Cost-Aware Text-to-SQL: Cloud Compute Cost Analysis for LLM-Generated Queries
Published:Dec 26, 2025 19:51
•1 min read
•ArXiv
Analysis
This paper addresses a critical gap in evaluating Text-to-SQL systems by focusing on cloud compute costs, a more relevant metric than execution time for real-world deployments. It highlights the cost inefficiencies of LLM-generated SQL queries and provides actionable insights for optimization, particularly for enterprise environments. The study's focus on cost variance and identification of inefficiency patterns is valuable.
Key Takeaways
- •Execution time is a poor indicator of query cost.
- •LLM-generated queries can exhibit significant cost variance.
- •Inefficiency patterns like missing partition filters and full-table scans are prevalent.
- •Reasoning models can be more cost-effective than standard models.
Reference
“Reasoning models process 44.5% fewer bytes than standard models while maintaining equivalent correctness.”