Enterprises are spending heavily on AI agents and copilots to query their data warehouse. When those systems cost more w
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A Tier 1 bank cut AI compute costs 21,000x
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Enterprises are spending heavily on AI agents and copilots to query their data warehouse. When those systems cost more with every question, and the accuracy isn’t guaranteed, the ROI conversation stalls.
A semantic engine in front of the warehouse closes the gap. It stops the LLM from guessing at schemas and business logic on every prompt, and routes each query to the cheapest correct path instead.
AtScale’s new case study documents a production benchmark run by the commercial banking division within a multinational Tier 1 bank, measuring exactly what that architecture is worth.
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You’ll learn:
Why the real cost lever is the layer underneath the LLM, not the LLM itself The actual SQL behind five everyday banking questions, guided vs. unguided Why the same five queries cost $17.93 through the unguided path and $0.0008 through the guided one Why the bank ruled out prompt engineering, metadata catalogs, dbt, and MCP alone before landing on this approach
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