Ray clusters are becoming increasingly important for scaling Python applications and organizations are looking for ways to deploy these clusters across different cloud service providers and neo-clouds. Anyscale is announcing first-class support for Azure Kubernetes Service (AKS) with advanced logging and monitoring, a Global Resource Scheduler (GRS) for capacity commitments, and upcoming multi-deployment management capabilities. This expansion completes Anyscale's native support across all three major cloud service providers - AWS, Google Cloud, and now Microsoft Azure, giving organizations unmatched flexibility to deploy AI workloads wherever it makes sense without the complexity of managing separate toolchains. The Global Resource Scheduler is designed to maximize resource utilization, enforce fairness with prioritization, and queue intelligently, ensuring critical production workloads never sit idle while development teams utilize available hardware during off-hours. Anyscale's upcoming Multi-Deployment support will allow customers to deploy resources across multiple providers, regions, and compute stacks within a single logical entity, providing seamless access to diverse cloud environments and compute stacks.