As AI rapidly rewrites enterprise economics, cloud cost optimization has become essential for preventing budgets from collapsing under AI workloads, enabling organizations to slash expenses and redirect engineering talent from reactive firefighting to higher-value innovation.

Nordstrom Inc. learned that lesson firsthand after migrating its data warehouse to Google Cloud LLC’s BigQuery, a move that exposed just how quickly an unoptimized platform can send costs skyward, according to Bryce Ageno (pictured, right), principal software engineer at Nordstrom. That’s where Rabbit Ltd.’s automated platform came in — cutting through the complexity BigQuery’s reservation system is known for with results that required little more than a few clicks.

“Literally pointed at it, clicked on it, 47% savings on our BigQuery reservation costs,” Ageno said. “That feature is worth it by itself, but there’s a bunch of other features that they have around across all of [Google Cloud Platform] for [Google Kubernetes Engine] automation for cloud storage costs, reduction [and] recommendations on how you structure your tables.”

Ageno and Balazs Molnar (left), co-founder and chief executive officer of Rabbit, spoke with theCUBE’s John Furrier and Alison Kosik at Google Cloud Next, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how automated cloud cost optimization is unlocking engineering capacity and reshaping the economics of enterprise AI. (* Disclosure below.)

Cloud cost optimization unlocks engineering capacity and AI investment

The gap between traditional FinOps dashboards and actual automated savings is where Rabbit’s value proposition lives, Molnar explained. According to the State of FinOps 2026 report, 98% of practitioners now manage AI spend — yet most organizations still overspend on AI workloads by four to five times their original budget.

“If you look at what the FinOps framework is [it’s] basically about showing you where you can optimize,” Molnar said. “The problem is you still need to do that optimization. Teams at enterprises like Bryce’s team — their job is not to optimize. Their job should be building stuff.”

The downstream impact at Nordstrom went well beyond the bill, according to Ageno. With BigQuery reservation costs cut substantially, the cost governance team was able to free senior engineers from reactive SQL tuning and redirect them toward higher-value work — data modeling, catalog integration and ingestion automation. What had been a top-priority cost workstream dropped in urgency almost overnight.

“We didn’t need to do as much now,” Ageno said. “I was like, ‘Oh, I could release a lot of our seniors to work on other stuff now.'”

The forward-looking challenge is that AI introduces a cost complexity that makes the BigQuery problem look simple by comparison. Unlike reserved cloud slots, AI spending varies wildly based on model selection, caching strategy and query volume — and the research shows enterprises are only beginning to build the governance frameworks needed to manage it. Take, for example, teams using frontier models to draft internal emails, with each message potentially costing $5 in tokens, Molnar noted.

“If you accept that AI is going to be the core of every single organization moving forward, I think there are really three things that you have to figure out,” Molnar said. “First, how are you going to pay for it? Second, how are you going to make it economical? And third, how are you going to use AI for the first two?”

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

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

Photo: SiliconANGLE