The 104 SQL queries processing 2,061,290 rows across 51 tables indicate substantial data aggregation work; SQL profiling likely dominates compute time given the row volume and table cardinality, while LLM synthesis operates on already-materialized result sets. This workload is suitable for scheduled runs on a predictable cadence but risks timeout or cost overruns if triggered on-demand without query optimization. The primary lever is query consolidation—reducing the 104 queries through materialized views, pre-aggregation, or table denormalization would yield the fastest cost reduction, since data retrieval at this scale typically outweighs downstream processing.