LLM Caching is a Lie! | Zuey

The Gist

Caching in LLM workflows is not a lie, but optimizing strictly for cache hit rates leads to inefficient task execution because model switching and workflow design matter more. Optimizing for the total combination of cost, speed, and quality is the correct goal for efficient AI development.

Quick Overview

Caching LLM prompts saves money when a message matches an existing cache, but changing model names or reasoning effort invalidates the cache and forces full price payments. Developers often cling to expensive models like Sol and preserve caches even when cheaper models like Luna complete tasks five times cheaper. Comparing options reveals that workflow efficiency depends on balancing task uncertainty, token costs, and model behavior rather than maximizing cache hit rates alone.

Key Points: Caching input prompts in LLM interactions can reduce input costs by up to ninety to ninety-five percent when a cache hit occurs. Changing the model name or the reasoning effort setting in the system prompt breaks the cache and forces full price payment for all tokens. Using a strong model like GPT 5.6 Sol for a simple task can take two to three hours due to overthinking and over-engineering. Switching to a cheaper model like Luna and breaking the cache can still be five times cheaper than running a slow, expensive model over multiple turns. Sol charges ten dollars per million input tokens, one dollar per million cached input tokens, and fifty dollars per million output tokens. Luna charges zero point twenty dollars per million input tokens, zero point zero two dollars per million cached input tokens, and one point twenty dollars per million output tokens. Task execution strategy dictates that low uncertainty problems require cheap models while high uncertainty problems require strong models. The ultimate goal of an efficient AI workflow is to optimize for cost to finish, time to finish, and output quality rather than optimizing for cache hit rate.

Context: Developers building AI applications often struggle to balance the speed, quality, and cost of large language models. Caching mechanisms introduced by providers promise massive savings, but blindly chasing high cache hit rates frequently traps engineers into using overly expensive models for simple execution tasks.