vCluster Labs helps enterprises deploy isolated, CNCF-certified Kubernetes tenant clusters on bare metal in seconds — giving AI teams the cloud experience they expect without provisioning separate physical clusters.
Enterprises building internal AI factory infrastructure face three compounding challenges.
Standard Kubernetes forces you to choose between tenant isolation and operational efficiency — separate clusters are too expensive, namespace isolation too weak.
Building GPU infrastructure yourself takes 6 to 10 engineers, 6 to 12 months, and significant budget — and most teams are still building two years in.
AI teams have used AWS and GCP. They expect self-service environments and managed Kubernetes — and they will go back to a hyperscaler if you cannot deliver it.
vCluster Labs delivers a complete path from GPU racks to production AI environments — bare metal provisioning via vMetal, tenant cluster orchestration via vCluster, and kernel-native workload isolation via vNode (currently in private beta). Enterprises like those powered across 100K+ GPU nodes and 50+ GPU clouds get AI factory infrastructure running in days, not quarters.
Every layer your AI factory needs — from bare metal provisioning and tenant isolation to pre-validated AI environments and Day 2 operations.
PXE boot, OS installation, machine registration, and full lifecycle management from rack to production. vCluster Standalone runs as a binary directly on bare metal with no external Kubernetes dependency — no k3s or kubeadm required.
Each AI team or business unit gets their own Kubernetes API server, etcd, RBAC, and CRDs running as a lightweight pod — without provisioning a separate physical cluster. Spin up isolated tenant environments in seconds, not hours.
Turn a bare Kubernetes cluster into a production AI platform in minutes with pre-validated environments for Run:AI, Ray, and Jupyter. Certified to work with tenant isolation — no custom integration work required.
Auto Nodes behaves like Karpenter for bare metal — automatically provisioning GPU nodes via Terraform when AI workloads are scheduled. Scale your AI factory infrastructure dynamically without manual intervention.
Built-in observability, updates, backups, disaster recovery, and compliance across every tenant cluster in your AI factory fleet — managed from a central UI, CLI, or API.
This isn’t a side project. Behind every vCluster deployment is 5+ years of deep K8s engineering, security hardening, and battle-tested infrastructure work at massive scale.
Talk to our team about your stack
Deploy vCluster on your infra in minutes
Go live with a hyperscaler-grade tenant experience in days
AI factory infrastructure refers to the full stack of compute, orchestration, and isolation required to run AI workloads at enterprise scale — from bare metal GPU servers through tenant environments to production AI platforms. vCluster Labs provides exactly this: the vMetal layer handles bare metal provisioning, vCluster orchestrates isolated tenant Kubernetes clusters, and vNode (currently in private beta) delivers kernel-native workload isolation. Together they form a complete, integrated AI factory stack without requiring teams to assemble it from separate tools.
vCluster virtualizes the Kubernetes control plane itself. Each tenant or AI team receives a fully isolated environment with its own API server, etcd, RBAC, and CRDs — all running as lightweight pods inside a shared host cluster. This eliminates the cost and provisioning time of separate physical clusters while maintaining stronger isolation than namespace partitioning. The result is hundreds of dedicated tenant environments on shared GPU infrastructure with near-zero marginal cost per tenant.
Enterprises can move from bare metal GPU hardware to production AI environments in days rather than months. Boost Run launched a managed Kubernetes offering in less than 45 days. Lintasarta launched Indonesia's first GPU cloud in 90 days with 170 plus tenant clusters. vCluster's pre-validated AI environments — including Run:AI, Ray, and Jupyter — mean teams skip weeks of integration work and reach production faster.
vCluster is purpose-built for bare metal GPU infrastructure. The vMetal component handles zero-touch bare metal provisioning — PXE boot, OS installation, machine registration, and full lifecycle management. vCluster Standalone runs as a single binary directly on Linux with no external Kubernetes dependency, eliminating the need for k3s, kubeadm, or k0s as a base layer. The full stack is production-proven across 100K plus GPU nodes.
vCluster's Certified Stacks include pre-validated integrations with Run:AI, Ray, Jupyter, and Slurm via Slinky. These environments are tested and certified against vCluster tenant isolation, so each AI team or business unit gets an isolated, fully functional AI platform without custom configuration. vCluster is also named in the NVIDIA DGX SuperPOD reference architecture.
vCluster provides a layered security model for enterprise AI factory infrastructure. Control plane isolation ensures each tenant has dedicated Kubernetes API servers and etcd. vNode (currently in private beta) adds kernel-native workload isolation using seccomp, cgroups, namespaces, and AppArmor — preventing container breakouts without VM overhead. Network isolation is enforced via hardware-level VLANs, VXLANs, and DPU policies through Netris integration. Air-gapped and FIPS deployments are also supported for compliance-sensitive environments.
See how vCluster Labs powers enterprise AI factories on bare metal GPU infrastructure.
