From GPUs to AI Services: Make AI Infrastructure Consumable at Enterprise Scale
AI infrastructure platforms haven’t caught up to AI ambition. Enterprises are investing heavily in GPUs and cloud resources, but much of that infrastructure sits idle. Without a scalable way to manage and deliver it to teams, innovation stalls—and costs spiral.
Enter the Rafay Platform: the platform that helps organizations move beyond GPU provisioning to deliver AI infrastructure as governed, self-service, monetizable services. Rafay enables platform teams, cloud providers, neoclouds, and sovereign AI operators to package GPUs, compute, AI tools, model services, and inference endpoints into consumable offerings with built-in governance, multi-tenancy, usage metering, and consumption control.
What Is AI Infrastructure Management?
AI infrastructure management is the discipline of turning AI infrastructure into governed, self-service services that can be consumed, delivered, and monetized at scale. Rather than focusing solely on provisioning GPUs and infrastructure, modern AI infrastructure management enables you to transform compute, models, AI platforms, and inference capabilities into consumable services for developers, business units, customers, and tenants.
To achieve this, you must provide self-service AI infrastructure consumption, enforce governance and policy controls, support multi-tenancy, manage quotas and usage, enable chargeback and showback, and deliver AI capabilities through service catalogs and automated workflows.
Whether delivering GPU-as-a-Service, Model-as-a-Service, inference-as-a-service, or self-service AI platforms, effective AI infrastructure management provides the platform layer that connects infrastructure investments to business outcomes. Rafay helps enterprises, neoclouds, cloud providers, and sovereign AI operators deliver governed, multi-tenant, and monetizable AI services through a single AI infrastructure management platform.
Why AI Infrastructure Management Is Challenging
Turning AI infrastructure into governed, self-service, and monetizable services remains a challenge for many. From self-service consumption and multi-tenancy to chargeback, governance, and resource utilization, many organizations lack the platform capabilities needed to deliver AI services at scale.
The main factors driving these management challenges include:
GPUs Exist, But They're Difficult to Consume
After you’ve invested heavily in GPU infrastructure, you see that access still depends on tickets, manual approvals, and provisioning requests. These workflows slow AI development and create unnecessary friction for platform teams and end users alike.Rafay addresses this challenge by providing a self-service AI infrastructure platform that enables developers, data scientists, and tenants to consume GPU resources on demand while maintaining governance, visibility, and control.
Multi-Tenant AI Infrastructure Is Hard to Govern
Supporting multiple teams, projects, customers, and workloads on shared infrastructure can be overwhelming as it introduces new challenges around isolation, security, and resource allocation. Without the right controls, organizations risk resource contention, inconsistent policies, and operational complexity.Rafay provides a multi-tenant AI infrastructure platform with built-in governance, quota enforcement, role-based access controls, and tenant isolation, allowing you to securely deliver AI infrastructure at scale.
GPU Utilization Often Falls Short
Idle GPUs, overprovisioned environments, and fragmented resource pools can significantly reduce infrastructure efficiency. As AI demand grows, simply adding more hardware often increases costs without improving utilization.Rafay maximizes GPU infrastructure utilization by pooling resources, virtualizing, time-slicing, and controlling consumption to ensure efficient capacity allocation across your users and workloads.
Monetizing AI Infrastructure Requires More Than GPUs
Providing GPU access is only one part of delivering AI services. You also need a way to package infrastructure into consumable offerings, measure usage, and allocate costs accurately.Rafay serves as an AI service delivery platform that supports service catalogs, SKU creation, usage metering, chargeback, billing integrations, and token-metered AI services. This allows you to transform infrastructure investments into monetizable GPU-as-a-Service, Model-as-a-Service, and Inference-as-a-Service offerings.
Governance Becomes More Complex Across Hybrid and Sovereign Environments
AI infrastructure increasingly spans public cloud environments, private data centers, sovereign AI infrastructure, and air-gapped deployments. Maintaining consistent governance across these environments can quickly become challenging.Rafay provides a unified platform operating model that enables you to enforce policies, manage resources, and deliver self-service AI infrastructure consistently across hybrid, multi-cloud, sovereign, and regulated environments.
