ashwathnakate/adaptive_llm_inference_router

The 'adaptive_llm_inference_router' is a personal experiment or reference implementation with negligible market traction (1 star, 0 forks). While the problem it solves—reducing inference costs by routing simple queries to smaller models—is highly relevant, the project lacks a unique technical moat or community backing. The 'router' pattern is currently being commoditized by both frontier labs (e.g., OpenAI's automatic switching between 4o and 4o-mini) and established infrastructure players like Martian, NotDiamond, and RouteLLM (LMSYS). Specifically, projects like 'RouteLLM' provide much deeper research-backed routing logic (using Elo ratings and training data) compared to this project's rule-based or simple classifier approach. Given its age (nearly 3 months) and lack of velocity, it is unlikely to evolve into a competitive tool. Major cloud providers (AWS Bedrock, Vertex AI) are also integrating model cascading directly into their orchestration layers, leaving very little room for unmaintained standalone routing scripts.