Rafay Systems targets the operating layer of the AI infrastructure boom

Coverage from SiliconANGLE's livestreaming video studiohelp_outline UPDATED 22:12 EDT / AUGUST 03 2026 AI Rafay Systems targets the operating layer of the AI infrastructure boom by John Furrier The AI infrastructure market is moving through a critical transition. The first phase was about acquiring graphics processing units and standing up capacity. The next phase is about turning that expensive hardware into a secure, reliable and profitable cloud service. That is where Rafay Systems Inc. sees its opportunity. In my recent conversation with Haseeb Budhani (pictured), co-founder and chief executive officer of Rafay Systems, we examined the operational pressure facing neoclouds, sovereign cloud providers, telecommunications companies and enterprises as they deploy increasingly large AI systems. The demand is unprecedented. Providers are buying infrastructure at massive scale, often with customers already waiting for capacity. But buying GPUs does not create a cloud. Operators still need orchestration, networking, security, multitenancy, observability, auditing and a developer experience that lets customers consume infrastructure without a lengthy manual process. The central issue is time to revenue. AI infrastructure is expensive, depreciates quickly and must begin generating returns as soon as possible. “End of the day, the thing that matters most is faster time to market and a better user experience,” Budhani said. “And if you can deliver both of those things, everybody wins.” I recently spoke with Budhani during an exclusive CUBE Conversation to discuss the software and operational requirements behind large-scale AI infrastructure, the rise of sovereign and neocloud providers and the pressure to turn GPU capacity into revenue. (* Disclosure below.)  AI infrastructure must become a service Budhani’s definition of a cloud is straightforward: Customers should be able to visit a portal or use an application programming interface, press a button and receive an AI service in a multitenant environment. “My definition of a cloud is where you must be able to go to a cloud provider’s page, click a button, and get an AI use case in a multi-tenant fashion,” he said. “I don’t want to talk to anybody. If you can do that, you’re a cloud. If you can’t do that, you’re not a cloud. You’re custom infrastructure.” That distinction matters because many emerging AI providers are still building the operational capabilities that Amazon Web Services Inc., Microsoft Corp. and Google LLC developed over more than a decade. The hyperscalers had thousands of engineers and years to build their control planes. Today’s AI clouds may have only months. They cannot afford to rebuild every layer themselves. Rafay’s role is to provide the operating software between the hardware and the customer experience. The company helps providers expose bare metal, Kubernetes environments, virtual machines, serverless services, open-source models and token-based offerings through a common platform. “The right AI cloud in this day and age, they do all of these things,” Budhani said. “It’s not just about bare metal or not just about Kubernetes or VM or tokens. It’s everything.” That breadth allows an AI cloud to serve multiple customer segments. A large model developer may want Kubernetes. Another customer may want bare metal. An enterprise developer may expect a serverless experience, while another organization may simply want to purchase and distribute tokens. The economic model improves as providers move higher in the stack. Bare-metal capacity may generate predictable revenue, but managed services and toke