Berkeley open-sources Quail, an ultra-high throughput AI-SQL engine with query plans
sh_reya · x · 2026-09-25
UC Berkeley's Full Stack Data Lab released Quail, an MIT-licensed ultra-high throughput AI-SQL engine built with Charles Frye of Modal, with a blog post, code, and playground demo.
- Context: AI-SQL (LLM-powered SQL functions, now offered by Snowflake Cortex, BigQuery, Databricks, MotherDuck) can trigger hundreds of thousands to millions of LLM calls per query, making it extremely expensive.
- Prior work: Academic systems like DocETL (Berkeley), LOTUS (Stanford), Palimpzest (MIT), ThalamusDB (Cornell) cut calls via optimizations like MOAR, Task Cascades, Abacus and cheaper-model routing (BARGAIN).
- Quail's key idea: organize execution around query plans; the new release adds AI filters and joins.
- Known gap: on agent-trace queries with matching prefixes, vLLM's automatic prefix cache is 2.32x faster — prefix caching is on Quail's roadmap.
Related event: UC Berkeley and Modal Open-Source Quail, an AI-SQL Inference Engine(25 posts)→
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