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⇱ DORA Metrics

For AI agents: A markdown version of this page is available at https://docs.datadoghq.com/delivery_performance/dora_metrics.md. A documentation index is available at /llms.txt.

DORA Metrics

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Overview

DevOps Research and Assessment (DORA) metrics are four key metrics that indicate the velocity and stability of software development.

Deployment frequency
How often an organization successfully releases to production.
Change lead time
The amount of time it takes a commit to get into production.
Change failure rate
The ratio of deployments that fail and require immediate intervention.
Failed deployment recovery time
The time it takes to recover from a deployment that fails and requires immediate intervention.

Defining and tracking DORA metrics can help you identify areas of improvement for your team or organization’s speed and quality of software delivery.

Set up DORA Metrics

To start configuring data sources to send deployment events to Datadog, see the Setup documentation.

Analyze DORA Metrics

After you’ve set up the data sources for your deployment and failure events, navigate to Software Delivery > Delivery Performance > DORA Metrics to identify improvements or regressions for each metric. You can also aggregate the metrics by team, service, repository, environment, time period, and custom tags to compare trends over time.

Click View Deployments to open a new tab with the list of deployment events.

Click View Change Failures to open a side panel with the list of deployment events marked as change failures.

Use DORA Metrics data

Export DORA Metrics widgets

Export your visualization widgets to dashboards, notebooks, or add them to existing incidents.

Click the Export icon on any visualization to add it to an incident, dashboard, or notebook. For more information about the metrics calculated by DORA Metrics, see the Data Collected documentation.

Create custom dashboards

Build custom dashboards using DORA Metrics to analyze your delivery workflow end-to-end, from commits and pull requests to production deployments. For example, compare code review performance across teams to identify which teams are blocked by slow approvals and prioritize where to invest in workflow improvements.

Within dashboards and graphs, custom tags are treated as attributes. To filter or group by a custom tag, it must be prefixed with an @ symbol.

Further Reading