Consume AI Infrastructure on Your Terms
AI infrastructure is only valuable if teams can access it, use it, and turn it into services. As mentioned earlier, Rafay enables cloud-like GPU consumption across public cloud, private cloud, and hybrid environments while maintaining governance and multi-tenancy controls.
Whether you're delivering Model-as-a-Service offerings, GPU-as-a-Service platforms, supporting AI Factory initiatives, or enabling third-party AI platforms, Rafay goes beyond the infrastructure and turns it into revenue-generating services without increasing operational complexity.
Consume the Rafay Platform as a SaaS
Our fully managed SaaS model helps Rafay's customers start AI application delivery immediately. We deliver a self-service experience for platform teams, developers, and AI practitioners while meeting enterprise security requirements with SOC-2 Type compliance. In short, customers can begin delivering AI services immediately without managing the control plane themselves.
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Consume Infrastructure Across Data Centers and Cloud Environments
Whether deploying GPUs in multiple colors, leasing/renting GPUs in a CSP environment, or both, Rafay can help. Whether deploying GPUs across multiple colocation facilities, private data centers, public clouds, or leased cloud capacity, Rafay helps organizations transform those resources into governed, self-service services through a consistent AI infrastructure management platform.
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Consume the Rafay Platform in Air-Gapped Environments
For highly regulated industries, sovereign AI clouds, and security-sensitive deployments, Rafay can be deployed in a fully air-gapped architecture. Customers get the same SaaS-like experience on their terms, maintaining complete control of their environment while delivering the same self-service consumption model, governance capabilities, and operational experience across AI infrastructure.
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Deliver Self-Service AI Infrastructure at Scale
AI infrastructure orchestration becomes difficult to manage when multiple teams, environments, and services compete for resources. The Rafay Platform helps you operationalize self-service AI infrastructure, enabling you to scale AI initiatives without increasing operational complexity.
Scale Self-Service Compute Consumption with Confidence
By now, we’ve established that with Rafay, enterprises and service providers deliver self-service consumption across public cloud and data center environments. Developers gain on-demand access to infrastructure and AI tooling, whereas platform teams can streamline AI platform operations while reducing manual infrastructure management overhead.
Accelerated Computing Infrastructure Management
Rafay provides the platform foundation required to package infrastructure into consumable services while maintaining governance, security, and control. As such, you can deliver GPU-as-a-Service, AI Models-as-a-Service, and AI Factory capabilities without building a custom platform.
From GPU-as-a-Service to AI-as-a-Service
Cloud providers, neoclouds, and GPU cloud platform operators are moving beyond infrastructure delivery to offer AI services that can be consumed and monetized at scale. Rafay is leading the charge to operationalize GPU-as-a-Service, AI Models-as-a-Service, and AI Factory offerings through a self-service consumption model.
Trusted by leading enterprises, neoclouds and service providers
Turn AI Infrastructure into Consumable Services
GPUs alone do not create business value. That's why organizations need a way to package infrastructure into consumable services.
Rafay helps organizations transform GPUs, compute resources, and AI platforms into self-service solutions that support internal innovation, external customers, and new revenue streams.
Here's a quick overview of how this would work:
GPU clusters
GPU-as-a-Service
Compute resources
Self-service compute platforms
Foundation models
Models-as-a-Service offerings
Inference workloads
Inference-as-a-Service and token-metered AI APIs
AI platforms
Self-service AI services
Infrastructure spend
Monetizable and token-metered AI services
What Makes an AI Infrastructure Platform Enterprise-Ready?
Multi-Tenancy By Design
Multi-tenant AI infrastructure must support multiple teams, projects, customers, and environments without sacrificing performance or security. Rafay enables isolated, multi-tenant environments from a single platform, allowing organizations to deliver AI infrastructure and services to diverse user groups while maintaining operational consistency.
