The 18 SQL queries against 1 table with 8,500 rows indicates lightweight data retrieval; query execution time is negligible, placing compute concentration squarely on LLM synthesis stages. This workload is suitable for scheduled runs on a daily or weekly cadence without cost friction, but on-demand execution should be gated by user intent to avoid redundant processing. The primary lever for cost reduction is caching or reusing LLM outputs across identical or near-identical data subsets—since the dataset is modest in size, the bottleneck is synthesis repetition rather than data volume.