# Together AI — AI for Systems: Using LLMs to Optimize Database Query Execution

- Company: Together AI (together.ai)
- Announced: 2026-04-03T00:00:00+00:00
- Category: research-paper
- Subject: Inference platform
- Source: https://www.together.ai/blog/using-llms-to-optimize-database-query-execution
- Record: https://forck.live/items/2362-ai-for-systems-using-llms-to-optimize-database-query-execution

Together AI, Stanford University, the University of Wisconsin–Madison, and Bauplan collaborated to test whether LLMs can optimize database query execution plans. They introduce DBPlanBench, a harness that exposes the internal physical operator graph of Apache DataFusion to an LLM, and show that LLM-guided plan rewrites can improve execution performance without modifying the database engine itself.

## Evidence

Verbatim from https://www.together.ai/blog/using-llms-to-optimize-database-query-execution:

> We worked in collaboration with Stanford University, the University of Wisconsin–Madison, and Bauplan to test whether LLMs can optimize database query execution plans. The results show that LLM-guided plan rewrites can improve execution performance without modifying the database engine itself.

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Record: https://forck.live/items/2362-ai-for-systems-using-llms-to-optimize-database-query-execution
Catalogue: https://forck.live/llms.txt
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