Enterprises have spent the past year spinning up artificial intelligence pilots, watching costs spiral and wondering when proof-of-concept magic will translate into production value. The gap between experimental models and scalable enterprise AI deployment has become an expensive problem with no clear path forward.

Red Hat Inc.’s latest platform release targets that bottleneck head-on, emphasizing inference at scale and agentic AI. The update is built to align seamlessly with existing enterprise workflows, eliminating the need for major infrastructure overhauls, according to Jennifer Vargas (pictured, left), senior principal marketing manager at Red Hat.

Red Hat’s Jennifer Vargas and James Harmison talk with theCUBE about the company’s strategy for scaling AI inference in enterprise Kubernetes environments.

“Everyone is trying to look for the ROI,” she told theCUBE. “It’s really expensive to deploy AI at the scale that everyone wants. On the enterprise, [they] need to demonstrate ROI very quickly. I think agentic AIs probably are key to that because it will help enterprises match their workflows. Enterprises already understand automation, how to automate processes. What they need is to take it to the next level.”

Vargas and James Harmison (right), senior principal technical marketing manager at Red Hat, spoke with theCUBE’s Rob Strechay and Savannah Peterson at the KubeCon + CloudNativeCon NA event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed Red Hat’s strategy for scaling AI inference in enterprise Kubernetes environments and its approach to open-source contributions across the cloud-native ecosystem. (* Disclosure below.)

Building in the open, scaling enterprise AI with intention

Red Hat’s approach to AI development centers on community contributions and governance that extend beyond corporate interests, with engineers given the freedom to collaborate across open-source projects before products take shape, according to Harmison. This open development model allows the company to identify which technologies gain traction organically before committing to long-term enterprise support.

“We build everything in the open from the get-go to start with,” he said. “When we talk about donating something to the CNCF, it’s not because we weren’t building in the open already; we usually were, and we’re hoping to get community contributions and people interested and excited about the technology. We generally hit this sort of critical mass where enough people become really interested in a technology that we know it’s kind of a hit in the open-source community and people are finding value in it.”

The company’s Red Hat AI 3 platform prioritizes flexibility and interoperability, allowing enterprises to integrate their own tools and frameworks while maintaining the resilience and reliability required for production environments, according to Vargas. Red Hat’s strategy balances providing opinionated tool recommendations with the freedom for customers to bring their own open-source technologies into the enterprise AI stack.

“One of the things that we do is to make sure that we give customers interoperability,” she said. “You need applications or technologies that work together that are resilient, that are reliable. That’s the part where we put the spark on our products … when everyone’s looking for an AI that’s transparent, that is trustworthy, that is reliable.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the KubeCon + CloudNativeCon NA event:

(* Disclosure: Red Hat sponsored this segment of theCUBE. Neither Red Hat nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE