Debugging performance issues in Python servers can be challenging, especially when it is difficult to replicate the conditions under which the issues occur. The article discusses using flame graphs to continuously profile code, thereby identifying specific lines responsible for performance problems. CPU utilization is a critical metric for application performance, as it impacts both server costs and end-user experience. High CPU usage can lead to increased server expenses and slower application response times. Flame graphs help pinpoint which parts of the code contribute most to CPU usage, enabling targeted optimizations. The article illustrates this with an example using Pyroscope, an open-source continuous profiler, demonstrating how optimizing a function called foo() reduced CPU utilization by 66%, resulting in significant cost savings. By identifying and addressing bottlenecks, such tools allow for improved performance and cost efficiency.