Smart model routing sends each LLM request to a smaller model when the task is simple and to a more capable model when it is difficult, instead of sending everything to the most expensive model. The article claims this can cut LLM API costs by around 10X when most requests are handled by the cheaper model. As a result, systems can lower token spend and keep response quality similar, but they must reliably judge request difficulty using task type, risk, context needs, and output constraints.
The article explains how fingerprinting LLM inputs to build exact-match and semantic caches can skip repeat model calls that would otherwise be billed again. It gives an example where 1,000,000 calls per month at $0.006 per call drop from about $6,000 to about $2,550 after a 60% hybrid cache hit rate, with embedding and vector-store costs around $150. It changes costs and latency by returning cached responses when the request inputs, context, model settings, permissions, and freshness checks still match, while tuning thresholds and TTLs and avoiding caching for personal, creative, or real-time data.
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