Most people think optimizing AI for cost means optimizing the LLM. It doesn’t. The real lever is the semantic engine underneath, the one that routes every query to the cheapest, correct path in the warehouse before the LLM ever guesses.
What you’ll learn:
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- Why the 21,000x efficiency gain isn’t marketing math — and how to check the work yourself
- How a Tier 1 bank’s commercial banking division measured $17.93 vs. $0.0008 for the same five queries
- Why prompt engineering, metadata catalogs, dbt, and MCP alone all failed the production test
- The four criteria any solution must meet before it qualifies for an AI analytics workload
- A side-by-side comparison of AtScale vs. Atlan, Denodo, dbt, and MCP on what actually matters
