LM routers vs LLM proxies: which one do you actually need?

Merge's platform offers a sophisticated approach to managing language model (LLM) requests by combining both proxy and router functionalities. While proxies serve as intermediaries between applications and model providers, ensuring unified access, logging, and resilience, routers make decisions on which models should handle specific requests based on factors like cost, latency, and quality. The article emphasizes the importance of distinguishing between these two layers, as they address different challenges in AI infrastructure. In practice, most tools integrate both functions under what is known as an LLM gateway, allowing users to efficiently route mixed-difficulty traffic and manage high LLM expenditures. Merge's Gateway exemplifies this by providing a unified endpoint that not only facilitates seamless transport but also enhances decision-making for routing requests across multiple models. For organizations, the order of implementation is crucial: proxies should be established first to gain visibility, which then informs the development of effective routing rules. Ultimately, Merge is highlighted as more than just a Unified API product, but as an integration platform capable of managing customer integrations and optimizing AI operations.

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