When the Index Tuner's Cost Model Lies: Where LLMs See What DTA Can't
A Microsoft team evaluates LLM-driven index tuning on real enterprise customer workloads. On query 22 of Real-R, the SOTA commercial tuner DTA recommends indexes that cause a near-10x regression; on the same query, GPT-5 cuts execution time from 10 seconds to 4. The LLM wins precisely where the what-if cost model is wrong. But that intuition is high-variance, can’t be bolted into the existing architecture, and can’t be validated cheaply — it’s not a replacement for DTA today, it’s a source of the candidate indexes DTA can’t see.