Caching is all you need

In an exploration of optimizing AI cost management, Hex engineers Zach Kirby and Olivia Koshy discovered that a high cache hit rate, previously thought to be a reliable metric, was misleadingly concealing significant user expenses. Despite a 94% cache hit rate, high costs arose from sporadic uncached interactions that skewed user credit usage, leading the team to focus on improving caching strategies. They developed a cache visualization tool to analyze input token usage and costs, revealing that uncached responses could be disproportionately expensive. By restructuring cache prompts and implementing an automated review system, they aimed to mitigate cache misses. Additionally, adapting subagent models to reduce cache cooling and refining cache route keys to lessen mid-run cache misses were essential strategies. Through these initiatives, they learned that understanding and addressing outlier costs, rather than relying on averages, was crucial in managing AI expenses effectively.

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