLab integrates AI deeper into the lifecycle, which is your accurate point).

Key Features

• Integrated security testing: Automatically run security scans during the CI/CD pipeline without leaving the development workflow.
• Cloud-agnostic deployments: Deploy applications across any cloud provider without being locked into a specific vendor ecosystem.
• AI code suggestions: Speed up coding with GitLab Duo, which uses AI to recommend real-time code snippets based on the project’s context.

What Makes It Different

GitLab brings software development, security, and delivery into a single platform, minimizing toolchain sprawl. While it enables DORA-related improvements through built-in workflows, AI insights, and integrated security, its all-in-one approach may not fit teams needing more flexibility or granular control.

11. Datadog: Best for Unified Monitoring Across Applications, Infrastructure, and Security

Datadog is a comprehensive cloud monitoring and security platform that gives you end-to-end visibility into application performance, infrastructure health, and digital experiences. That means you can collect, analyze, and visualize large volumes of operational data in real time.

Consolidating logs, metrics, traces, and security signals into a single pane of glass is how you detect anomalies faster and optimize system performance proactively. In fact, Datadog integrates seamlessly with over 600 technologies, which makes it one of the most flexible observability platforms available.

However, Datadog’s pricing can become very expensive very quickly as you scale, especially with multiple modules (APM, logs, security, etc.). Users also sometimes report alert fatigue because of the high volume of signals if not tuned properly.

Key Features

• Continuous profiler: Identify resource-intensive code paths in live production environments without impacting performance.
• Synthetic monitoring: Simulate user transactions from various locations to catch issues before real users experience them.
• Universal service monitoring: Auto-discover and monitor services across your environment without requiring code changes.

What Makes It Different

Datadog stands out because it provides true end-to-end visibility across applications, infrastructure, logs, and security without heavy manual instrumentation. This gives you faster and more precise feedback for continuous improvement.

12. Typo: Best for Proactively Improving Developer Efficiency and Code Quality

Typo is a software engineering intelligence platform that can help you improve the speed, quality, and predictability of software delivery. And it does so through actionable insights and automation. It connects to your dev tool stack, unifies software development lifecycle (SDLC) data, and uses AI to highlight blockers, analyze code quality, and measure engineering impact in real-time.

The platform is good for developers and engineering leaders. You can use it to identify bottlenecks early and continuously optimize team performance, especially because it’s built around a developer-first mindset.

Still, Typo is relatively newer and less battle-tested at large enterprise scale compared to incumbents like GitLab, Datadog, or even Axify. Besides, some users may find its AI predictions less mature in highly complex, multi-repo environments.

Key Features

• Sprint delay prediction: Identify tasks and stories that are at risk of missing sprint goals with predictive analytics built into the workflow.
• Code hotspot detection: Automatically detect high-risk areas in your codebase that have frequent changes and quality issues.
• Developer health check-ins: Use conversational check-ins to measure developer satisfaction, stress levels, and productivity trends over time.

What Makes It Different

Typo stands out by combining predictive intelligence with developer-centric automation, not just highlighting issues but actively driving improvements within the day-to-day workflows. Apart from tracking delivery metrics, Typo proactively identifies sprint risks, code quality gaps, and team health signals.

13. Faros AI: Best for Enterprise-Scale Engineering Intelligence and Optimization

Faros AI is a comprehensive engineering intelligence platform designed to help organizations connect, analyze, and optimize their entire software development lifecycle. It brings together telemetry data from SDLC tools, HR systems, financials, and surveys to offer a unified view of engineering operations.

Faros also has built-in AI insights, so it’s great for tech leaders to remove bottlenecks, maximize efficiency, and align engineering output directly with business outcomes. It offers flexible data ingestion, compatibility with custom workflows, and strict security standards.

However, Faros AI’s setup and customization can be complex and time-consuming, especially for teams without strong internal data engineering support. We’d say it’s better suited for companies that already have mature ops, not for smaller or fast-scaling teams needing quick wins.

Key Features

• Engineering benchmarking: Compare your team's delivery speed, quality, and efficiency against industry peers using anonymized aggregate data.
• AI-guided decision support: Get smart recommendations on where to optimize investments and workflows based on real-time operational data.
• SDLC health monitoring: Continuously monitor the health of the full development lifecycle across tools and teams.

What Makes It Different

Faros AI stands out because it connects raw engineering data from virtually any source without forcing teams to overhaul their workflows. Unlike other DORA tools that operate as isolated reporting layers, Faros acts as an extensible business intelligence platform specifically built for engineering.

Look Beyond DORA Metrics

If you’ve read this far, you know the best DORA metrics tools. Each platform has its pros and cons, though, so choose wisely.

Of course, DORA metrics aren’t the only metrics that count.

Your team may also need to follow Agile metrics, like daily sprints, or you may want to track team morale.

In that case, Axify offers solutions for different teams and stakeholders.

If you’re a CIO, use Axify’s new executive dashboard to gain a high-level view of engineering performance and make informed decisions to drive strategic improvements. If you’re a software project manager, Axify is excellent for monitoring real-time project progress and team health. That’s how you can maintain both product delivery and team wellbeing.

While DORA metrics provide critical insights into your team’s DevOps performance, you need to integrate them with other key performance indicators and holistic metrics.

That’s the best way to better understand your team’s effectiveness and health and to work toward your business goals.

Book a virtual tour today and see how Axify can help.

FAQ

What is DORA in software?

DORA (DevOps Research and Assessment) is a research group that studies how software teams deliver code and improve performance. Their work led to key insights on what makes DevOps teams successful while focusing on speed, stability, and efficiency.

What are the 5 key metrics of DORA?

The five key DORA metrics are Deployment Frequency, Lead Time for Changes, Change Failure Rate, Time to Restore Service, and Reliability. These metrics measure how fast and reliably teams deliver software over a period of time.

Who uses DORA metrics?

Engineering managers, DevOps teams, product leaders, and CTOs use DORA metrics to track software delivery performance. They help teams spot bottlenecks, improve DevOps productivity, and build an action plan for better delivery outcomes.

What is the DORA framework?

The DORA framework is a research-backed model that connects DevOps practices to business performance. It shows how improving specific software delivery habits directly boosts organizational success, like profitability, speed, and market share.

What are the benefits of DORA?

DORA gives teams a clear way to measure performance, identify weak spots, and focus on high-impact improvements. Using DORA metrics leads to faster deployments, fewer failures, stronger recovery after incidents, and exciting opportunities for growth.