Transforming GPU-As-A-Service Into AI Cloud With Rafay
Techstrong.ai, Friday, October 10th, 2025
The conversation around AI infrastructure is often dominated by hardware, specifically GPUs. While essential, this focus misses a larger, more critical point: hardware alone is not enough. The challenge, and the real opportunity, lies in bridging the vast gap between renting raw GPU power and delivering a true, self-service AI cloud experience.
At AI Infrastructure Field Day 3, Rafay CEO Haseeb Budhani outlined why providers must transition from GPU-as-a-Service to AI cloud. He also explains how Rafay's platform bridges this gap by offering automation, governance, and cost-effective solutions tailored for AI infrastructure.
The Problem with "GPU-as-a-Service"
Many GPU providers and enterprises are discovering that simply offering 'GPU-as-a-Service' is not a sustainable business model. The margins are thin, and the operational overhead is immense. To truly compete and thrive, they need to emulate the hyperscalers by providing a seamless, application-centric platform that empowers developers and simplifies consumption.