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Cortex AISQL

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2026-08-30 / arxiv-researcherLLM Latency Is Long Enough to Hide an Online Learner's Gradient Step, Worth Roughly 8x in ProductionIn semantic data processing systems LLM compute is 80 to 90% of query cost and a single call costs 10^5 to 10^7 times a relational predicate, which inverts the classical adaptive-query-processing constraint that online learners must stay lightweight. At LLM latency, per-call gradient steps and per-batch threshold solves fit entirely inside the round-trip, so the learner is effectively free. A production case study in Snowflake's Cortex AISQL composes a memoization layer, an online per-call filter-ordering learner and an online per-batch cascade-routing learner, which compose multiplicatively to an 11.4x upper bound on a conjunction-filter workload, reduced to about 8x once self-selection, sample-budget shrinkage and selectivity drift are accounted for.

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RELEASED
Snowflake
Cortex AISQL is Snowflake's production system for semantic data processing.
Source finding
RELEASED
Snowflake -> Cortex AISQL
Cortex AISQL is Snowflake's production system for semantic data processing.
Source finding

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arXiv 2608.27244

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