Understanding LLM Routers: Optimizing Large Language Model Usage

An LLM Router is a software that directs prompts to different language models, enabling cost savings, specialization, and availability. It differs from LLM Gateways which focus on traffic management and governance.

Details

An LLM Router is a piece of software that directs prompts to different large language models (LLMs) instead of always using the same model. This enables three key benefits: cost savings by using cheaper models for simpler tasks, specialization by routing to models optimized for specific use cases, and availability by load balancing or using fallback models. LLM Routers differ from LLM Gateways which focus more on managing traffic, observability, and governance. There are two main approaches to LLM routing - rule-based routers which are programmatic, and AI-powered routers that use a model to determine the best routing. The rule-based approach is simpler to implement but less flexible, while the AI-powered approach is more powerful but adds cost and latency. Hybrid approaches try to balance the tradeoffs. LLM Routing is particularly useful for autonomous agents like OpenClaw, which by default can only connect to a single model, making it hard to optimize for cost and quality. LLM Routers like Manifest allow connecting multiple models of varying complexity and cost to the agent, enabling better cost control and output quality.