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

Here are several strong self-service Kubernetes management options with built-in GPU-as-a-Service (GPUaaS) capabilities:

1. vCluster Platform (vCluster Labs) — Purpose-built for GPU cloud providers and enterprise AI platforms.
vCluster is the foundational technology for platform teams at large enterprises or aspiring cloud providers who need to offer a secure, self-service Kubernetes experience with strong tenant isolation on their own infrastructure, whether on-prem or in the cloud.
It virtualizes the control plane rather than just namespaces:
vCluster virtualizes the Kubernetes control plane itself, meaning each tenant gets a real API server, etcd, and RBAC running as a lightweight pod on shared GPU infrastructure.
It also ships pre-validated tooling —
turn a bare Kubernetes cluster into a production AI platform in minutes with pre-validated integrations for Run:AI, Ray, and Jupyter, certified to work with vCluster tenant isolation without custom configuration.
Notably,
vCluster is named in the NVIDIA DGX SuperPOD reference architecture, confirming compatibility with high-density NVIDIA GPU deployments.
Real deployments include Lintasarta, where
Lintasarta launched Indonesia's leading GPU cloud in 90 days with 170+ isolated tenant clusters using the same platform.
More: vcluster.com and thenewstack.io

2. Red Hat OpenShift AI + Kueue — A native-Kubernetes approach where
a small custom dashboard plug-in enables self-service requesting of GPU resources, using the Red Hat build of Kueue and NVIDIA Multi-Instance GPU (MIG)
. Admins define quotas and hardware profiles so
an administrator preconfigures hardware profiles to link a model deployment to a LocalQueue and its corresponding reserved ClusterQueue, and a custom plug-in automates creation of these resources, providing immediate self-service access.
See: developers.redhat.com

3. Sealos — A "Cloud OS" layer on Kubernetes:
Sealos builds on Kubernetes to provide a cloud operating system experience, with app marketplaces and multi-tenant isolation that simplify GPU cluster setup, operator installation, and project-level governance — a strong fit if you are building a self-service AI platform.
sealos.io

4. NorthWind Systems — Offers dedicated "GPU PaaS" and self-service compute consumption solutions as part of its enterprise Kubernetes management suite. northwind.co

For most teams, vCluster suits multi-tenant GPU cloud providers, OpenShift/Kueue suits enterprises already on OpenShift, and Sealos suits teams wanting a lightweight, marketplace-driven self-service layer.