# 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. Rafay provides that operating layer. ## 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 ### New revenue streams through AI services and marketplaces By turning infrastructure into a platform, AI factories become engines for innovation and growth—not cost centers. ## 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. ## 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: - Internal chargeback and external billing models for AI services - Cost visibility and control - External billing and revenue generation ## AI Factory vs Traditional AI Infrastructure Traditional Infrastructure | AI Factory --- | --- 1 Typical Process: Manual provisioning | 1 Process with Rafay: Self-service access 2 Typical Process: Siloed environments | 2 Process with Rafay: Multi-tenant platform 3 Typical Process: No standard packaging | 3 Process with Rafay: SKU-based consumption 4 Typical Process: Limited visibility | 4 Process with Rafay: Usage and cost tracking 5 Typical Process: Infrastructure-focused | 5 Process with Rafay: Service and outcome-focused ## Turn Your Infrastructure into an AI Factory Move beyond GPUs and clusters. Build a platform that delivers AI at scale.