Kubernetes clusters generate huge amounts of metrics, logs, and traces even when nothing is actually wrong. I wanted to see whether anomaly detection could help separate real problems from background noise. To test this, I built a small Kubernetes cluster and intentionally broke it in different ways. The results were mixed: some failures were detected early, while normal behavior often triggered false alarms. This lightning talk shares what anomaly detection is good at, where it struggles, and what DevOps teams should realistically expect when using it in Kubernetes environments.