The 18 SQL queries against 4,500 rows represent a moderate analytical load, but the bottleneck is almost certainly the LLM synthesis phase rather than query execution itself. SQL profiling on a single table at this scale typically completes in milliseconds; the sequential or batched LLM API calls required to generate contextual commentary across multiple report subsections will dominate total runtime. This workload is acceptable for scheduled runs on a daily or weekly cadence, but on-demand execution should be reserved for urgent requests given latency sensitivity. To reduce compute time on future runs, prioritize caching LLM outputs for stable metrics and implement query result sampling or aggregation to reduce the number of synthesis prompts required per report section.