Karpenter has emerged as a favored choice for teams seeking alternatives to the Kubernetes Cluster Autoscaler, offering a straightforward deployment and configuration process that enhances autoscaling responsiveness. While Karpenter excels in node provisioning, the demands of growing environments necessitate further optimization and visibility, particularly in workload resource usage, cost management, and reliable scaling under real-world conditions. Cast AI for Karpenter is designed to complement rather than replace Karpenter by adding features such as advanced node selection, workload optimization, live container migration, and improved cost visibility. This integration facilitates improved performance, stability, and cost efficiency without altering the core autoscaling strategy. Cast AI's machine learning models analyze workload behavior, predict resource needs, and recommend optimization actions, enhancing Karpenter's capabilities. This approach aids in maintaining efficient cluster utilization, ensuring stable Spot usage, and providing clear insights into resource costs. The introduction of agentic AI capabilities and other advanced features within Cast AI promises further automation and optimization, thereby allowing teams to operate clusters with increased efficiency and confidence. Early access to Cast AI for Karpenter, which includes these enhancements, begins at AWS re:Invent on December 1, offering DevOps engineers, SREs, and platform engineers advanced tools for cluster management.