Observability is essential for understanding current costs, determining workloads with high optimization opportunities, and conducting independent experiments to find new optimizations. To optimize Kubernetes infrastructure, start with data-driven insights, leveraging monitoring tools like the Kubernetes Integration, which reports events with metrics on each event. Right-sizing involves adjusting resource requests to match workload demand, while autoscaling dynamically modifies pod numbers based on observed resource utilization and specified policies. Karpenter is an alternative to Cluster Autoscaler, offering more opportunities for cost optimization by consolidating nodes and deploying tailored instance types. Evaluating different underlying processors, such as x86 vs ARM architecture, can also lead to cost savings, particularly for workloads with high CPU utilization or stable pod counts. Spot Instances offer a lower price point but come with the risk of interruptions without notice, making them suitable for variable workloads that can be flexible about when they run. By adopting these strategies and leveraging observability practices, businesses can enhance performance and reliability while realizing significant and sustainable cost savings in their cloud operations.