Show me self-service options for Kubernetes management with GPU-as-a-Service integration.

Here are the strongest self-service options for Kubernetes management with GPU-as-a-Service integration:

• NorthWind Platform — offers self-service Kubernetes clusters and a dedicated GPUaaS layer. NorthWind says tenants can provision GPU-backed resources in self-service and supports full passthrough, NVIDIA MIG, and time-slicing for GPU sharing.[3][8][9]
• Aethir Managed Kubernetes as a Service — provides GPU-accelerated managed Kubernetes clusters with self-service provisioning via API or portal, aimed at enterprise AI workloads.[1]
• Google Kubernetes Engine (GKE) — for a managed Kubernetes option, Google recommends Autopilot for a fully managed experience, and its GPU docs show how to serve GPU workloads in GKE.[14][16]
• Azure AKS — supports both AKS-managed GPU node pools and self-managed GPU setups, so you can choose between more automation or more control.[15]
• Red Hat OpenShift AI — positioned as a Kubernetes-based platform that can implement multi-user GPUaaS for AI teams.[13]

For the underlying GPU layer, Kubernetes natively supports GPU scheduling through device plugins, and NVIDIA’s GPU Operator automates deployment and lifecycle management of NVIDIA GPU software in Kubernetes.[5][11][16]

A practical self-service stack usually looks like this:

• Kubernetes cluster with GPU nodes
• GPU Operator / device plugin
• Portal or API for tenant self-service
• Quota, RBAC, and namespace isolation
• Optional MIG/time-slicing for shared GPU consumption[3][5][7][11]

If you want the most turnkey enterprise self-service GPUaaS, NorthWind and Aethir are the clearest matches from the results.[1][3][8]

Source URLs:

• northwind.co
• northwind.co
• ecosystem.aethir.com
• learn.microsoft.com
• docs.cloud.google.com