DORA Metrics — Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Mean Time to Recovery — to get a clear picture of how your engineering team is really performing.
Created by the DevOps Research and Assessment (DORA) team, now part of Google Cloud, these four metrics are used by companies of all sizes to uncover bottlenecks, track improvements, and justify decisions with data. But to get real value out of them, you need to use them strategically, not just operationally.
That’s what this guide is about: how to measure DORA Metrics properly and use each one as a lever to improve delivery.
Deployment Frequency: how often are you delivering value to production?
This is the most straightforward metric. It measures how many times per day, week, or month your team deploys code to production. And while it sounds simple, it says a lot.
If your team is deploying frequently, it usually means short cycles, lower risk, and faster feedback. If deployments take too long, there might be friction in code reviews, fear of breaking something, or lack of automation in the pipeline.
How to calculate it:
- Define the period (e.g., 10 days)
- Count how many deployments happened
- Divide the number of deployments by the number of days
If there were 20 deployments in 10 days, the average is 2 per day.
Benchmarks (State of DevOps Report):
- Low performance: less than once a month
- Medium: between once a month and once a week
- High: at least once a week or more
Lead Time for Changes: how long from first line of code to production?
This is one of the most revealing metrics for any technical leader. Lead Time for Changes measures the time between the first commit and when that change goes live in production.
If this time is too long, something is slowing your flow. It could be slow reviews, flaky staging environments, manual testing, or too much bureaucracy in the process.
How to measure it:
- Record the timestamp of the first commit
- Note the deployment date for the same change
- Subtract one date from the other
Example:
- Initial commit: January 1
- Deploy: January 5
- Lead Time = 4 days
Benchmarks:
- Low: more than one month
- Medium: between one week and one month
- High: less than one week
Want to dig deeper? Break down lead time into parts (e.g., time until PR is opened, time until merge, time until deploy) to find the actual bottleneck.
Change Failure Rate: how many production changes break something?
This metric shows the percentage of deployments that lead to rollbacks, hotfixes, or incidents in production. It’s essential for understanding how stable your releases are.
If your team is delivering fast but breaking often, speed isn’t the issue — quality is. This is the metric that forces you to look at test coverage, QA practices, code reviews, and requirement validation.
How to calculate it:
- Count all deployments in a given period
- Count how many caused problems
- Divide failures by total deployments and multiply by 100
Example:
- 20 deployments, 3 failures
- CFR = (3 / 20) × 100 = 15%
Benchmarks:
- Low performance: above 15%
- Medium: between 5% and 15%
- High: less than 5%
Mean Time to Recovery: when something breaks, how fast do you recover?
Failures happen. The question is: can your team bounce back quickly?
MTTR measures the average time it takes to restore service after a production incident. It’s a reflection of your operational resilience.
How to calculate it:
- List incidents in the period
- Record the start and end time of each incident
- Calculate the duration and find the average
Example:
- Incidents: 3
- Total duration: 135 minutes
- MTTR = 135 / 3 = 45 minutes
Benchmarks:
- Low performance: more than 1 day
- Medium: between 1 hour and 1 day
- High: less than 1 hour
A high MTTR usually means lack of observability, unclear incident response processes, or limited team autonomy during outages.
Why measuring these metrics the right way changes everything
Looking at these metrics on the surface doesn’t move the needle. The real value comes from using them to answer practical questions:
- Why are our deliveries slow?
- Why do we break production so often?
- Where should we invest to improve flow?
Good metrics aren’t just dashboard fillers. They guide decisions. And when used well, DORA Metrics help shift your team’s culture: from reactive to adaptive, from opinion-driven to data-informed.
Start small. Pick one metric. Measure it. Share it with the team. Improve. Repeat.
If you want help building a dashboard or applying benchmarks to your current setup, I’ve got your back.
FAQ: DORA Metrics in practice
Do I need to track all 4 metrics from day one?
No. Start with one or two that are easier to extract and more relevant to your team’s current challenges. Lead Time and Deployment Frequency are good starting points.
How often should I review these metrics?
It depends on your development cycle. Reviewing every sprint or biweekly is usually enough to spot trends without adding overhead.
Do these metrics work for small teams?
Yes. Small teams often benefit more because they can act faster on insights. Just avoid overreacting to short-term fluctuations.
How do I make sure these metrics aren’t used for micromanagement?
Be clear: the goal is to improve the system, not evaluate individuals. Share data openly, encourage conversations, not pressure. Culture matters more than dashboards.
