Kubernetes resource management, specifically CPU and memory limits and requests, are crucial for ensuring cluster stability, performance, and cost optimization. Setting these values helps balance performance and cost-efficiency, but incorrect settings can lead to issues like overprovisioning, Pod eviction, or running out of memory. To set optimal values, it's essential to identify current usage patterns, gather historical data, and apply headroom for highly available production apps. Additionally, using tools like CAST AI's Workload Autoscaler and monitoring cluster performance can help ensure the right resource allocation, reducing waste and costs.