# Cortex AISQL: A Production SQL Engine for Unstructured Data

arXiv: https://arxiv.org/abs/2511.07663 · HTML: https://mlsanity.com/paper/2511.07663/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 41 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 23 of 23)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://mlsanity.com/paper/2305.05176/) (2023)
- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes](https://mlsanity.com/paper/2304.09433/) (2023)
- [Neo: A Learned Query Optimizer](https://mlsanity.com/paper/1904.03711/) (2019)
- [Learned Cardinalities: Estimating Correlated Joins with Deep Learning](https://mlsanity.com/paper/1809.00677/) (2018)
- [Bao: Learning to Steer Query Optimizers](https://mlsanity.com/paper/2004.03814/) (2020)
- [DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing](https://mlsanity.com/paper/2410.12189/) (2024)
- [ALEX: An Updatable Adaptive Learned Index](https://mlsanity.com/paper/1905.08898/) (2019)
- [BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics](https://mlsanity.com/paper/1805.01046/) (2018)
- [Deep Unsupervised Cardinality Estimation](https://mlsanity.com/paper/1905.04278/) (2019)
- [Deep Reinforcement Learning for Join Order Enumeration](https://mlsanity.com/paper/1803.00055/) (2018)
- [UQE: A Query Engine for Unstructured Databases](https://mlsanity.com/paper/2407.09522/) (2024)
- [DeepDB: Learn from Data, not from Queries!](https://mlsanity.com/paper/1909.00607/) (2019)
- [The Design of an LLM-powered Unstructured Analytics System](https://mlsanity.com/paper/2409.00847/) (2024)
- [Approximate Selection with Guarantees using Proxies](https://mlsanity.com/paper/2004.00827/) (2020)
- [SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models](https://mlsanity.com/paper/2311.09818/) (2023)
- [Semantic Indexes for Machine Learning-based Queries over Unstructured Data](https://mlsanity.com/paper/2009.04540/) (2020)
- [Towards Accurate and Efficient Document Analytics with Large Language Models](https://mlsanity.com/paper/2405.04674/) (2024)
- [Rational Tuning of LLM Cascades via Probabilistic Modeling](https://mlsanity.com/paper/2501.09345/) (2025)

## Built on by (most cited first, 11 of 11)

- [Larch: Learned Query Optimization for Semantic Predicates](https://mlsanity.com/paper/2606.07923/) (2026)
- [Streaming Model Cascades for Semantic SQL](https://mlsanity.com/paper/2604.00660/) (2026)
- [AvalancheBench: Evaluating Enterprise Data Agents Through Latent World Recovery](https://mlsanity.com/paper/2605.24183/) (2026)
- [The Stochastic Shift: A New Evaluation Paradigm for Text-to-SQL with AI Operators](https://mlsanity.com/paper/2609.21133/) (2026)
- [Towards Effective Orchestration of AI x DB Workloads](https://mlsanity.com/paper/2603.03772/) (2026)
- [Agent-Aided Design for Dynamic CAD Models](https://mlsanity.com/paper/2604.15184/) (2026)
- [Evergreen: Efficient Claim Verification for Semantic Aggregates](https://mlsanity.com/paper/2604.26180/) (2026)
- [MLSkip: Data Skipping for ML Filters via Lightweight Metadata](https://mlsanity.com/paper/2606.03946/) (2026)
- [SemJoin: Semantic Join Optimization](https://mlsanity.com/paper/2606.29532/) (2026)
- [Compositional Online Learning for Semantic Data Processing Systems](https://mlsanity.com/paper/2608.27244/) (2026)
- [Towards Reliable AI Data Scientists: Data Agents with Workflow Harnesses](https://mlsanity.com/paper/2609.35255/) (2026)

## Abstract (arXiv)

Snowflake's Cortex AISQL is a production SQL engine that integrates native semantic operations directly into SQL. This integration allows users to write declarative queries that combine relational operations with semantic reasoning, enabling them to query both structured and unstructured data effortlessly. However, making semantic operations efficient at production scale poses fundamental challenges. Semantic operations are more expensive than traditional SQL operations, possess distinct latency and throughput characteristics, and their cost and selectivity are unknown during query compilation. Furthermore, existing query engines are not designed to optimize semantic operations. The AISQL query execution engine addresses these challenges through three novel techniques informed by production deployment data from Snowflake customers. First, AI-aware query optimization treats AI inference cost as a first-class optimization objective, reasoning about large language model (LLM) cost directly during query planning to achieve 2-8× speedups. Second, adaptive model cascades reduce inference costs by routing most rows through a fast proxy model while escalating uncertain cases to a powerful oracle model, achieving 2-6× speedups while maintaining 90-95% of oracle model quality. Third, semantic join query rewriting lowers the quadratic time complexity of join operations to linear through reformulation as multi-label classification tasks, achieving 15-70× speedups with often improved prediction quality. AISQL is deployed in production at Snowflake, where it powers diverse customer workloads across analytics, search, and content understanding.