Kubernetes, while transformative in application deployment and management, has led to significant resource waste due to overprovisioning, with clusters typically utilizing only 13-25% of CPU and 18-35% of memory. This inefficiency is costly, with annual waste per cluster ranging from $50,000 to $500,000, driven by systematic overprovisioning patterns that vary by workload type. Jobs and CronJobs, in particular, are among the worst offenders, wasting 60-80% of allocated resources due to unpredictable data volumes and a "set and forget" approach. StatefulSets and Deployments also contribute to waste through overallocation for growth and lack of dynamic scaling, respectively. Several root causes, including psychological factors like loss aversion and technical barriers such as inadequate monitoring, exacerbate the problem. However, through comprehensive optimization strategies such as resource profiling, autoscaling, and governance, organizations can achieve a 40-70% reduction in compute costs while enhancing application performance and resource efficiency. This requires a shift towards treating resource optimization as a continuous practice supported by proper monitoring and processes.