Adaptive Multi-LLM Inference Router

Developed a benchmark-driven multi-LLM routing system that optimizes inference cost while maintaining response quality. Instead of sending every query to the same model, the system first routes each request through a lightweight, low-cost classifier that identifies its task category (such as mathematical reasoning, code generation, summarization, or factual knowledge) and estimates its complexity. Based on these outputs, a routing engine consults a model capability database containing the strengths and relative costs of multiple Fireworks AI models. It then dynamically selects the lowest-cost model expected to solve the query accurately and forwards the request for final inference. This modular architecture separates query analysis from response generation, enabling efficient utilization of specialized models while reducing unnecessary inference costs. The capability database can be updated independently as new benchmark results or models become available, making the system scalable, flexible, and easy to maintain.