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vCLUSTER X KUBEFLOW

Unlock Better GPU Utilization and Tenant Isolation for Kubeflow

Supercharge your AI/ML workloads by pairing Kubeflow’s powerful GPU orchestration with vCluster’s Kubernetes-native multi-tenancy. Maximize node usage, improve tenant autonomy, and simplify management.

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Not All Multi-Tenancy Models Are Created Equal

Teams using Kubeflow typically choose one of three approaches to isolate workloads and manage infrastructure. Only one model delivers both optimal GPU utilization and strong tenant autonomy.

Namespaces
Single-Tenant Clusters
vCluster
Great on paper but serious limitations in autonomy and security.

In the default Kubeflow model, tenants are assigned namespaces within a single shared Kubernetes cluster. This leads to impressive GPU utilization (often >90%)—but creates issues for security, autonomy, and flexibility:

  • Weak tenant isolation: Shared control plane means no clear boundary between tenants.
  • Limited autonomy: Tenants can’t self-manage Kubeflow projects or other workloads.
  • CRD restrictions: Difficult or impossible for tenants to install or manage custom resources.

Bottom Line: You’re maximizing hardware use, but compromising on platform flexibility and security.

Strong isolation but at the cost of wasted resources and complexity.

Some teams opt for complete cluster separation using hosted control planes such as RedHat HyperShift or Kamaji. While this improves isolation it’s a step backward in terms of efficiency and resource utilization:

  • Fragmented node pools: GPU schedulers like Kubeflow can’t optimize across all nodes.
  • Underutilized hardware: Some tenants hit resource ceilings while others leave GPUs idle, leading to waste and inefficient spend.
  • Operational burden: Managing multiple clusters increases cost and complexity.

Bottom Line: You gain isolation but lose shared efficiency and scheduling power—defeating one of Kubeflow’s biggest benefits.

The best of both worlds: strong isolation and high node utilization.

vCluster introduces a smarter model: isolate tenants at the Kubernetes control plane level while keeping them on a shared cluster. This model integrates seamlessly with Kubeflow and unlocks powerful advantages:

  • Optimal GPU scheduling: Kubeflow can still orchestrate workloads across all GPUs.
  • Full tenant autonomy: Each tenant gets its own virtual control plane to manage CRDs, projects, and policies.
  • Strong isolation: vCluster supports both control plane and data plane isolation.

Bottom Line: You keep the efficiency of shared scheduling while gaining the security and flexibility of true tenant isolation.

vCluster Isn’t Just for Kubeflow—
It Solves Multi-Tenancy for All K8s Workloads

By virtualizing Kubernetes itself, vCluster provides a future-proof architecture that works across all platforms, tools, and environments. Whether you use Kubeflow today or add new tools tomorrow, vCluster has you covered.

Works with any orchestration tool

Including KubeFlow, Run:ai, Volcano, Argo Workflows, Ray, and more.

Certified Kubernetes distro

No need to re-architect your workloads or tools.

Fully open-source

No lock-in, flexible deployment, and a vibrant ecosystem.

Cloud-agnostic

Compatible with private data centers and public cloud providers.

Combine Kubeflow with Strong, Kubernetes-Native Isolation

vCluster helps platform teams scale faster, isolate better, and use every GPU more efficiently. See it in action.

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