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This topic explains what metrics are, why to use them, and where to find them in LaunchDarkly. LaunchDarkly metrics measure audience and application events that change in response to feature flag variations. LaunchDarkly metrics help you understand how different flag variation affects customer actions, application performance, or business outcomes. You connect metrics to experiments and guarded rollouts to evaluate impact, compare variations, or make release decisions based on regressions or other measured data. Metrics in LaunchDarkly can help you:
  • Measure how flag variations affect customer behavior or system performance
  • Detect regressions or unexpected effects that occur during a rollout
  • Apply consistent measurements across multiple experiments and guarded rollouts
  • Increase confidence in experiment and release results
Consistent use of metrics gives your organization a single, reliable source of truth for experimentation and release health.

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How to use metrics

You can connect metrics to several LaunchDarkly features:
  • Experiments: Evaluate how a flag variation affects conversion rate, latency, or engagement
  • Guarded rollouts: Track key performance and reliability metrics during progressive releases
  • Release policies: Assign key performance and reliability metrics to use for all guarded rollouts in your organization
LaunchDarkly records the version of each metric you attach to a running experiment or guarded rollout. If you update a metric’s aggregation method or analysis method while an experiment or rollout is in progress, LaunchDarkly continues to use the original aggregation or analysis method until the experiment or rollout completes. If you stop and restart an experiment or rollout, LaunchDarkly uses the latest versions of all attached metrics.

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Other metrics in LaunchDarkly

Other LaunchDarkly features, such as observability, use metrics to measure various application and system data collected over time. You might typically use these metrics for feature monitoring or in dashboard displays, but you cannot use them to create LaunchDarkly experiments or guarded rollouts. For example, the LaunchDarkly observability SDK plugins enable you to instrument OpenTelemetry metrics that record various service measurements in your application. You can view OpenTelemetry metrics on the Observability metrics page, or you can use them to create custom charts in Dashboards to view application performance measurements alongside other observability data. However, you cannot choose custom OpenTelemetry metrics as the basis for creating an experiment or guarded rollout.
LaunchDarkly autogenerates some OpenTelemetry metricsWhile custom OpenTelemetry metrics cannot be used in experiments or guarded rollouts, LaunchDarkly observability SDKs do automatically generate feature metrics for selected observability and OpenTelemetry events. These autogenerated metrics can be used in experiments and rollouts. To learn more, read Autogenerated metrics.
The topics in this section are focused solely on metrics that you can create and use for evaluating feature flag variations in experiments, guarded rollouts, or release policies. These metrics are created either from metric events stored in LaunchDarkly or a configured data warehouse, or from OpenTelemetry spans selected for trace-based metrics.

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How to manage metrics

You can manage metrics from the Metrics section of the LaunchDarkly user interface. From this section, you can:
  • View all metrics in a project
  • Create, edit, or archive metrics
  • Group related metrics for reuse
  • Review how metrics connect to experiments, rollouts, and dashboards
To learn how to browse, search, and filter metrics, read Using the Metrics list.

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Next steps

To learn more about using existing metrics, read: To create new metrics, read Creating metrics.