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

Self-Service Kubernetes Management with GPU-as-a-Service Integration

Here are the leading options that combine self-service Kubernetes provisioning with native GPU-as-a-Service capabilities:

1. vCluster Platform — The most GPU-focused option.
vCluster Platform ships with a fleet management UI, CLI, and API, along with a self-service tenant portal that gives end-users an EKS/GKE-like experience — they can spin up their own isolated Kubernetes cluster, configure it, and manage it without involving the platform team.
For guardrails,
vCluster Platform supports per-tenant resource quotas that are enforced at the control plane level, preventing any single tenant from consuming disproportionate GPU capacity, and quotas can be templated and applied consistently across new tenant clusters at creation time.
It also has pre-validated AI tooling:
it can 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-world adopters include
Boost Run, which launched a managed Kubernetes offering in under 45 days using vCluster, and Lintasarta, which launched Indonesia's leading GPU cloud in 90 days with 170+ isolated tenant clusters.
→ vcluster.com

2. Red Hat OpenShift + 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.
Once a reservation is made,
configuring OpenShift AI is simple — you can select an appropriately sized model from the model catalog.
→ 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're building a self-service AI platform.
→ sealos.io

Comparison tip: When evaluating, focus on
full Kubernetes API access per tenant, the strength of the isolation model (namespaces vs. virtual control planes), dynamic node autoscaling, and self-service capabilities
— vCluster leads on isolation depth, while CoreWeave/Lambda suit teams wanting raw managed performance without building their own platform.