# Elevate Your AI Factory. Run It on Rafay.

**Rafay transforms GPU infrastructure into self-service, governed AI platforms that deliver applications and services at scale. With built-in usage tracking and monetization capabilities, organizations move from deploying GPUs to operating AI platforms.**

Organizations have already invested billions in accelerated compute. But GPUs alone don’t create outcomes. Without a scalable operating model, infrastructure remains fragmented, underutilized, and difficult to consume.

The world’s largest AI factories succeed by turning infrastructure into a platform—where developers, data scientists, and customers can access AI environments on demand, with governance, visibility, and cost control built in from day one.

## What Is an AI Factory?

An AI Factory is an operating model that transforms GPU infrastructure into a self-service, multi-tenant platform for building, deploying, and delivering AI applications and services.

## Why AI Factories Are Needed

AI infrastructure is widely deployed, but difficult to operationalize and scale across teams.

Most organizations face the same challenges:

- GPUs are available, but access is manual and slow
- Environments are inconsistent across teams
- Infrastructure is siloed and underutilized
- Usage is difficult to track, govern, or attribute to cost

This creates a gap between **infrastructure investment and usable AI outcomes**.

AI Factories close this gap by introducing a platform model for how infrastructure is consumed, governed, and delivered as services.

# What Defines an AI Factory?

AI Factories extend beyond infrastructure. They introduce a consumption and operating layer with five core capabilities:

### Self-Service AI Consumption

Developers provision compute, environments, and AI services on demand without tickets or manual setup.

### Multi-Tenant Governance

Infrastructure is securely shared across teams, customers, or business units with isolation, access controls, and policy enforcement.

### Standardized SKUs and Environments

Compute, AI workspaces, and applications are packaged into repeatable offerings that are deployed consistently across environments.

### Integrated Usage Tracking and Cost Control

All usage is measured and attributed, enabling chargeback, cost visibility, and operational accountability.

### AI Application and Model Delivery

AI Factories deliver not just infrastructure, but models, APIs, and applications that are consumed directly by developers and end users.

## What Leading AI Factories Achieve

Organizations using Rafay to power AI factories unlock measurable outcomes:

### Faster time from infrastructure to production AI services

### Higher GPU utilization through shared, multi-tenant consumption models

### Reduced operational overhead with automated lifecycle management

By turning infrastructure into a platform, AI factories become engines for innovation and growth—not cost centers.

## Proven in Production AI Factories

### TELUS Launches a Sovereign, Developer-Ready AI Studio

Rafay powers real-world AI factories across telecom, cloud providers, and enterprises. For example, TELUS built a sovereign AI factory that enables developers to provision GPU-powered environments on demand, access curated model catalogs, and deploy production-ready AI services—all within a governed, multi-tenant platform.

This model is becoming the standard for AI infrastructure globally.

## The Rafay Advantage

AI Factories require more than infrastructure orchestration. They require a complete operating model for how infrastructure is consumed, governed, and monetized.Rafay delivers this through four core layers:

### The Orchestration Layer

_Operationalizes GPU infrastructure_

Automates provisioning and lifecycle management of Kubernetes clusters, GPU resources, and environments across data centers and public clouds.

### The Consumption Layer

_Enables self-service AI access_

Provides developer-ready portals and APIs where users can:

- Provision compute resources effortlessly
- Launch environments instantly
- Deploy AI workloads without manual intervention

### The Governance Layer

_Applies control and compliance at scale_

Enforces:

- Multi-tenant isolation
- Role-based access control
- Quotas and policy guardrails

### The Monetization Layer

_Tracks, attributes, and monetizes usage_

Captures usage across infrastructure, environments, and AI services to enable:

- Enables internal chargeback and external billing models for AI services
- Cost visibility and control
- External billing and revenue generation

This is what turns AI infrastructure from a cost center into a revenue-generating platform.

## AI Factory vs Traditional AI Infrastructure

Traditional Infrastructure vs AI Factory

| **Typical Process**               | **Process with Rafay**            |
|-----------------------------------|-----------------------------------|
| Manual provisioning                | Self-service access                |
| Siloed environments                | Multi-tenant platform              |
| No standard packaging              | SKU-based consumption              |
| Limited visibility                 | Usage and cost tracking            |
| Infrastructure-focused             | Service and outcome-focused        |

## Turn Your Infrastructure into an AI Factory

Move beyond GPUs and clusters. Build a platform that delivers AI at scale.