The 40 SQL queries against 718,470 rows across 5 tables indicate that compute time is likely dominated by data retrieval and aggregation rather than LLM synthesis, particularly if queries involve joins or window functions on the serve-return dataset. This workload is acceptable for scheduled runs on a daily or weekly cadence but risks timeout or cost overruns if triggered on-demand at high frequency. The primary lever to reduce execution time is query optimization—specifically, pre-aggregating serve and return metrics into materialized summary tables and pushing filtering logic earlier in the pipeline to reduce row volume before LLM processing stages.