GPU As a Service (GPUaas) for Cloud Providers

GPU infrastructure is a major investment, and without the right orchestration and workflow automation in place, those resources remain underutilized or are delivered to the market at low price points. The sure-fire way to drive higher margins is to deliver self-service consumption experiences to developers while enforcing enterprise-grade controls and strong multi-tenancy.

The Rafay Platform empowers neoclouds, Sovereign AI Clouds, Telcos, and cloud service providers (CSPs) to offer premium services that meet the highest enterprise expectations for governance and control, while delivering self-service consumption to their enterprise users. With Rafay, CSPs achieve higher revenues, higher margins, and higher infrastructure utilization.

What Is GPU-as-a-Service (GPUaaS)?

GPU-as-a-Service (GPUaaS) is a cloud delivery model that allows organizations to consume GPU resources on demand rather than purchasing and managing dedicated hardware. Instead of manually provisioning GPU infrastructure, organizations can provide GPU-powered environments through self-service with automation, governance, and usage controls.

AI service providers use GPUaaS to deliver scalable GPU capacity, AI development environments, inference services, and managed AI platforms through self-service portals that simplify resource access while maintaining operational control.

Rafay helps cloud providers, telcos, neoclouds, and sovereign AI clouds transform GPU infrastructure into consumable services. The platform enables self-service GPUaaS delivery with built-in governance, multi-tenancy, automation, and usage visibility, helping providers improve infrastructure utilization and accelerate time to revenue.

Capabilities Required to Deliver GPU-as-a-Service

Building an enterprise GPU-as-a-Service offering requires more than GPU hardware. Providers need capabilities that enable secure, self-service GPU consumption at scale, including the following:

Multi-tenancy

Securely isolate customers, business units, or teams while enabling shared use of GPU infrastructure.

Governance

Apply policies for provisioning, lifecycle management, and compliance to ensure resources are used consistently and responsibly.

Billing and Chargeback

Track GPU consumption to support customer billing, internal chargeback, or showback reporting.

Self-Service Marketplace

Provide a catalog where users can provision approved GPU-powered environments and AI services on demand.

Identity and Access Management (IAM)

Integrate with enterprise identity providers to enable role-based access control and centralized authentication.

Security

Protect workloads with tenant isolation, policy enforcement, and secure access to GPU resources.

Resource Quotas

Prevent resource contention by controlling GPU allocation, capacity limits, and consumption across tenants.

Resource Isolation

Ensure workloads remain independent to improve security, performance, and reliability in shared environments.

From GPU Infrastructure to Revenue-Generating AI Services

GPU-as-a-Service provides the foundation for a broader portfolio of AI and compute services. By packaging infrastructure into self-service offerings, providers can increase GPU utilization while creating new revenue opportunities. With the Rafay Platform, cloud providers, neoclouds, telcos, and sovereign AI clouds can package GPU infrastructure into ready-to-consume services, including:

• GPU-as-a-Service (GPUaaS): Deliver on-demand GPU capacity through secure, multi-tenant environments with governance, quotas, and self-service provisioning.
• AI Models-as-a-Service: Provide access to preconfigured foundation models and inference endpoints that developers can consume without managing the underlying infrastructure.
• Developer Workspaces: Offer ready-to-use development environments with the frameworks, libraries, and GPU resources needed to build, train, and test AI applications.
• AI Applications: Publish packaged AI applications, NVIDIA Blueprints, partner solutions, and other AI services through a self-service marketplace.

Key outcomes include:

• Launch GPU cloud services and AI marketplaces faster while improving GPU utilization.
• Deliver a catalog of GPU, AI, and compute services that compete with hyperscale cloud offerings.
• Support enterprise customers with secure multi-tenant environments and built-in governance.
• Meet sovereign cloud requirements with fully air-gapped deployment options.
• Simplify lifecycle management for Kubernetes, GPUs, and bare metal infrastructure.

Supporting Features

Everything Needed to Launch an Enterprise GPU-as-a-Service Platform

The Rafay Platform gives enterprises the capabilities needed to launch, operate, and grow enterprise GPU-as-a-Service offerings, enabling them to:

• Monetize GPU infrastructure with built-in SKU management, billing automation, and token metering.
• Differentiate your services with white-labeled portals and branded self-service experiences.
• Support enterprise and sovereign customers with secure, multi-tenant, and air-gapped architectures.
• Accelerate AI adoption through integrations with leading AI platforms and developer tools.

Key Benefits

Launch GPU-as-a-Service Faster with Rafay

Monetize GPU Infrastructure in Days, Not Months

Launch revenue-ready AI/ML environments with built-in SKU management, billing, and consumption metering so every GPU hour turns into billable services faster.

Differentiate with Enterprise-Grade AI Services

Offer a fully integrated, white-labeled portfolio of AI/ML and GenAI tools (Jupyter, Ray, Kubeflow, Slurm) that attracts developers and retains enterprise customers.

Drive Adoption Across Enterprises and Governments

Deliver secure, sovereign-ready deployments that meet compliance requirements for regulated industries, expanding your addressable market.

Expand Your AI Service Portfolio

Go beyond GPU capacity by offering AI models, developer workspaces, NVIDIA Blueprints, and packaged AI applications through a self-service marketplace.

Boost Margins Through Automation

Reduce engineering overhead with multi-tenant automation and operational efficiency, freeing your teams to focus on growth while cutting costs.

Frequently Asked Questions About GPU as a Service

What counts as a node?

A node is a physical or virtual server/machine.

Do you have any volume discounts?

Yes! As the number of nodes increases, the price per cluster or per node decreases.

What about short-lived or ephemeral clusters?

Our customers love to experiment, and we don’t ding them for it. We don’t charge for node count spikes, but look at the running average of nodes in use when calculating usage.

Is there a difference between production and non-production pricing?

The management overhead for helping our customers operate dev vs prod clusters is effectively the same, so we treat all nodes the same.

What if I use more nodes than I’ve licensed?

Rafay has a true-up forward policy, meaning that we don’t carry out chargebacks for scenarios where the consumption in a completed billing cycle exceeded the licensed count. If the new, steady-state number of nodes is expected to be higher, our customer success team will discuss the situation with you and take steps to adjust billing accordingly for the next billing cycle.

How much does Enterprise Support (24x7x365) cost?

Enterprise Support is available at an additional fee equaling 20% of the cluster or node subscription.

Is GPU virtualization supported?

Yes. The Rafay Platform supports three GPU sharing modes that operators can offer to tenants in self-service: full passthrough (one physical GPU per workload), NVIDIA MIG (Multi-Instance GPU) partitioning, and time-slicing (multiple workloads sharing a GPU in time-multiplexed fashion).

How does Rafay solve for chargeback and billing?

Rafay offers a comprehensive solution for chargebacks and billing. The platform collects granular chargeback information on resource usage, which can be easily exported to customers’ existing billing systems for further processing and distribution.