Build, Operate and Monetize a Neocloud with Rafay

A neocloud provider is a cloud provider focused on delivering AI infrastructure and accelerated computing services. Unlike general-purpose cloud providers, neoclouds package GPU infrastructure, compute environments, AI development platforms, and inference services for AI training, fine-tuning, and production workloads.

A successful neocloud platform requires more than GPU infrastructure. Providers need capabilities for infrastructure orchestration, multi-tenancy, governance, self-service provisioning, service catalogs, usage metering, and billing integration to deliver AI services efficiently and at scale.

Rafay provides the platform for building and operating a neocloud by automating infrastructure provisioning, standardizing service delivery, enforcing governance policies, and enabling secure self-service access. This helps providers launch AI services faster without building and maintaining their own cloud management platform.

Rafay increases GPU utilization by enabling shared, fractional GPU consumption through NVIDIA MIG partitioning and time-slicing, which allow multiple workloads or tenants to share physical GPUs that would otherwise sit idle between large training jobs. Quota-based allocation and self-service provisioning keep more of the fleet active at any given time, reducing the stranded capacity that drives down utilization rates. On the margin side, Rafay helps providers move up the value stack from commodity GPU-hour rental toward token-metered AI services — which command stronger price points and higher margins than raw compute. Providers can offer foundation, compute, and AI SKUs additively on the same fleet, shifting their revenue mix toward AI services without re-platforming.

A neocloud can typically reach its first billable AI services in approximately six to eight weeks using the Rafay Platform, compared to the many months a from-scratch platform build would require. Rafay provides the multi-tenant operating layer, self-service portal, SKU design tools, metering engine, and billing integration that neoclouds would otherwise need to build themselves. Once the platform is running, operators can expand their service catalog — adding new GPU SKUs, inference endpoints, or AI service tiers — without re-platforming. The accelerated timeline means neoclouds can begin generating token-metered revenue while their infrastructure is still scaling, rather than waiting for a complete build-out.

Rafay enforces hard multi-tenancy so many customers can share one GPU fleet without sharing blast radius, data, or network access. Network isolation is implemented with per-tenant VRF and VLAN for north-south traffic and InfiniBand PKEY isolation via NVIDIA UFM for east-west GPU traffic. Each bare metal tenant receives a dedicated provisioning head node, and storage uses dedicated namespaces, access zones, and per-tenant bucket policies. Operators govern the entire fleet from a single control plane while every tenant remains cleanly separated — with their own quotas, RBAC policies, and performance isolation that prevents noisy-neighbor effects.

Providers can deliver bare metal, virtual machines, Kubernetes clusters, SLURM clusters, AI workspaces, applications, Model-as-a-Service offerings, and token-metered AI APIs through a governed, self-service platform.

We provide tenant isolation, role-based access controls, policy enforcement, and quota management that allow multiple teams, customers, or business units to securely share infrastructure while maintaining governance and operational consistency.

Rafay enables organizations to package models, inference endpoints, and AI tooling into governed, self-service services. Through multi-tenancy, usage metering, policy controls, and service catalogs, organizations can deliver and monetize AI capabilities at scale.

Yes. The Rafay platform supports full white-label customization, so GPU cloud providers and neoclouds can present the self-service portal entirely under their own brand. Logo, colors, domain name, and product name are configurable per white-labeled partner. Language, number format, currency display, and unit systems are configurable at the tenant level. This delivers a branded, hyperscaler-style self-service experience to end customers without building a portal from scratch.

Yes. GPUaaS can be deployed in sovereign, private, and fully air-gapped environments to meet data residency, security, and regulatory requirements while providing controlled access to GPU resources.