For the company I work for, one of the most important aspects is ensuring we can fallback to different models in case of content filtering since they are not equally sensitive/restrict.
From what I understand, it's from people using it in their workflows - say, Claude but keep hitting the rate limits, so they have to wait until Claude says "you got 10 messages left until 9pm", so when they hit that, or before they switch to (maybe) ChatGPT manually.
With the router thingy, it keeps a record, so you know every query where you stand, and can switch to another model automatically instead of interrupting workflow?
I may be explaining this very badly, but I think that's one use-case for how these LLM Routers help.
We get rate limited when using Azure's OpenAI API. As a gov contractor working with AI, I have limited means for getting access to frontier LLMs. So routing tools that can fail over to another model can be useful.
You may have a variety of model types/sizes, fine tunes, etc, that serve different purposes - optimizing for cost/speed/specificity of task. At least that's the general theory with routing. This one only seems to optimize for cost/quality.
I think a lot of people are just interested in hitting the LLM without any bells or whistles, from Typescript. A low level connector lib would come in handy, yeah? https://github.com/monarchwadia/ragged
I don't find success just using a prompt against some other model without having some way to evaluate it and usually updating it for that model.