GPU Cloud Services for AI Infrastructure | Rafay
GPU Cloud Services for AI Infrastructure
A GPU cloud platform enables organizations to transform GPU infrastructure into self-service, multi-tenant services that developers, data scientists, and AI teams can consume on demand. Instead of manually provisioning GPU clusters, platform teams expose GPU resources through governed service catalogs with built-in policies, quotas, usage tracking, and chargeback.
The Rafay Platform transforms GPU infrastructure into a secure, multi-tenant, revenue-ready cloud. Cloud providers, neoclouds, and Sovereign AI clouds that partner with Rafay are delivering CSP-grade use cases to their user communities. Learn how Rafay helps power the most innovative GPU providers in the world.
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What is a GPU Cloud Platform?
A Graphics Processing Unit cloud platform is software that enables organizations to turn GPU infrastructure into self-service, governed cloud services. Rather than manually provisioning GPU clusters for every workload, platform teams can package GPUs, AI applications, and development environments into standardized services that developers and data scientists consume on demand. Built-in governance, multi-tenancy, usage tracking, and policy controls help organizations improve GPU utilization while maintaining operational control.
Why Organizations Build GPU Clouds
More organizations are building GPU clouds to make expensive AI infrastructure easier to consume, govern, and scale. With GPU demand outpacing supply, manual provisioning often leaves costly hardware underutilized and developers waiting for access.
GPU cloud service providers offer automated provisioning, self-service access, and centralized governance to make GPU utilization seamless and cost-effective. It simplifies governance with policy enforcement, multi-tenant isolation, quotas, and usage tracking, enabling platform teams to scale AI infrastructure without sacrificing control.
For enterprises, this means faster AI development with greater operational control. For cloud providers, neoclouds, and sovereign AI clouds, it also creates opportunities to package GPU resources, AI applications, and model APIs into monetizable GPU-as-a-Service and AI service offerings.
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Key Capabilities of a GPU Cloud Platform
- Self-service provisioning: Give developers and data scientists on-demand access to GPU-powered environments through portals or APIs.
- Multi-tenant governance: Isolate teams and customers with RBAC, quotas, policy enforcement, and audit controls.
- Flexible GPU allocation: Support dedicated, shared, and fractional GPU allocation to maximize utilization.
- Service catalogs and SKUs: Package GPU infrastructure, AI applications, and development environments into standardized services.
- Usage metering and chargeback: Track resource consumption for cost visibility, internal chargeback, or customer billing.
- Policy-driven automation: Standardize provisioning, security, and lifecycle management across every deployment.
- Hybrid and multi-cloud deployment: Deliver a consistent operating model across private data centers, public clouds, and sovereign AI environments.
Common Use Cases of GPUs
Organizations use GPU cloud platforms to support a wide range of AI and high-performance computing workloads, including:
AI Model Training
Provision GPU clusters for training foundation models, fine-tuning LLMs, and distributed machine learning.
Inference and AI services
Deliver inference APIs, Retrieval-Augmented Generation (RAG) applications, and Model-as-a-Service offerings through governed self-service environments.
Developer workspaces
Provide AI engineers and data scientists with on-demand notebooks, Kubernetes clusters, and GPU-enabled development environments.
High-performance computing (HPC)
Support simulation, financial modeling, life sciences, and scientific research with scalable GPU infrastructure.
Media and Visualization
Accelerate rendering, video transcoding, and graphics-intensive workloads.
GPU Cloud Platform vs DIY GPU Infrastructure
As AI infrastructure grows, manually managing GPU resources with scripts and ticket-based workflows becomes increasingly difficult. Whereas a GPU cloud platform provides a standardized operating model that improves scalability, governance, and developer productivity. Here's a closer look at how they compare:
| DIY GPU Infrastructure | GPU Cloud Platform |
|---|---|
| Manual provisioning through tickets and scripts | Self-service provisioning through portals and APIs |
| Inconsistent governance across teams and environments | Centralized governance with policies, RBAC, and quotas |
| Underutilized GPU resources | Optimized GPU allocation and higher utilization |
| One-off environment configurations | Standardized service catalogs and reusable SKUs |
| Limited visibility into GPU consumption | Built-in usage metering, reporting, and chargeback |
| Difficult to support multiple tenants securely | Multi-tenant isolation for teams and customers |
| Significant operational overhead for platform teams | Automated provisioning and lifecycle management |
| Primarily delivers raw GPU access | Delivers GPU-as-a-Service and AI services |
Deliver a full-service GPU cloud in days
Assemble Inventory
Onboard GPU and CPU resources from data centers, public clouds, or colocation into a single control plane. Standardize and unify infrastructure for easier governance.
Select Service Offerings
Create standardized compute and application packages such as training, inference, or RAG workloads, complete with networking, storage, and policy enforcement.
Choose Allocation Models
Maximize GPU utilization with dedicated, shared, or fractional GPU allocation. Rafay ensures the right workload lands on the right compute at the right time.
Deliver Self-Service Experiences
Expose services through APIs or branded portals. Enable developers and data scientists to instantly access GPU-backed environments while maintaining governance and control.
Features
Turn GPU Infrastructure into a Revenue-Ready AI Cloud
The Rafay Platform provides the orchestration and workflow automation required for GPU clouds to turn static compute into enterprise-grade, centrally governed, self-service environments so costly hardware is turned into a means for generating business value and higher revenues.
FAQs About GPU Cloud Platforms
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.
What is GPU-as-a-Service?
GPU-as-a-Service (GPUaaS) is a cloud delivery model that provides on-demand access to GPU resources without requiring organizations to purchase and manage dedicated hardware. Using a GPU cloud platform, providers can package GPU infrastructure into standardized, self-service services with built-in governance, usage metering, and billing or chargeback capabilities.
How does a GPU Cloud Platform Work?
A GPU cloud platform transforms raw GPU infrastructure into governed, self-service services. Platform teams onboard GPU resources, define standardized services, enforce policies and quotas, and expose resources through self-service portals or APIs. Developers and data scientists can then provision GPU-backed environments on demand, while operators maintain centralized governance, usage visibility, and cost control.