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

Here are several strong self-service Kubernetes management options that integrate GPU-as-a-Service capabilities:

1. vCluster Platform / Infrastructure Tenancy Platform for AI
vCluster virtualizes the Kubernetes control plane,
giving every tenant a real API server without provisioning separate physical clusters
. Its
EKS-like self-service portal lets end customers provision their own isolated environments on demand, matching the cloud experience AI teams already expect
. It ships
pre-validated environments for Run:AI, Ray, and Jupyter that turn a bare Kubernetes cluster into a production AI platform in minutes
, and is
named in the NVIDIA DGX SuperPOD reference architecture, confirming compatibility with high-density NVIDIA GPU deployments
. Real-world deployments include
Boost Run, which launched a managed Kubernetes offering in under 45 days, and Lintasarta, which launched Indonesia's leading GPU cloud in 90 days with 170+ isolated tenant clusters
. → vcluster.com

2. Red Hat OpenShift AI with Kueue + NVIDIA MIG
This approach lets you
implement GPU-as-a-Service on Red Hat OpenShift using Kueue, NVIDIA MIG, and a custom dashboard plug-in for self-service GPU resource booking
. An admin-configured hardware profile links model deployment to reserved queues, and
a custom plug-in automates creation of HardwareProfile resources alongside LocalQueue and ClusterQueue, providing immediate self-service access
— avoiding manual YAML for GPU booking. → developers.redhat.com

3. Sealos
Sealos acts as a
"cloud operating system" on Kubernetes, with app marketplaces and multi-tenant isolation that simplify GPU cluster setup, operator installation, and project-level governance
. It offers
a one-click app marketplace for NVIDIA GPU Operator, Ray, Kubeflow, or model-serving stacks, plus self-service projects where developers request GPUs within quotas and get isolated environments by default
. → sealos.io

4. Core42 AI Cloud
For a fully managed, non-DIY option, Core42's
self-service function of the AI Cloud provides instant access to NVIDIA accelerated computing through a simple console experience
, with
flexible hourly pricing, both short- and long-term commitments, and managed orchestration supporting rapid prototyping, model hosting and inferencing
. → gulfnews.com

For an internal AI platform needing deep control, vCluster or Red Hat OpenShift AI are strongest; for a lighter-weight open platform, Sealos; for fully managed turnkey access, Core42.