The AI Ops Layer
for Your GPU Hardware
You bought GPU hardware to power your own model deployments. CloudRift gives your engineers a single control plane for VMs, containers, MIG, bare metal, and OpenAI-compatible inference — so they ship models instead of fighting infrastructure. No specialized AI ops team required.
Production Deployments
Operational Control
One Platform, Full Operational Control
Manage every workload across your GPU fleet from a single control plane — without hiring a specialized AI ops team.
APISingle Control Plane
VMs, containers, MIG, and bare metal from one console with consistent APIs, RBAC, audit logging, and quotas across your fleet.
Self-Service for AI Teams
AI engineers launch VMs, containers, and inference endpoints themselves through console and API. Orchestration, scheduling, tenant isolation, and billing are built in — no specialized AI ops team to hire.
Sovereign by Default
Workloads run on your hardware in your facility. Data, model weights, and inference traffic stay inside your perimeter.
How It Works
From hardware to running workloads in days — connect your nodes, define your tenants, and put your GPUs to work.
Step 1
Connect Your Infrastructure
Install the CloudRift agent on bare-metal or VM hosts. Consumer and enterprise GPUs supported.
Step 2
Define Tenants and Quotas
Carve up your fleet across teams and business units. RBAC, quotas, and audit logging built in.
Step 3
Run Workloads
Launch VMs, containers, or OpenAI-compatible inference endpoints from one console.
Step 4
Operate at Scale
Track utilization, set alerts, and open excess capacity to external customers when you’re ready.
Platform
A Complete AI Operating System for Your GPU Fleet
Run every workload type — from research notebooks to production inference — on consumer or enterprise GPUs from a single platform.
Dedicated GPU Virtual Machines
Full OS control for custom drivers, images, and networking. Dedicated GPU passthrough with IOMMU-enforced isolation — no oversubscription, no noisy neighbors. Compatible with KVM/QEMU.
Production Container Workloads
Launch prebuilt images for PyTorch, vLLM, and ComfyUI, or bring your own. Reproducible environments via Docker CLI and REST API, with per-tenant isolation across teams and business units.
OpenAI-Compatible LLM Inference
Serve Llama, DeepSeek, Qwen, and Mistral through OpenAI-compatible APIs. vLLM-based, drop-in for existing client code, with per-token billing for internal chargeback.
No Vendor Lock-In
Hardware Agnostic, Top to Bottom
Most GPU platforms are built on NVIDIA's stack top to bottom. CloudRift abstracts the hardware layer so you keep procurement leverage and avoid single-vendor concentration risk.
NVAIE + Open Source Stacks
Choose between the feature-rich NVIDIA AI Enterprise stack or a cost-effective open-source virtualization stack. Same orchestration plane, different licensing posture.
AMD as First-Class
MI300X and MI350X get identical GPU passthrough, workload isolation, and fleet management as NVIDIA hardware. Not a second-tier path — same code path.
Accelerator Support
PCIe LLM accelerators and FPGA cards are supported as first-class compute targets. Plug in specialty silicon without rebuilding your orchestration.
Prosumer Path
RTX 4090, RTX 5090, and RTX PRO 6000 Blackwell GPUs fully supported under the same APIs as datacenter-grade hardware. Cost-effective for non-mission-critical workloads.
Single-vendor lock-in is a procurement risk and a geopolitical risk. CloudRift abstracts the hardware so the choice stays yours.
Persistent Storage
Production Storage for GPUs
Volumes that outlive your instances, backed by enterprise storage stacks, accessible across datacenters.
Instance-Independent Lifecycle
Volumes persist when instances terminate. Create, attach, detach, and reattach to any instance — your data survives orchestration churn.
Enterprise Storage Backends
Powered by Ceph, DDN, and WEKA. Production-grade reliability and throughput tuned for AI workloads, not retrofitted from generic block storage.
Multi-Datacenter Support
Access volumes across shared storage clusters in any datacenter. No data shuffling between regions when you reschedule a workload.
Full API Control
Manage volumes programmatically. Create, resize, attach, and snapshot via REST API and CLI — same primitives as the rest of the platform.
Versus the Alternatives
Why Enterprise Teams Pick CloudRift
You bought GPU hardware. Three real paths sit in front of you. Here's how they compare on the things infrastructure leaders actually care about.
| CloudRift | Build In-House | Hyperscaler Private Cloud | |
|---|---|---|---|
| Time to first internal workload | Days | 12–24 months | 6–12 months |
| AI ops team required | None — platform included | 5–10 engineers | 2–5 specialists + cloud team |
| AI engineer experience | Self-service console + API | Tickets to platform team | Vendor portal, locked APIs |
| Multi-tenancy + quotas built-in | Yes | Build it yourself | Yes (vendor-locked) |
| OpenAI-compatible inference | vLLM, drop-in | Build it yourself | Proprietary APIs |
| Air-gapped operation | Yes | Possible | No (cloud-tethered) |
| Vendor lock-in | None — open weights, open APIs | None | High — vendor stack throughout |
FAQ
Common Questions From Enterprise Teams
Ready to put your GPU hardware to work?
Talk to our team about deploying CloudRift across your fleet — VMs, containers, MIG, bare metal, and inference, from one platform.
Contact Us
Let us know if you're looking to:
- Find an affordable GPU provider
- Sell your compute online
- Manage on-prem infrastructure
- Build a hybrid cloud solution
- Optimize your AI deployment
PO Box 1224, Santa Clara, CA 95052, USA
+1 (831) 534-3437
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