Orchestrate AI Workloads at Scale.
Fully managed Kubernetes clusters designed for AI-native applications — enabling seamless deployment, scaling, and management of containerised workloads.
Core Capabilities
Fully Managed Clusters
Deploy and manage Kubernetes clusters without handling control plane complexity or infrastructure operations
Elastic Auto-Scaling
Automatically scale compute resources up or down based on workload demand for optimal performance and cost efficiency
GPU-Native Orchestration
Run AI and ML workloads on GPU-enabled nodes with high-performance networking and distributed scheduling
Integrated AI Ecosystem
Deploy inference engines, ML frameworks, and Kubernetes-native tools from pre-configured environments
Use Cases for Kubernetes
Flexible GPU infrastructure designed to support development, scaling, and deployment of modern AI workloads.
Distributed Training Workloads
Run large-scale training jobs across multiple GPU nodes with efficient resource scheduling.
Inference at Scale
Serve models in real time with load balancing and auto-scaling across clusters.
MLOps & Pipelines
Manage end-to-end ML workflows, from experimentation to deployment, within a unified platform.
Microservices & APIs
Run containerised applications and AI-powered services with high availability and scalability.