ai-factory
The AI Factory Platform for GPU Clouds
Build your AI factory on shared bare metal GPU hardware. vCluster creates fully isolated, CNCF-certified tenant clusters in seconds without provisioning separate physical clusters.
Trusted by the fastest-growing AI cloud providers
Problem
Why AI Factories Stall
Building a production-grade AI factory on GPU infrastructure exposes three hard problems.
DIY Takes Years
Based on industry experience, building a GPU cloud platform typically requires 6 to 10 engineers, 6 to 12 months, and over a million dollars. Most teams we've spoken with are still building two years in.
Isolation or Efficiency
Standard Kubernetes forces you to choose between strong tenant isolation and operational efficiency. Namespace isolation is too weak. Separate clusters are too expensive.
Customers Expect Cloud UX
AI teams have used AWS and GCP. They expect self-service environments, managed Kubernetes, and cloud-native tooling and will go back to a hyperscaler if you cannot deliver it.
Solution
One Platform from Bare Metal to AI Factory
vCluster delivers the complete AI factory infrastructure stack: zero-touch bare metal provisioning via vMetal, CNCF-certified tenant clusters via vCluster, and kernel-native workload isolation via vNode (currently in private beta). Boost Run launched a managed Kubernetes service in under 45 days. Lintasarta launched Indonesia's leading GPU cloud in 90 days.
Built for AI Factory Scale
From GPU rack provisioning to isolated tenant environments and pre-validated AI platforms, every layer your AI factory needs is included.
Bare Metal
Zero-Touch GPU Rack Provisioning
vMetal handles PXE boot, OS installation, machine registration, and network automation for GPU servers. Go from rack to production-ready Kubernetes nodes without manual configuration or external dependencies.
- PXE boot and OS installation automated
- Full GPU server lifecycle management
- Network automation with VLAN and VXLAN support
Tenant Isolation
Isolated Tenant Clusters on Shared Hardware
Every AI factory tenant gets their own Kubernetes API server, etcd, scheduler, and RBAC running as lightweight pods on shared bare metal GPU infrastructure. Spin up hundreds of isolated tenant environments in seconds.
- Own API server and etcd per tenant
- Spins up in seconds not hours
- No physical cluster required per tenant
AI Platforms
Pre-Validated AI Environments Ready Instantly
Turn a bare Kubernetes cluster into a production AI platform in minutes. Certified integrations with Run:AI, Ray, and Jupyter mean your AI factory tenants get a full platform environment without weeks of integration work.
- Run:AI, Ray, and Jupyter certified
- Cluster to AI platform in minutes
- Tested with tenant isolation
Workload Security
Kernel-Native Isolation Without VM Overhead
vNode (currently in private beta) delivers container breakout protection for AI factory workloads using seccomp, cgroups, namespaces, and AppArmor at the kernel level. Bare metal GPU performance is preserved with no hypervisor tax.
- Prevents container breakout attempts
- No hypervisor overhead on GPU workloads
- Defense-in-depth with vCluster control planes
Tenant Experience
Self-Service Portal for AI Factory Tenants
Give every AI factory team an EKS-like self-service experience. Tenants provision their own environments through a managed portal without waiting on platform engineering or filing infrastructure tickets.
- EKS-like experience for end users
- Tenant-driven environment provisioning
- Reduces platform team operational burden
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 is an AI factory and how does vCluster support it?
An AI factory is an internal or external infrastructure platform purpose-built to run, scale, and isolate AI workloads across GPU hardware. vCluster supports AI factory deployments by providing the full infrastructure stack: bare metal GPU provisioning via vMetal, CNCF-certified isolated tenant clusters via vCluster Platform, and kernel-native workload isolation via vNode (currently in private beta). This means a single GPU cluster can serve hundreds of isolated teams or customers without provisioning separate physical infrastructure per tenant.
How long does it take to launch an AI factory with vCluster?
Boost Run launched a production managed Kubernetes service in under 45 days using vCluster Platform. Lintasarta deployed Indonesia's leading GPU cloud with over 170 isolated tenant environments in 90 days. The speed advantage comes from eliminating the need to build control plane infrastructure from scratch. Based on experience working with GPU cloud operators, a DIY approach typically requires 6 to 10 engineers and 6 to 12 months and over a million dollars in investment before the first tenant is onboarded.
How does vCluster isolate AI workloads across shared GPU hardware?
vCluster delivers a layered isolation model. At the control plane layer, every tenant gets their own Kubernetes API server, etcd, and RBAC as a lightweight pod on shared hardware. At the node layer, tenants can be assigned shared nodes, private nodes, or dedicated nodes depending on their isolation requirement. At the workload layer, vNode (currently in private beta) adds kernel-native isolation using seccomp, cgroups, and AppArmor to prevent container breakout without introducing VM overhead.
Is vCluster certified for production AI factory deployments?
Yes. vCluster is a CNCF-certified Kubernetes distribution, meaning every tenant cluster meets full Kubernetes API conformance standards. vCluster is named in the NVIDIA DGX SuperPOD reference architecture and referenced in SemiAnalysis ClusterMax evaluation criteria for GPU cloud providers. The platform currently powers over 100,000 GPU nodes and over 1 million CPU nodes in production across more than 50 GPU clouds and Fortune 500 customers.
Can vCluster support compliance requirements in an AI factory?
Yes. vCluster Platform supports air-gapped and FIPS deployments for environments with strict compliance requirements. Tenant network isolation is enforced via hardware-level VLANs, VXLANs, VRFs, and ACLs through Netris integration. Control planes can also run as dedicated VMs rather than pods for OS-level separation when regulatory requirements demand it. Day 2 operations including observability, backups, and compliance tooling are built into the platform.
Does vCluster replace the need for k3s or kubeadm on bare metal?
Yes. vCluster Standalone runs as a single binary directly on Linux bare metal with no external Kubernetes dependency. It replaces k3s, kubeadm, and k0s as the base layer for AI factory deployments on GPU hardware. Combined with vMetal's zero-touch provisioning, this eliminates the need to install and maintain a separate bootstrapping K8s distribution before tenant clusters can be created.
Launch Your AI Factory Faster
See how GPU cloud teams go from bare metal to production AI infrastructure.
