LinkedIn has internally developed a generic anomaly detection platform for time series metrics called ThirdEye. In this talk, we describe our experience on-boarding client performance data (RUM) for LinkedIn pages and apps onto ThirdEye. We also discuss our previous generation anomaly detection system built specifically for performance use-case and lessons learned from it. We give an overview of ThirdEye, focusing on how to build a low-cost, end-to-end system that can leverage any algorithm, and explain lessons learned and best practices that will be useful to any engineering or operations team. One key insight for audience will be how and why a perfect anomaly detection system cannot exist.