This RouteLLM framework sounds really promising, especially for cost optimization. It reminds me of the KNN-router project ([https://github.com/pulzeai-oss/knn-router](https://github.co...), which uses a k-nearest neighbors approach to route queries to the most appropriate models.
What I like about these kinds of solutions is that they address the practical challenges of using multiple LLMs. Rate limits, cost per token, and even just choosing the right model for the job can be a real headache.
KNN-router, for example, lets you define your own logic for routing queries, so you can factor in things like model accuracy, response time, and cost. You can even set up fallback models for when your primary model is unavailable.
It's cool to see these kinds of tools emerging because it shows that people are starting to think seriously about how to build robust, cost-effective LLM pipelines. This is going to be crucial as more and more companies start incorporating LLMs into their products and services.
Cost is a plus but at least what I see is that getting good response time is even bigger. Something like OpenAI Azure instances are inconsistent and it is far too normal to get a 40sec lag with responses with gpt4-o.
What I like about these kinds of solutions is that they address the practical challenges of using multiple LLMs. Rate limits, cost per token, and even just choosing the right model for the job can be a real headache.
KNN-router, for example, lets you define your own logic for routing queries, so you can factor in things like model accuracy, response time, and cost. You can even set up fallback models for when your primary model is unavailable.
It's cool to see these kinds of tools emerging because it shows that people are starting to think seriously about how to build robust, cost-effective LLM pipelines. This is going to be crucial as more and more companies start incorporating LLMs into their products and services.