Nylas achieved an 80% reduction in CPU usage across its infrastructure by implementing a lightweight profiling strategy that allows for continuous monitoring in production. As a developer platform providing APIs for email, contacts, and calendar integration, Nylas relies heavily on its Sync Engine, a large Python application, to maintain high performance and uptime for its clients. The key to optimization was the use of both detailed benchmarking and lightweight statistical profiling, which involved sampling the application call stack periodically to identify major areas of slowness. This approach, though less granular, provided manageable data with minimal overhead, allowing for real-time insights into CPU usage without the performance drawbacks of traditional profilers. By exposing profiling data through HTTP and aggregating it with a collector agent, Nylas was able to visualize CPU time distribution and identify inefficiencies, leading to significant optimizations. The success of this strategy highlights the importance of effective measurement and instrumentation in maintaining service performance.