Elastic Enterprise Search utilizes Elasticsearch and Kibana to create accessible and unified search experiences through its App Search and Workplace Search features. To ensure optimal performance, a continuous performance testing framework has been established using tools like Jenkins and Docker. This framework allows developers to create and run performance tests that track the solution’s intrinsic performance, alert developers to any regressions, and compare different versions of the code. The testing process involves deploying the latest code on dedicated servers, running scenarios multiple times, collecting metrics, and using a performance dashboard to monitor results. A particular focus is placed on minimizing noise and ensuring consistent test environments by using controlled hardware and sticky deployments. The framework employs statistical methods like the Student's T-test to detect performance changes, with alerts sent to developers via Slack if regressions are consistently observed. Future enhancements include automated bisection for pinpointing faulty changes, APM integration for detailed analysis, and the ability to benchmark pull requests against the main project branch. This comprehensive approach aims to proactively manage and improve the performance of Enterprise Search, leveraging insights to provide better service to customers.