AI Infrastructure Management for Neoclouds and Enterprises
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
How Do You Make GPU Infrastructure Self-Service for Developers and AI Teams?
Rafay gives developers, data scientists, and tenants on-demand access to GPU infrastructure without relying on tickets, manual approvals, or one-off provisioning workflows. Teams can consume approved infrastructure and environments through self-service workflows while platform operators maintain governance, visibility, and control.
Multi-Tenant AI Infrastructure Is Hard to Govern
How Do You Securely Share AI Infrastructure Across Teams and Tenants?
Rafay enables multiple teams, projects, and customers to share AI infrastructure with built-in tenant isolation, role-based access controls, quotas, and policy enforcement. This helps organizations scale shared infrastructure while reducing resource contention and maintaining consistent governance across users and workloads.
GPU Utilization Often Falls Short
How Do You Improve GPU Utilization Across Teams and Workloads?
Rafay helps organizations pool and share GPU resources across users and workloads instead of leaving capacity stranded in isolated environments. Capabilities such as virtualization, time-slicing, quotas, and consumption controls help operators allocate capacity more efficiently and make high-value GPU infrastructure available to more users.
Monetizing AI Infrastructure Requires More Than GPUs
How Do You Go From GPU Provisioning to Customer Billing?
Providing GPU access is only one part of delivering AI services. Rafay helps operators package infrastructure into consumable offerings, define SKUs, meter usage, support chargeback, and integrate usage data with billing systems. This enables providers to turn infrastructure into monetizable GPU-as-a-Service, Model-as-a-Service, and inference offerings.
Governance Becomes More Complex Across Hybrid and Sovereign Environments
How Do You Maintain Consistent AI Infrastructure Governance Across Environments?
AI infrastructure can span public cloud, private data centers, sovereign regions, and air-gapped environments. Rafay provides a consistent operating model for enforcing policies, managing access and resources, and maintaining governance across these environments. This allows organizations to scale AI infrastructure without creating separate operational processes or governance models for every deployment location.
What Do You Still Have to Build After Buying an AI Infrastructure Platform?
With Rafay, organizations do not need to build the core operating layer required to turn AI infrastructure into a self-service, multi-tenant platform. The Rafay Platform provides orchestration, service catalogs, tenant and identity controls, policy enforcement, quotas, usage metering, chargeback capabilities, lifecycle automation, and APIs for delivering infrastructure and AI services.
Customers may still integrate Rafay with their preferred infrastructure, networking, storage, billing, identity, observability, and customer-facing systems. Rafay is designed to orchestrate and connect those components rather than require customers to replace their existing technology stack.
For organizations building a NeoCloud or AI Factory, this reduces the amount of custom platform engineering required between acquiring infrastructure and making it consumable by developers or customers. Rafay’s current platform positioning already emphasizes a single operating layer rather than a patchwork of point solutions.
Consume AI-powered 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 colos, 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 Stacks 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
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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-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.
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
How Many Engineers Does It Take to Operate AI Infrastructure at Scale? 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. However, there is no universal engineer-to-infrastructure ratio because staffing depends on environment size, architecture, services offered, support requirements, and the amount of automation in place. Rafay is designed to reduce the platform engineering effort required to operate AI infrastructure at scale. Rafay automates infrastructure lifecycle management, provisioning, governance, policy enforcement, tenant operations, and self-service workflows so teams do not have to manage those processes manually for every environment.
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."
Frequently Asked Questions
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.
With Rafay, organizations do not need to build the core operating layer required to turn AI infrastructure into a self-service, multi-tenant platform. The Rafay Platform provides orchestration, service catalogs, tenant and identity controls, policy enforcement, quotas, usage metering, chargeback capabilities, lifecycle automation, and APIs for delivering infrastructure and AI services.
Customers can still integrate Rafay with their preferred infrastructure, networking, storage, billing, identity, observability, and customer-facing systems.
Multi-tenant AI infrastructure must support multiple teams, projects, customers, and environments without sacrificing security or operational control. Rafay enables isolated, multi-tenant environments with role-based access controls, quotas, policy enforcement, tenant boundaries, and usage attribution across shared infrastructure.
This allows organizations to securely share AI infrastructure while maintaining consistent governance across users and workloads.
Providing GPU access is only one part of delivering AI services. Rafay helps operators package infrastructure into consumable offerings, define SKUs, meter usage, support chargeback, and integrate usage data with billing systems.
This enables providers to turn infrastructure into monetizable GPU-as-a-Service, Model-as-a-Service, and inference offerings.
The Rafay Platform unifies the operations and consumption of AI infrastructure through a single control plane. Operators can manage bare metal, virtual machines, Kubernetes, SLURM, GPU resources, and AI services from one platform instead of relying on a patchwork of point solutions.
For providers, this creates a consistent foundation for delivering multiple consumption and monetization models across the same AI infrastructure estate.
There is no universal staffing ratio because requirements depend on environment size, architecture, services offered, and the level of automation in place. Rafay is designed to reduce the platform engineering effort required to operate AI infrastructure by automating provisioning, lifecycle management, governance, policy enforcement, and self-service workflows.
Rafay has documented customer examples in which small platform teams manage large-scale environments, including Freddie Mac reducing its platform engineering team from 37 engineers to 3.
Troubleshooting AI infrastructure requires visibility across the layers that can affect workload performance, including applications, Kubernetes, GPUs, servers, storage, and networking. Rafay provides centralized operational visibility across the environments it manages and integrates with monitoring and infrastructure technologies across the AI stack.
This gives operators a more consistent way to identify and resolve issues without troubleshooting each environment in isolation.
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.
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.
GPU PaaS Reference Architecture
Understand what it takes to deliver the right GPU infrastructure to your business.
