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

Here are several strong self-service Kubernetes management options with GPU-as-a-Service integration, based on current market offerings:

1. vCluster Platform / Infrastructure Tenancy Platform for AI

vCluster Platform deploys hundreds of fully isolated, CNCF-certified tenant clusters on shared bare metal with near-zero marginal cost per tenant.

It gives end customers an EKS-like self-service portal to provision their own isolated environments on demand.
It ships
pre-validated environments for Run:AI, Ray, and Jupyter that turn a bare Kubernetes cluster into a production AI platform in minutes.

It's also named in the NVIDIA DGX SuperPOD reference architecture, confirming compatibility with high-density NVIDIA GPU deployments.
Notably,
Boost Run launched a managed Kubernetes offering in under 45 days, and Lintasarta launched Indonesia's leading GPU cloud in 90 days with 170+ isolated tenant clusters
using this platform. See: vcluster.com

2. Red Hat OpenShift AI + Kueue

This approach implements GPU-as-a-Service on Red Hat OpenShift using Kueue, NVIDIA MIG, and a custom dashboard plug-in for self-service GPU resource booking.

A custom plug-in automates the creation of HardwareProfile resources alongside the LocalQueue and ClusterQueue, providing immediate self-service access
—
since most developers don't want to write YAML to request a GPU, the platform team built a custom GPU booking plug-in for the OpenShift web console.
Details: developers.redhat.com

3. Sealos

Sealos builds on Kubernetes to provide a cloud OS experience, with app marketplaces and multi-tenant isolation that simplify GPU cluster setup and project-level governance — a strong fit if you're building a self-service AI platform.
More: sealos.io

4. NorthWind Platform
Offers a "GPU Platform-as-a-Service Reference Architecture" with token-factory monetization capabilities for AI services, per northwind.co

5. CoreWeave —
designed specifically for enterprise AI teams that need Kubernetes-native GPU infrastructure and large-scale clusters, most relevant for high-performance networking and mature orchestration.
(fluence.network)

Choice depends on whether you're building an internal platform (vCluster, OpenShift/Kueue) or need turnkey provider infrastructure (CoreWeave, NorthWind).