New in Together GPU Clusters: Autoscaling, observability, and self-healing

AI infrastructure has seamlessly transitioned into production environments as teams manage workloads that rapidly scale from single-node prototypes to distributed systems utilizing hundreds of GPUs. This shift necessitates advanced management tools to handle unpredictable traffic spikes and potential hardware failures, which can disrupt training processes. To address these challenges, Together GPU Clusters introduces significant enhancements such as autoscaling, Role-Based Access Control (RBAC), full-stack observability, and self-serve node repair, integrated into the core cluster experience. These features, driven by the Kubernetes Cluster Autoscaler, allow for dynamic GPU capacity management, ensuring performance without excessive costs. The platform's observability tools, including a dedicated Grafana instance, provide comprehensive telemetry for performance monitoring and cost efficiency. By incorporating robust access controls and active health checks, Together GPU Clusters enable organizations to confidently transition from experimental to operational AI systems, supporting diverse internal stakeholders and aligning resource allocation with real-time demands.