The 15 SQL queries against 150 rows indicate minimal database overhead; compute concentration is almost certainly in LLM synthesis rather than data retrieval. This workload is suitable for scheduled runs on a daily or weekly cadence, though on-demand execution would also be acceptable given the modest row volume. To reduce future execution time, prioritize query result caching or materialization of intermediate feature sets, which would eliminate redundant SQL re-execution across pipeline stages and shift the cost-reduction focus entirely to prompt optimization.