The 33 SQL queries across 446 rows and 5 tables indicate moderate query complexity, but the real compute burden lies in LLM synthesis rather than data retrieval—SQL profiling on this dataset scale typically consumes <10% of total pipeline runtime. This workload is acceptable for scheduled runs on a 6–12 hour cadence but risks timeout on tight on-demand SLAs; consider implementing query result caching to avoid re-execution of identical joins across multiple LLM prompts. The primary lever is reducing LLM call frequency by pre-aggregating funding metrics and startup attributes into materialized summary tables, which would collapse the 33 queries into 4–5 and eliminate redundant data fetches that currently drive synthesis overhead.