From GPUs to Revenue: A Practical Guide to AI Factory Builds
This white paper breaks down what it actually takes to turn GPU investments into measurable business outcomes.
The Rafay Platform - FOR CLOUD SERVICE PROVIDERS
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.
Together, these capabilities enable providers to deliver GPU resources as secure, scalable, and self-service services that are ready for enterprise AI workloads.
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.
Trusted by leading enterprises, neoclouds and service providers
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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.
Featured Resources
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Frequently Asked Questions About GPU as a Service
Find answers to common questions about our GPUaaS services below.
How does GPU as a Service work?
GPU-as-a-Service delivers GPU resources through a self-service platform, allowing users to provision GPU-powered environments on demand. Providers manage provisioning, governance, security, and usage, while users consume GPU resources without managing the underlying infrastructure.
Who uses GPU-as-a-Service?
GPU-as-a-Service is used by cloud providers, neoclouds, enterprises, AI platform teams, and research organizations that need scalable, on-demand GPU resources for AI development, training, inference, and high-performance computing.
What are the benefits of GPUaaS?
GPU-as-a-Service helps organizations improve GPU utilization, scale resources on demand, reduce infrastructure costs, and accelerate AI development by providing fast, self-service access to GPU capacity.
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, optimal for large training runs), NVIDIA MIG (Multi-Instance GPU) partitioning (up to seven isolated MIG instances per A100 or H100, each with dedicated memory and compute), and time-slicing (multiple workloads sharing a GPU in time-multiplexed fashion, suited for lower-intensity inference or development workloads). Operators configure which sharing modes are available per SKU through PaaS Studio; tenants select the appropriate GPU size from the catalog without needing to understand the underlying partitioning mechanism. Security and compute isolation between MIG instances is enforced at the NVIDIA hardware level; chargeback data is collected per MIG instance or per time-slice allocation for granular cost attribution across tenants and business units.
Does your platform also support CPU consumption?
Yes. The Rafay Platform has always supported CPU-based workloads and can easily deliver a PaaS experience that offers CPU+GPU instances to end users.
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. Rafay allows for customizable chargeback group definitions to align with organizational structures or projects. Both group definition and data collection can be carried out programmatically, enabling efficient and accurate billing processes.
Can GPUaaS be deployed in sovereign or air-gapped environments?
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.
Whitepaper
GPU cloud evaluation report
Evaluating how the Rafay Platform delivers a GPU cloud for enterprises and cloud service providers by PivotNine.
