We run Datadog Continuous Profiler on every service in our testing and production environment, which gives us the insight we need to reduce the time it takes to diagnose issues and fix performance bottlenecks.
Site Reliability Architect at Cvent
Datadog Continuous Profiler gives us unparalleled visibility into resource allocations in production with low overhead, and has become a crucial tool for optimizing CPU and memory performance at Faire.
Staff Developer - Platform at Faire
Datadog Continuous Profiler analyzes code performance all the time and in any environment, including production, with negligible overhead. Quickly identify and optimize the most resource-consuming parts in your application code in order to improve MTTR, enhance user experience, and reduce cloud provider costs.
Pinpoint hard to replicate code issues that are invisible to other tools
- Continuously profile each line of code in any environment without affecting application performance and user experience
- Identify methods that are inefficient under production load, despite having performed well in pre-production environments
- Reduce end-user latency and infrastructure costs by resolving code-level bottlenecks
Method-level visibility into every request
- Tie every distributed trace to the methods and threads that executed the request
- Understand why requests took longer to execute and which ones consumed the most CPU and memory
- Determine the root cause of code issues with a breakdown of time spent by method on CPU, garbage collection, lock contention, and I/O
Automated code profiling insights, leveraging years of runtime expertise
- Derive actionable insights from an automatic heuristic analysis of the main problem areas in your code
- Surface runtime performance problems such as deadlocked threads, inefficient garbage collection, and memory leaks
- Apply suggested fixes to improve application efficiency, empowering new engineers to operate like seasoned veterans
Track every deploy and eliminate performance regressions
- Monitor code performance variations in production by applying long-term, code-level metrics to alerts and dashboards
- Compare code behavior and impact across hosts, services, and versions during canary, blue/green, or shadow deploys
- Isolate the most resource-heavy functions to quickly understand what is causing a spike and decide whether to roll back or ship a fix