Smart Model Routing Can Cut AI Costs by Up to 60% Without Sacrificing Quality

Developers building AI agents often rely on a single large language model for all queries, which is costly and inefficient. A team at a hackathon built SupportMind AI using a routing strategy that directs simple queries to a smaller, faster model and complex ones to a more capable model. They used an open-source library called cascadeflow to define keyword-based rules that assign each query to the appropriate model at runtime. In production environments handling thousands of daily queries, this approach can reduce model costs by 40–60% while preserving response quality where it matters most. The project is publicly available on GitHub, along with a live demo of the system in action.