ai-cloud
Cloud GPU Orchestration Tools for AI Cloud Providers
vCluster gives GPU cloud providers the orchestration tools to run hundreds of isolated tenant clusters on bare metal without provisioning separate physical clusters per customer.
Trusted by the fastest-growing AI cloud providers
Problem
Why GPU Orchestration Breaks at Scale
Generic tools were not built for the demands of AI cloud infrastructure at scale.
Bare Metal Alone Is Not Enough
Selling raw GPU compute is a race to the bottom. Customers want managed Kubernetes and cloud-native tooling, not just hardware.
DIY Orchestration Takes Too Long
Based on industry estimates, building a GPU cloud platform in-house typically requires 6 to 10 engineers, 6 to 12 months, and over a million dollars.
Isolation Models Force Hard Tradeoffs
Namespace isolation is too weak for real tenants. Separate physical clusters per tenant are too expensive and slow to provision.
Solution
One Stack from Bare Metal to Tenant Clusters
vCluster virtualizes the Kubernetes control plane so every tenant gets a real, CNCF-certified K8s environment as a lightweight process on shared GPU hardware. Provision hundreds of isolated tenant clusters in seconds, not days. Proven across 100K+ GPU nodes and 50+ GPU clouds.
Cloud GPU Orchestration Built for AI Clouds
Every layer of the stack from bare metal provisioning to tenant isolation is purpose-built for GPU cloud operators.
Tenant Isolation
Isolated Tenant Clusters in Seconds
Each tenant gets a dedicated Kubernetes control plane running as a lightweight pod on shared GPU infrastructure. Own API server, etcd, scheduler, and RBAC with zero physical cluster overhead.
- Spins up in seconds, not hours
- Full K8s API per tenant
- Zero marginal cost to provision
Bare Metal Ops
Zero-Touch GPU Server Provisioning
PXE boot, OS installation, machine registration, and network configuration are fully automated. Go from GPU rack to production-ready Kubernetes without manual intervention across your entire server fleet.
- Automated PXE boot and OS install
- Full machine lifecycle management
- Network automation built in
Dynamic Scaling
Auto-Provision GPU Nodes on Demand
Bare metal GPU nodes are automatically provisioned via Terraform when tenants schedule workloads. Scale physical GPU infrastructure dynamically without manual intervention or pre-provisioning idle capacity.
- Triggered by tenant workload scheduling
- Terraform-native provisioning
- Eliminates idle GPU capacity waste
AI Environments
Production AI Platforms in Minutes
Pre-validated environments with Run:AI, Ray, and Jupyter turn a bare Kubernetes cluster into a production AI platform in minutes. Certified to run inside isolated tenant environments without custom integration work.
- Supports Run:AI, Ray, and Jupyter
- Cluster to AI platform in minutes
- Certified with tenant isolation
Workload Security
Kernel-Native GPU Workload Isolation
Each workload runs in its own secure runtime using seccomp, cgroups, namespaces, and AppArmor (vNode, currently in private beta). No hypervisor tax means bare metal GPU performance is preserved while preventing container breakout across tenants.
- No hypervisor overhead on GPU
- Prevents container breakout attacks
- Defense-in-depth with control plane isolation
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 other cloud GPU orchestration tools?
vCluster is the only platform that virtualizes the Kubernetes control plane itself, running each tenant's K8s environment as a lightweight process on shared GPU infrastructure rather than provisioning separate physical clusters. This means you get strong tenant isolation at near-zero marginal cost per tenant. The stack covers the full path from bare metal provisioning through tenant cluster orchestration to kernel-native workload isolation, which, to our knowledge, no other single vendor delivers as an integrated stack today.
Can vCluster run directly on bare metal GPU servers without an existing Kubernetes cluster?
Yes. vCluster Standalone runs as a single binary directly on bare metal Linux with no external Kubernetes dependency. There is no need for k3s, kubeadm, or k0s as an underlying base layer. Combined with vMetal for zero-touch provisioning, you can go from a GPU rack to a production Kubernetes environment without any intermediate infrastructure.
How quickly can an AI cloud provider go live with vCluster?
Boost Run launched a managed Kubernetes service in fewer than 45 days with zero new platform engineering hires. Lintasarta built Indonesia's leading GPU cloud in 90 days and deployed over 170 tenant clusters. Based on industry experience, a DIY approach typically requires 6 to 10 engineers and 6 to 12 months.
What isolation options are available for GPU cloud tenants?
vCluster offers a flexible isolation spectrum. Shared Nodes provide namespace-level separation for cost efficiency. Private Nodes give each tenant fully dedicated physical nodes with their own CNI and CSI. Dedicated Nodes eliminate noisy-neighbor GPU contention entirely. vNode (currently in private beta) adds kernel-native workload isolation using seccomp, cgroups, and AppArmor without any hypervisor overhead, preserving bare metal GPU performance.
Is vCluster's Kubernetes distribution standards-compliant?
Yes. Every tenant cluster created by vCluster is a CNCF-certified Kubernetes distribution with 100 percent API compatibility. Tenants get full cluster-admin access, can install their own CRDs, configure RBAC, and use any standard Kubernetes tooling without restriction. vCluster is also named in the NVIDIA DGX SuperPOD reference architecture.
Does vCluster support multi-region GPU cloud deployments?
Yes. vCluster Platform provides a central control plane that spans multiple clouds and data centers from a single management interface. A built-in VPN handles secure connectivity between control planes and distributed worker nodes across locations, making it well-suited for inference providers and GPU cloud operators running infrastructure across multiple regions or colocation facilities.
Launch Your GPU Cloud Faster
See how vCluster powers cloud GPU orchestration for AI cloud providers at scale.
