The original headline is: "How We Reduced Our LLM API Costs by 60%: What Actually Worked"

Short summary

Production LLM costs stem primarily from redundant input tokens rather than model choice. Instrument all API calls to identify cost drivers, then implement semantic caching to match similar queries against cached responses. This approach typically achieves 30%+ cache hit rates and cuts API spend by 50–60%, with working Django code provided.

• •Most LLM API spend comes from redundant input tokens (system prompts, context, examples), not output tokens
• •Instrument token logging middleware to measure cost breakdown across request types before optimizing
• •Semantic caching (embedding-based similarity matching) commonly achieves 30%+ cache hit rates and 50–60% cost reduction

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