AI Infrastructure Governance and Policy Controls
Rafay helps platform teams enforce policies, standardize deployments, and maintain governance while enabling self-service consumption across private, public, hybrid, and sovereign cloud environments. As AI adoption grows, organizations need consistent controls across infrastructure, platforms, and services.
Quotas and Resource Management
Without clear controls, high-demand resources such as GPUs can become bottlenecks. Rafay helps organizations use their resources better by using quotas, usage policies, and consumption controls that improve utilization and stop resource contention.
Chargeback and Consumption Visibility
Rafay provides visibility into resource consumption, enabling chargeback and showback models that align infrastructure investments with business outcomes. Visibility into infrastructure consumption helps organizations allocate costs, optimize resource utilization, and scale AI operations more effectively.
Enterprise Security and Compliance
Rafay integrates with enterprise identity providers, role-based access controls, and existing security frameworks to help organizations operationalize AI infrastructure without compromising governance. As a result, AI infrastructure can meet the security, identity, and compliance requirements of modern enterprises.
What Makes an AI Infrastructure Platform Enterprise-Ready?
Multi-Tenancy By Design
Multi-tenant AI infrastructure must support multiple teams, projects, customers, and environments without sacrificing performance or security. Rafay enables isolated, multi-tenant environments from a single platform, allowing organizations to deliver AI infrastructure and services to diverse user groups while maintaining operational consistency.
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AI Infrastructure Governance and Policy Controls
Rafay helps platform teams enforce policies, standardize deployments, and maintain governance while enabling self-service consumption across private, public, hybrid, and sovereign cloud environments. As AI adoption grows, organizations need consistent controls across infrastructure, platforms, and services.
Learn More
Quotas and Resource Management
Without clear controls, high-demand resources such as GPUs can become bottlenecks. Rafay helps organizations use their resources better by using quotas, usage policies, and consumption controls that improve utilization and stop resource contention.
Learn More
Chargeback and Consumption Visibility
Rafay provides visibility into resource consumption, enabling chargeback and showback models that align infrastructure investments with business outcomes. Visibility into infrastructure consumption helps organizations allocate costs, optimize resource utilization, and scale AI operations more effectively.
Learn More
Enterprise Security and Compliance
Rafay integrates with enterprise identity providers, role-based access controls, and existing security frameworks to help organizations operationalize AI infrastructure without compromising governance. As a result, AI infrastructure can meet the security, identity, and compliance requirements of modern enterprises.
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Why Leading AI Service Providers Choose Rafay
Building AI infrastructure is one challenge. But building the platform required to deliver consumable, governable, and monetizable AI services is another. That’s why organizations choose Rafay to accelerate service delivery, simplify operations, and create new revenue opportunities from their AI investments.
Here's how:
Proven at Scale
Rafay supports enterprises, cloud providers, telecommunications companies, and sovereign AI cloud operators delivering AI infrastructure and services across public cloud, private cloud, and hybrid environments.
Accelerate Time to Revenue
Launching new AI services often requires significant operational overhead before customers can begin consuming them. Rafay helps providers transform GPU infrastructure faster by delivering the self-service capabilities, governance controls, and multi-tenant architecture required to launch services at scale.
We helped Buzz HPC achieve revenue generation in just five weeks after launching its AI infrastructure offering.
Reduce Operational Complexity
Rafay makes managing AI infrastructure across multiple teams and environments less overwhelming for engineering teams. Through a single platform operating model, infrastructure delivery, governance, and lifecycle management are simplified.
For instance, Freddie Mac reduced platform engineering operations from 37 engineers to just 3.
Launch AI Services Faster
AI service providers use Rafay to transform raw infrastructure into consumable services, including GPU-as-a-Service, AI Factories, AI Models-as-a-Service, and self-service AI platforms. This allows you to focus on delivering value to customers rather than building and maintaining custom operational tooling.
Indosat onboarded 28 enterprise customers within weeks of launching its AI service platform.
