How I Slashed Our LLM API Token Costs by 90% — From 1M to 100K Daily

Short summary

Engineer cut customer-service bot's LLM costs by 90% using prefix caching—storing conversation state to avoid reprocessing message history. Daily tokens dropped from 1M to 100K, latency fell 8x (3.2s→0.4s). Includes working Python implementation with diskcache.

• •Prefix caching reuses prior conversation state instead of reprocessing entire message history
• •90% token reduction (1M to 100K daily) achieved with deterministic hashing and disk caching
• •Practical Python code provided; trade-offs discussed between three caching strategies

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