RAG Chunk Size Is a Cost Decision, Not Just a Config Setting

A developer building a Retrieval-Augmented Generation (RAG) pipeline discovered that chunk size choices have significant, hidden resource implications at every stage. Using small chunks initially caused ingestion to balloon unexpectedly, consuming excessive CPU time and embedding API calls at scale. Switching to larger chunks resolved ingestion speed but introduced higher per-query token usage and increased LLM response latency on every subsequent request. The author concluded there is no universally optimal chunk size — only trade-offs between ingestion-time costs and query-time costs that must be actively monitored. A single chunking decision, once deployed, silently affects CPU, storage, vector database size, API spend, and user experience for the entire lifetime of the system.