The hidden cost of redundant LLM calls

Most RAG pipelines make 2-3x more LLM calls than necessary. We analyzed the architectures built by the 40+ engineering teams in our private beta and found that 40% of token spend goes to calls that could be cached, batched, or eliminated entirely.

The scale of the problem

The numbers were staggering: the median pipeline modeled in PRISM makes 2.3 LLM calls per user request where 1 would suffice.

Redundant summarization. Many pipelines summarize retrieved documents before passing them to the final LLM call. If your retrieval stage already returns relevant chunks, this step often adds cost without improving output quality.

Missing semantic caching. 34% of the pipelines we analyzed had zero caching nodes. Semantic caching — where similar queries hit the cache — could eliminate 20-30% of backend LLM calls instantly.

By simulating these changes on the PRISM canvas, teams were able to see the immediate mathematical drop in projected P50 monthly costs without having to refactor their actual codebases.