Democratizing GPU Access: How PaaS Self-Service Workflows Transform AI Development

A surprising pattern is emerging in enterprises today: End-users building AI applications have to wait months before they are granted access to multi-million dollar GPU infrastructure. 

The problem is not a new one. IT processes in most enterprises are a series of steps that span multiple teams and multiple automation workflows. The result is a multi-week timeline that must be followed to service a request from an end-user in need of a compute environment. In the pre-AI world, most developers got used to these delays, and managers viewed these delays as the cost of doing business. But when it comes to GPU-based infrastructure that costs millions of dollars, and given the visibility that AI projects have in enterprises, such delays are no longer acceptable.

Rafay’s Approach to Self-Service GPU Access

Rafay has developed a comprehensive platform that transforms how organizations deliver GPU resources through a self-service model. Our approach creates an experience layer between technical infrastructure and AI teams, allowing for the delivery of GPU resources through an intuitive catalog. 

The platform’s multi-tenant architecture enables organizations to share GPU infrastructure efficiently while maintaining strong isolation between different users and workloads. This isolation happens at multiple levels, creating a secure foundation for self-service access.

Instead of exposing complex infrastructure details, Rafay presents GPU resources and AI environments as a simple, intuitive catalog of options, in business-relevant terms rather than infrastructure jargon. Our platform allows administrators to create custom service and compute profiles tailored to their users’ needs.

Key Platform Capabilities

Organizations adopting Rafay’s approach benefit from three critical capabilities:

Self-service resource provisioning transforms how users access GPU infrastructure. The platform provides an intuitive portal for teams to instantly deploy the AI or cloud native applications they need. These range from compute clusters to specialized workbenches and it can all be done without specialized infrastructure knowledge or IT intervention. 

Secure multi-tenant foundations enable resource sharing without compromising isolation between users and projects. Rafay’s architecture implements multiple isolation layers that maintain security while maximizing resource efficiency.

Governance and visibility that provide insights into resource usage, costs, and performance. The platform includes comprehensive monitoring, logging, and chargeback capabilities to ensure accountability.

Rafay’s platform enables AI initiatives to move from concept to production faster, making organizations more responsive to emerging opportunities. By transforming GPU infrastructure from a gated resource to a self-service capability, Rafay fundamentally changes what’s possible with enterprise AI: higher GPU utilization, broader AI adoption across business units, and the ability to run multiple secure workloads on shared infrastructure.

Ready to Transform Your GPU Infrastructure?

The future of AI development belongs to organizations that can put GPU power directly in the hands of those creating business value. Learn more about Rafay’s approach in this NVIDIA blog: “How Rafay’s Self-Service Platform Delivers NVIDIA Accelerated Computing for Enterprise AI Workloads.”

Contact us today to discover how we can transform your GPU infrastructure from a bottleneck to a business accelerator.