Organizations often experience unexpected high cloud bills as they scale their Kubernetes deployments, which can be managed by implementing cost optimization strategies. These strategies include defining workload requirements accurately, choosing appropriate instance types, and leveraging storage and spot instances effectively. AWS serves as an example, though the principles apply to other providers like Google Cloud and Azure. Tools such as AWS Cost Explorer, Kubernetes Persistent Volumes, and CNCF projects like KEDA and Prometheus assist in resource management. Emerging trends in cost optimization include AI-driven adjustments, serverless Kubernetes, and automation tools like CAST AI and Kubecost, which help organizations optimize cloud investments while maintaining performance and scalability. Regular monitoring and strategic planning are essential for ongoing cost efficiency in cloud-native environments.