Built for AI-First Infrastructure
- NVIDIA AI Cloud-Ready validated platform
- NVIDIA Cloud Partner ecosystem alignment
- Recognized as a Leader and Outperformer in the GigaOm Kubernetes Radar Report
- Featured in the Deloitte Fast 500
- Recipient of Frost & Sullivan recognition
Focus on AI Innovation, Not Infrastructure
Experience unparalleled performance and scalability with the Rafay Platform GPU PaaS™ stack. Simplify AI infrastructure management and application delivery while reducing operational costs and enhancing productivity. The solution supports traditional and LLM-based (GenAI) models and offers users ways to efficiently use GPU resources with capabilities like GPU matchmaking, virtualization, and time-slicing–saving customers money and time-to-production.
“We are able to deliver new, innovative products and services to the global market faster and manage them cost-effectively with Rafay.”
Our focus on democratizing AI across India demands for us to move at lightning speed to deliver high-value data science experiences to developers. In working with NVIDIA and Rafay to deliver a PaaS for AI and GPU consumption, we are delivering the self-service experience developers and enterprises across India are looking for.
"With Rafay, we have complete peace of mind that our K8s clusters & apps are operating efficiently and securely."
We are thrilled to have collaborated with NVIDIA and Rafay in evaluating, and defining requirements for, a Platform-as-a-Service layer for AI application consumption. As part of the Indosat group, Lintasarta is playing a crucial role in not only paving the way for us to become an AI-native TechCo, but is also playing a leadership role in the industry to help steer the AI revolution in the right direction.
“One single tool, one single process, one single knowledge base helps us achieve efficiency. Less chaos, less complexity.”
Our work with Rafay in publishing the Platform-as-a-Service (PaaS) reference architecture gives enterprises, NVIDIA Cloud Partners (NCPs) and other GPU Cloud providers a path to delivering accelerated computing infrastructure, along with AI applications, in days. With the AI market moving so fast, time-to-market is key, and the Rafay Platform is a powerful enabler for NVIDIA customers looking to move fast.
"Rafay has streamlined the management and operations of 100+ Amazon EKS clusters while helping us enable developer self-service."
"The big draw was that you could centralize the lifecycle management & operations."
"Rafay’s thought leadership and white glove support has been fantastic."
"Choose packaged software distributions or cloud-managed services for production deployments that integrate different technology components, simplify life cycle management of that stack and provide multi-cloud management rather than a DIY approach."
“Rafay was up and running quickly, easy to use, and allowed us to deploy & manage standardized clusters anywhere."
"Easily operate and rapidly deploy applications anywhere across multi-cloud and edge environments."
"Rafay’s unified view for Kubernetes Operations & deep DevOps expertise has allowed us to significantly increase development velocity."
Questions and Answers About AI Infrastructure Management
Find answers to common questions about our enterprise solutions and how they can benefit you.
AI infrastructure management is the practice of turning GPUs, compute, AI platforms, and related resources into governed, self-service services that can be consumed, managed, and scaled efficiently. It combines infrastructure provisioning with governance, multi-tenancy, usage controls, and service delivery.
Rafay helps organizations transform GPU infrastructure into governed, self-service services that can be consumed by developers, business units, customers, and tenants. Through capabilities such as multi-tenancy, service catalogs, usage metering, chargeback, policy controls, and workflow automation, Rafay enables organizations to deliver GPU-as-a-Service, Model-as-a-Service, inference services, and self-service AI platforms at scale.
The Rafay Platform enables organizations to package GPU resources into self-service offerings that users can consume on demand. The platform provides governance, quotas, tenant isolation, usage visibility, and chargeback capabilities required to operate GPU-as-a-Service at scale.
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.
We track infrastructure and service consumption, collects detailed usage data, and supports chargeback and showback models. Usage information can be integrated with existing billing systems to support monetization and cost allocation.
No. While Kubernetes lifecycle management is a foundational capability, Rafay goes beyond cluster management to help organizations deliver GPU-as-a-Service, self-service compute, AI platforms, model services, inference endpoints, and governed multi-tenant consumption experiences across cloud, data center, and sovereign environments.
The Definitive GPU PaaS Reference Architecture
Understand what it takes to deliver the right GPU infrastructure to your business.
