As an expert in Platform-as-a-Service (PaaS), I have observed that organizations frequently waste over 70% of their Kubernetes resources due to over-provisioning. One highly effective technique for significantly reducing cost per workload without compromising Service Level Objectives (SLOs) is the implementation of dynamic bin-packing using advanced auto-provisioners like Karpenter. Unlike the standard Kubernetes Cluster Autoscaler, which is restricted to predefined node groups, Karpenter evaluates the specific resource requirements of pending pods and provisions the most cost-efficient instance types in real time. This approach minimizes "slack" the gap between requested and actual resource usage, allowing workloads to maintain high performance while the infrastructure footprint remains as lean as possible. Rightsizing at the Namespace Level: Implementing rightsizing at the namespace level is achieved by aligning pod resource requests with actual historical usage, typically using the 90th or 95th percentile (p90/p95) of utilization. In practice, this is managed through a "recommendation-first" workflow using tools like Vertical Pod Autoscaler (VPA) or Goldilocks, which analyze usage patterns and suggest optimal CPU and memory settings for each container. To ensure SLOs are protected, these recommendations are often integrated into CI/CD pipelines as guardrails, preventing the deployment of workloads that exceed a specific request-to-usage ratio. Cloud Cost Anomaly Detection: Anomaly detection at the namespace level involves establishing a dynamic rolling baseline based on historical spending data for specific dimensions such as service, region, and namespace. Modern FinOps tools like AWS Cost Anomaly Detection or Kubecost score daily or hourly spend against this baseline, triggering alerts when costs deviate by a set percentage or dollar amount. The Proof of Success: The single most effective metric to prove these optimizations are working is Cluster Resource Utilization (CRU), specifically the ratio of Total Requested vs. Total Capacity. A successful rightsizing and anomaly detection program will show this ratio tightening over time, with a significant reduction in the cost-per-request or "idle cost" per namespace. Organizations applying these techniques typically report cost reductions ranging from 25% to 80% within just a few months while maintaining or even improving application stability