Red Hat released Red Hat AI 3.5 this week, a move designed to let software engineering teams run AI with the same operational rigor as enterprise apps on mission-critical infrastructure.

Echoing both the “pilots-to-production” and “single control plane for infrastructure, models, and agents” narratives playing out across much of the tech industry, Red Hat’s key move here seems to be its expansion of platform capabilities to run enhanced multi-tenancy for AI service providers. 

Crucially, the new AI platform release is built to run AI use cases that require complete hardware-to-software isolation (needed when AI workloads have to wrangle sensitive data, proprietary models, and regulated information), as well as handle priority-aware service requests (where mission-critical workloads execute in favor of lower-grade tasks) with native multi-tenancy on a shared GPU infrastructure. 

Red Hat’s Senior Director of Product for Red Hat AI is Tushar Katarki. He tells The New Stack that running enterprise AI without safety controls is “like driving a supercar blindfolded” in real terms.

“With Red Hat AI 3.5, we are delivering the operational guardrails, verifiable trust, and multi-tenant controls needed to run AI as a mission-critical service rather than an unpredictable experiment,” Katarki says. “You can’t scale what you can’t measure, and you certainly shouldn’t deploy what you can’t verify. By unifying pre-deployment safety benchmarking, real-time observability, and GPU resource management, we are giving platform teams the power to turn isolated AI pilots into a fully governed enterprise architecture.”

, tells The New Stack that Red Hat’s work is of the time and of the moment; primarily because “every GPU request now becomes a priority decision”, so one developer’s internal experiment cannot be treated with the same urgency as a financial close.

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