The 104 SQL queries against 13 tables and 16.8M rows indicate that data retrieval dominates the pipeline's compute footprint; query optimization and indexing strategy will yield the highest ROI for cost reduction. LLM synthesis, while present, is secondary to the volume of structured data being processed and joined. This workload is suitable for scheduled runs on a predictable cadence but risks cost overruns if triggered on-demand without row-level filtering or table sampling controls. Prioritize pushing aggregation and filtering logic into SQL rather than post-processing results in downstream synthesis steps.