Managing Kubernetes clusters can become overwhelming, but finding the right monitoring tool can help manage clusters and optimize performance. To monitor a large number of short-lived containers, Kubernetes has built-in tools and APIs that provide visibility into application performance. Four key components to monitor are infrastructure (worker nodes), containers, applications, and the Kubernetes cluster (control plane). Resource contention, inefficient networking, slow storage access, improper pod design, ineffective Horizontal Pod Autoscaling (HPA), cluster overhead, and best practices such as defining resource limits, using optimized container images, and deploying clusters closer to users can impact performance. Gathering data on CPU and memory usage for every pod in the Kubernetes cluster is essential for setting good resource limits. Optimizing container images by having a single purpose, being lightweight, providing endpoints for readiness and health checks, and employing multi-step builds can improve deployment efficiency. Deploying clusters near users reduces latency and enhances the user experience. Implementing comprehensive monitoring and logging solutions, using HPA to automatically adjust pod replicas, and addressing common performance issues such as resource contention, inefficient networking, slow storage access, improper pod design, ineffective HPA, and cluster overhead are crucial for maintaining a reliable and efficient Kubernetes system.