LLM Routing and Model Cascades: How to Cut AI Costs Without Sacrificing Quality
Most production AI systems fail at cost management the same way: they ship with a single frontier model handling every request, watch their API bill grow linearly with traffic, and then scramble to add caching or reduce context windows as a band-aid. The actual fix — routing different queries to different models based on what each query actually needs — sounds obvious in retrospect but is rarely implemented well.
The numbers make the case plainly. Current frontier models like Claude Opus cost roughly $5 per million input tokens and $25 per million output tokens. Efficient models in the same family cost $1 and $5 respectively — a 5x ratio. Research using RouteLLM shows that with proper routing, you can maintain 95% of frontier model quality while routing 85% of queries to cheaper models, achieving cost reductions of 45–85% depending on your workload. That's not a marginal improvement; it changes the unit economics of deploying AI at scale.
