ai-cloud
Managed Kubernetes for GPU Cloud Builders
vCluster Platform delivers isolated tenant clusters on bare metal so GPU cloud providers can launch managed Kubernetes in weeks, not quarters.
Trusted by the fastest-growing AI cloud providers
Problem
Why GPU Cloud Launches Stall
Building managed Kubernetes on GPU hardware is harder than it looks.
DIY Takes Years and Millions
Building a GPU cloud platform yourself takes 6 to 10 engineers, 6 to 12 months, and over a million dollars. Most teams are still building two years in.
Weak Isolation or Wasted Hardware
Namespace isolation is too weak. Separate physical clusters are too expensive. Standard Kubernetes forces you to choose between tenant isolation and operational efficiency.
Customers Expect a Cloud Experience
AI teams have used AWS and GCP. They expect self-service environments and cloud-native tooling, and they will go back to a hyperscaler if you cannot deliver it.
Solution
The Full Stack for Managed Kubernetes on GPU Hardware
vCluster Platform virtualizes the Kubernetes control plane, running CNCF-certified tenant clusters as lightweight processes on your bare metal. From GPU rack provisioning to isolated tenant environments to pre-validated AI stacks, it is the complete path to launching managed Kubernetes for GPU cloud without building from scratch. Proven across 100K+ GPU nodes and 50+ GPU clouds and Fortune 500 customers.
Built for GPU Cloud at Every Layer
From bare metal provisioning to tenant isolation to AI-ready environments, vCluster covers the full stack GPU cloud providers need.
Tenant Isolation
Isolated Tenant Clusters in Seconds
Each tenant gets a fully isolated, CNCF-certified Kubernetes cluster with its own API server, etcd, and RBAC, running as a lightweight process on shared GPU infrastructure. No separate physical clusters required.
- Own API server per tenant
- Spins up in seconds
- CNCF-certified K8s per tenant
Bare Metal
Zero-Touch GPU Server Provisioning
vMetal handles PXE boot, OS installation, machine registration, and network configuration automatically. Go from racked GPU servers to a production-ready Kubernetes base layer with no manual steps.
- PXE boot and OS install
- Full machine lifecycle management
- Network automation built in
Dynamic Scaling
Automatic GPU Node Provisioning
Auto Nodes acts as bare metal Karpenter, automatically provisioning GPU servers via Terraform when tenants schedule workloads. GPU utilization rises without manual intervention.
- Automatic bare metal scaling
- Terraform-driven provisioning
- Responds to tenant workload demand
AI Environments
Pre-Validated AI Platforms Included
Certified Stacks turn a bare Kubernetes cluster into a production AI environment in minutes. Pre-validated integrations for Run:AI, Ray, and Jupyter mean tenants get a complete AI platform, not just raw compute.
- Run:AI, Ray, Jupyter ready
- Cluster to AI platform in minutes
- Certified with tenant isolation
Tenant Experience
EKS-Like Portal for Your Tenants
Give your customers an EKS-like self-service experience. Tenants provision and manage their own isolated Kubernetes environments through a portal without opening a support ticket or waiting on your team.
- Self-service cluster provisioning
- EKS-like tenant experience
- No platform team bottleneck
Why vCluster
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.
100K+
GPU Nodes Powered
50+
GPU Clouds & F500s
<45
Days to Launch
30K
GitHub Stars
Get Started in 3 Steps
1
Schedule a Demo
Talk to our team about your stack
2
Deploy vCluster
Deploy vCluster on your infra in minutes
3
Onboard Your Tenants
Go live with a hyperscaler-grade tenant experience in days
FAQs
What makes vCluster different from building managed Kubernetes ourselves?
Based on customer experience, building managed Kubernetes for a GPU cloud in-house typically requires 6 to 10 dedicated engineers working for 6 to 12 months at a cost exceeding one million dollars, and most teams are still building two years in. vCluster Platform provides the complete stack from bare metal provisioning to isolated tenant clusters to pre-validated AI environments, so you are not starting from zero. Boost Run launched managed Kubernetes in under 45 days using vCluster, with no new platform engineering hires.
How does tenant isolation work on shared GPU hardware?
vCluster virtualizes the Kubernetes control plane itself. Each tenant receives a fully isolated, CNCF-certified Kubernetes cluster with its own API server, etcd, scheduler, and RBAC, running as lightweight processes on a shared host cluster. Tenants cannot see platform internals or other tenants' workloads. For workloads requiring stronger boundaries, vNode (currently in private beta) adds kernel-native isolation using seccomp, cgroups, namespaces, and AppArmor without hypervisor overhead, preserving bare metal GPU performance.
Do tenants get real Kubernetes or a restricted subset?
Every tenant gets a fully conformant, CNCF-certified Kubernetes cluster with complete cluster-admin access within their environment. They can install custom CRDs, configure their own RBAC, and use any Kubernetes tooling without restrictions. This is not a namespace partition or a proprietary API subset. It is a real Kubernetes cluster running as an efficient process, fully isolated from other tenants on the same GPU hardware.
How quickly can a GPU cloud provider go live with managed Kubernetes?
vCluster is designed to compress launch timelines significantly. Boost Run launched their managed Kubernetes offering in under 45 days with zero new platform engineering hires. Lintasarta launched Indonesia's leading GPU cloud in 90 days with over 170 isolated tenant clusters. The platform covers bare metal provisioning, tenant cluster orchestration, and AI environment configuration so providers are not assembling disparate tools from scratch.
Is vCluster compatible with NVIDIA GPU hardware and AI platforms?
Yes. vCluster is named in the NVIDIA DGX SuperPOD reference architecture. The platform is also cited in the SemiAnalysis ClusterMax evaluation criteria. Certified Stacks provide pre-validated integrations with Run:AI, Ray, and Jupyter, turning bare Kubernetes clusters into production AI environments in minutes. The platform is proven across 100K+ GPU nodes in production.
Can vCluster support a GPU cloud deployed across multiple regions or data centers?
Yes. vCluster Platform includes multi-region and multi-cloud support, with a central control plane that manages tenant clusters across multiple clouds and data centers from a single interface. The built-in VPN enables secure connectivity between control planes and distributed worker nodes in different locations. This makes vCluster suitable for GPU cloud providers running distributed infrastructure or planning to expand across regions.
Launch Managed Kubernetes on Your GPU Cloud
See how GPU cloud providers go from bare metal to managed Kubernetes faster.
