The 10 SQL queries against 37,620 rows suggest modest database overhead; compute time concentration likely shifts to LLM synthesis given the multiple API calls required for contextual enrichment across that row volume. This workload is acceptable for scheduled runs on a daily or weekly cadence but risks latency issues if triggered on-demand during peak API load periods. The primary lever for cost reduction is row filtering at the SQL stage—applying stricter WHERE clauses to exclude low-value records before LLM processing, since API costs scale linearly with input volume while query optimization yields diminishing returns at this scale.