vCluster is a Kubernetes virtualization platform designed to create fully functional virtual clusters within existing namespaces. This tool allows users to run multiple isolated Kubernetes environments on a single host cluster, enhancing multi-tenancy and isolation while reducing costs compared to separate full clusters.
Key Features:
Isolation and Multi-Tenancy: Each vCluster operates in its own namespace, providing strong isolation for enhanced security and resource management.
Cost-Effective: Reduces infrastructure expenses by utilizing shared resources instead of dedicated clusters.
Security and Isolation: Offers granular permissions and an isolated control plane, minimizing risks associated with privileged access.
Flexibility in Environments and Storage: Supports various Kubernetes versions and distributions, along with adaptable backing stores like SQLite or etcd for scalability needs.
Scalability Enhancements: Reduces API server load through independent management of CRDs within each cluster.
Audience & Benefit:
Ideal for platform engineers, DevOps teams, cloud providers, and organizations requiring scalable Kubernetes solutions without high infrastructure costs. vCluster enables secure, efficient multi-tenant deployments, allowing teams to manage resources independently with reduced complexity and overhead.
This tool is installed via winget, offering a seamless setup process.
CNCF Certified Kubernetes — Distribution · Kubernetes AI Conformant
What is vCluster?
vCluster creates Tenant Clusters — fully isolated Kubernetes environments that run on top of a Control Plane Cluster or standalone on dedicated infrastructure or bare metal. Each tenant gets its own API server, CRDs, and RBAC, with a cluster experience indistinguishable from a dedicated Kubernetes cluster.
Built for production. Trusted in production. 40M+ Tenant Clusters deployed by teams at Adobe, CoreWeave, NVIDIA, Lintasarta, Atlan, Deloitte, and hundreds of AI clouds, AI factories, and Fortune 500 platform organizations.
and — every Tenant Cluster is upstream Kubernetes with no vendor lock‑in, validated for portable AI/ML workloads (training, inference, agentic).
> The public-cloud experience, on your own infrastructure. Give every team the Kubernetes they need — with strict isolation, hardware-aware scheduling, and zero tenant sprawl — whether you run one region or 100K GPUs.
🚀 Quick Start
# Install vCluster CLI
brew install loft-sh/tap/vcluster
# Create a Tenant Cluster
vcluster create my-vcluster --namespace team-x
# Use kubectl as usual — you're now in your Tenant Cluster
kubectl get namespaces
Prerequisites: A running Kubernetes cluster and kubectl configured.
No Kubernetes cluster? Run vCluster directly on Docker with vind (vCluster in Docker) — like kind, but with the full vCluster feature set (UI, sleep/resume, LoadBalancer, image cache):
vcluster create my-vcluster --driver docker
kubectl get namespaces
🎮 Try in the Browser
🎁 vCluster Free Tier
Real usage, not a gated demo. Unlimited Tenant Clusters up to 64 CPUs / 32 GPUs, plus the full vCluster Platform UI — for free. Get Started Free →
Private Nodes (v0.27, CNI/CSI isolation), Auto Nodes (v0.28, Karpenter autoscaling), Standalone Mode (v0.29, no Control Plane Cluster — dedicated infrastructure or bare metal)
vCluster supports multiple deployment architectures. Each builds on the previous, offering progressively stronger isolation — from dense shared infrastructure to fully standalone deployments on dedicated infrastructure or bare metal.
🔹 Dedicated Nodes — Isolated compute on labeled node pools
Tenant Clusters get their own set of labeled nodes on the Control Plane Cluster. Workloads are isolated but still managed by the Control Plane Cluster.
🔹 Private Nodes v0.27+ — Full CNI/CSI isolation
External nodes join the Tenant Cluster directly with their own CNI, CSI, and networking stack. Complete workload isolation from the Control Plane Cluster.
🔹 vCluster Standalone v0.29+ — No Control Plane Cluster required
Run vCluster without any Control Plane Cluster. Deploy the Virtual Control Plane directly on bare metal or VMs. The highest level of isolation — vCluster becomes the cluster.
⚡ Auto Nodes v0.28+ — Karpenter-powered dynamic autoscaling
Automatically provision and deprovision private nodes based on workload demand. Works across public cloud, private cloud, hybrid, and bare metal environments.
Each Tenant Cluster gets its own API server, controller manager, and data store — complete Kubernetes API isolation
🔗 Shared Platform Stack(Shared / Dedicated Nodes)
Leverage the Control Plane Cluster's CNI, CSI, ingress, and other infrastructure — no duplicate platform components
🔒 Strong Tenant Isolation
Tenants get admin access inside their Tenant Cluster while having minimal permissions on the Control Plane Cluster
🔄 Resource Syncing(Shared / Dedicated Nodes)
Bidirectional sync of any Kubernetes resource — pods, services, secrets, configmaps, CRDs, and more
💤 Sleep Mode
Pause inactive Tenant Clusters to save resources. Instant wake when needed
🖥️ Standalone Deployment
Run without a Control Plane Cluster on dedicated infrastructure or bare metal — purpose-built for AI factories and on-prem GPU fleets
🧩 Integrations
Native support for cert-manager, external-secrets, KubeVirt, Istio, and metrics-server (host-side integrations apply in Shared / Dedicated Nodes modes)
📊 High Availability
Multiple replicas with leader election. Embedded etcd or external databases (PostgreSQL, MySQL, RDS)
> Shared Platform Stack, Resource Syncing, and host-cluster integrations apply only in Shared and Dedicated Nodes modes, where the Tenant Cluster shares the Control Plane Cluster's CNI, CSI, and platform stack. Private Nodes and Standalone deployments bring their own CNI, CSI, and platform components.
🌐 The vCluster Platform
vCluster is the foundation of a broader platform for running production Kubernetes and AI infrastructure on your own hardware — from a single rack to 100K-GPU supercomputers.
Hardware-enforced network isolation via programmatic VLANs, VRFs, and ACLs
Together these provide a complete foundation for AI factories — certified Kubernetes stacks, isolated Tenant Clusters, runtime workload sandboxing, and GPU infrastructure operations — the same pattern used to run production AI on hundreds of AI clouds and Fortune 500 on-prem platforms.
