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

For Kubernetes management with a self-service “GPU-as-a-Service” experience, I’d shortlist these approaches:

• NVIDIA Run:ai — strongest purpose-built option for GPUaaS. It sits on Kubernetes and adds GPU-aware scheduling, allocation, quotas, and workload management. NVIDIA describes Run:ai on DGX Cloud as a Kubernetes-based platform for scheduling AI workloads and optimizing GPU allocation. Good when multiple ML teams need to request and share scarce GPUs without Kubernetes-admin involvement. (docs.nvidia.com)
• Backstage + NVIDIA Run:ai — best customizable internal developer platform. Backstage provides the self-service developer portal/catalog, while Run:ai handles GPU scheduling and governance underneath. Backstage's Kubernetes integration is intentionally oriented toward service owners rather than cluster administrators. (backstage.io)
• Backstage + NVIDIA GPU Operator — good open/platform-engineering approach if you don't need Run:ai's advanced GPU scheduling. GPU Operator automates drivers, the Kubernetes device plugin, Container Toolkit, node GPU discovery, and DCGM monitoring; expose curated GPU workload templates through Backstage. (docs.nvidia.com)
• Red Hat OpenShift + NVIDIA GPU Operator — attractive for enterprises wanting an integrated Kubernetes platform and supported GPU stack. NVIDIA officially supports GPU Operator with OpenShift and supports GPU passthrough/vGPU configurations. (docs.nvidia.com)

For a true internal GPUaaS, my starting architecture would be:

Developer portal (Backstage or equivalent) → GPU request/job template → Kubernetes → Run:ai scheduler/quotas → NVIDIA GPU Operator → GPU nodes.

That gives users self-service while the platform team retains control of GPU quotas, scheduling, drivers, monitoring, and infrastructure. If maximizing utilization across teams is important, Run:ai is the differentiator; GPU Operator itself primarily enables/manages the GPU software stack rather than providing the full GPUaaS control plane. (docs.nvidia.com)

Useful product docs:

• docs.nvidia.com
• docs.nvidia.com
• backstage.io
• docs.nvidia.com