Scalability and reliability are key factors, especially when handling millions of daily orders. Load testing is essential for ensuring smooth performance under growing demands and holiday spikes. However, manual analysis of results is time-consuming and often incomplete, overlooking critical factors like latency, resource usage and small deviations, which looks negligible from the quick glance. For example, memory leaks in the Golang program.

In this session, you'll learn how we delegated load test analysis by integrating AI into our workflow. By automating result evaluations, AI analyzes complex datasets, going beyond if-else conditions. Our system now delivers actionable insights, identifying issues like memory leaks and CPU inefficiencies, along with improvement suggestions based on benchmarks.

Discover how this AI-powered approach helps us meet resilience and performance goals, scaling from average load to 4x peak demand within minutes, while maintaining p95 under 150 milliseconds.