Why your LLM bill is exploding — and how semantic caching can cut it by 73%

Our LLM API bill was growing 30% month-over-month. Traffic was increasing, but not that fast. When I analyzed our query logs, I found the real problem: Users ask the same questions in different ways.

"What's your return policy?," "How do I return something?", and "Can I get a refund?" were all hitting our LLM separately, generating nearly identical responses, each incurring full API costs.

Exact-match caching, the obvious first solution, captured only 18% of these redundant calls. The same semantic question, phrased differently, bypassed the cache entirely.

So, I implemented semantic caching based on what queries mean, not how they're worded. After implementing it, our cache hit rate increased to 67%, reducing LLM API costs by 73%. But getting there requires solving problems that naive implementations miss.

Why exact-match caching falls short

Traditional caching uses query text as the cache key. This works when queries are identical:

# Exact-match ...