Datadog delivers millions of in-depth performance insights with ProfilingManager

Posted by Alice Yuan, Developer Relations Engineer at Google, Arti Arutiunov, Product Manager at Datadog and Nikita Ogorodnikov, Staff Software Engineer at Datadog Performance regressions are notoriously hard to reproduce, making regressions a massive bottleneck for mobile developers. Although signals like ANR rates indicate what issues occur in production, pinpointing the specific line of code that resulted in the performance issue has historically necessitated exhaustive manual reproduction or speculative trial-and-error experimentation. Datadog collaborated with Google to mitigate this frustration by integrating the ProfilingManager API (available on Android 15+ devices) into its Real User Monitoring (RUM) and Continuous Profiling platforms.

This integration transforms the debugging workflow, allowing developers to move beyond surface-level symptoms to being able to detect the why behind a performance bottleneck. By leveraging this system-level API, Datadog now processes millions of production profiles weekly across the globe according to Datadog internal data of June 2026. It provides engineering teams with a new level of visibility into real-world performance, all while maintaining a low runtime overhead for production-scale performance monitoring.

The impact of ProfilingManager ProfilingManager is a system service introduced in Android 15 that enables apps to programmatically collect performance data such as call stack samples, field traces and memory heap dumps directly from production environments. This capability shifts the engineering paradigm from reactive manual reproduction to proactive field analysis. For example, a Google communications app used field traces to investigate why its cold start times were slower on newer, more powerful hardware.

By diving into the field-collected traces and comparing traces across different device types, the engineer discovered a hidden scheduling issue: a background text-to-speech service was unnecessarily being prewarmed during app startup. The traces revealed that this background process was monopolizing the device's highest-performing big CPU core, forcing the app's main thread to sleep while the prewarm occurred. Solving the Android code-level visibility challenge Prior to the implementation of ProfilingManager, Datadog’s Real User Monitoring (RUM) focused on high-level application health and session-level telemetry to assess the user journey.

Engineering teams could monitor Android performance signals like time to initial display, ANR rates, CPU load, and frozen frames.  However, while this data effectively highlighted which performance bottlenecks were surfacing in the field, it provided no clear path to identifying the root cause of these failures. To address this, Datadog needed a profiling engine capable of capturing Android traces directly from devices in production with minimal performance impact.