The 30 SQL queries across 5 tables and 8,948 rows indicate moderate query complexity, but LLM synthesis is the dominant compute cost in this pipeline—text generation and semantic enrichment typically consume 10–50× the resources of relational operations at this data scale. This workload is suitable for scheduled runs on a 4–6 hour cadence but risks timeout or cost overruns if triggered on-demand without caching or result reuse. The primary lever is reducing LLM call volume by pre-filtering the pipeline to high-value accounts or products before synthesis, or by caching enrichment results for stable reference data (product names, account segments) across runs.