Datadog's Kubernetes Cluster Autoscaler, currently in limited preview, addresses the challenge of overprovisioning and underutilization in Kubernetes infrastructures by offering automated solutions to optimize compute resources and reduce cloud costs. By simulating existing clusters, it provides recommendations for cost-efficient node configurations and enables automatic workload migration through managed integrations like Karpenter or GitOps solutions. The tool leverages Datadog's Kubernetes observability to align node capacity with real workload behavior, ensuring performance and availability are not compromised. It also considers constraints such as node and pod affinities, taints, and disruption budgets, offering insights into potential savings and workload impacts before changes are made. For GPU-backed AI and ML workloads, it provides specific recommendations to match actual usage patterns. The system supports both live node scaling and GitOps workflows, allowing organizations to update node configurations efficiently while maintaining application performance.