Launched: Datadog Integration

LaunchDarkly + Datadog logos

LaunchDarkly makes it easier for teams to deploy features more confidently by doing so with flags, thus reducing the associated risks in production. Teams that use Datadog to monitor the performance of their systems can now use LaunchDarkly’s Datadog integration to visually correlate and understand how feature deployments impact their application and infrastructure metrics.

This integration pushes events from LaunchDarkly and overlays them in the Datadog Events dashboard to improve visibility and context of changes across all systems. This makes it easier to detect and resolve harmful changes to system performance as a result of feature flags that are turned on or off.

For example, if a flag is enabled that causes a service to significantly slow down, the DevOps team will now have the LaunchDarkly context inside their Datadog dashboards – enabling them to understand what triggered the issue.

Guillame Clochard, an engineer from iAdvize, recently enabled the integration for his team. Here’s what he had to say:

“The LaunchDarkly Datadog integration helped us monitor precisely how our flags change and, by creating alerts in Datadog, we are able to notify the corresponding team if a critical operational flag is modified.”

We are confident the Datadog integration improves the ability for teams to monitor and control the health of their production systems while enabling continuous delivery. To learn more about this integration and get started, check out our documentation. If you have questions about LaunchDarkly, contact or start a free trial today!

Karishma Irani
Karishma is a Principal Product Manager at LaunchDarkly, prior to which she launched and managed the infrastructure monitoring product at New Relic. For 3+ years, she helped enterprise customers make monitoring a seamlessly integrated part of their scaling organizations. At LaunchDarkly, she continues to help teams address their biggest DevOps challenges by encouraging teams to 'Test in Production' using good feature management and experimentation practices. When she's not building or breaking products, she’s drinking coffee, until it’s a socially acceptable hour to drink wine.