How Sabre turned x86 efficiency into AI investment

As AI inference demand surges and enterprise compute budgets buckle under expanding workloads, x86 efficiency has emerged as one of the most powerful — and underutilized — levers in cloud financial management.

The pressure is showing up at the intersection of hybrid cloud and financial operations, where platform choice is becoming one of the fastest ways to cut costs. Of course, the sudden mania driving cloud spend upwards has little to do with new architecture, according to Mike Thompson (pictured, right), director of cloud product and go-to-market at Advanced Micro Devices Inc.

“The biggest change this year that I see is simply demand for compute because AI, especially for inference, is becoming more prevalent,” Thompson said. “We spent years training the model. That’s what drove a lot of the economy for the last three, four, five years. Now, we have those models ready to take to market so that people can actually use them. We’re moving from training to inference and that is creating a huge increase in demand for compute in the cloud and on-premise.”

Thompson and Tim McArdle (left), senior FinOps engineer at Sabre Corp., spoke with theCUBE’s John Furrier and Alison Kosik at Google Cloud Next 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed AMD’s x86 efficiency gains, FinOps discipline and how Sabre is reinvesting infrastructure savings into AI initiatives. (* Disclosure below.)

x86 efficiency drives FinOps reinvestment for AI

As AI cloud costs escalate and FinOps practices evolve, enterprises are learning that governance dashboards alone won’t save them. Instead, platform selection is the real lever for bending the cost curve. The math turns punishing fast: Many organizations run servers at only about 10% utilization, and the compounding inefficiency of multi-architecture environments makes that hole even deeper, Thompson noted. The answer increasingly points to x86 — the compute standard so deeply embedded across on-prem and cloud infrastructure that moving workloads between the two rarely requires a second thought.

“Most of the enterprise and larger customers that I see out there … are running on-premise in their own real estate, but also running in cloud,” Thompson added. “When you’re running containers, that’s one of the things that makes it easier. You have a container, it contains everything you need. You can drop it in. It’s really hard to run a hybrid environment with containers on Arm because generally those servers aren’t available on-prem. I see containers being leveraged specifically on x86 because of the ease of migration between the two.”

The business case at Sabre illustrates the opportunity directly. The travel technology company — whose platform underpins flight bookings across virtually every major airline and has operated at scale since 1960 — migrated a CPU-intensive workload to AMD-based instances on Google Cloud, deploying more than 50,000 virtual CPUs with zero code changes and achieving immediate cost and footprint reductions, McArdle pointed out. Those savings didn’t sit idle — they went straight into AI.

“When we moved to the AMD platform we experienced a price benefit. It’s faster and we have a smaller footprint and we made zero code changes,” he said. “For us, that’s a huge win-win-win. We are able to take that savings and invest it in the new world of agentic AI. That has helped us immensely.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Google Cloud Next 2026:

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

Photo: SiliconANGLE