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Show HN: We built open OpenRouter that turns usage into a better model

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Re: Show HN: We built open OpenRouter that turns usage into a better model

#24

what's the business model here. How does experiential labs make money

They make money on enterprise plans: https://www.experientiallabs.ai/pricing#enterprise

Look at the Intelligence features in the Enterprise plan:

* Per-prompt model optimization

* Caching

* A model you own, trained on your traffic

Re: Show HN: We built open OpenRouter that turns usage into a better model

#25

What online signal recalibrates simulated rankings against actual task success? Also do you have a plan to support semantic caching at the router level?

For the online signal, we use a LLM judge with a rubric calibrated offline by the user via TUI. UX of the calibration is a major focus area. Semantic caching is interesting, open to supporting it but not currently planned.

Re: Show HN: We built open OpenRouter that turns usage into a better model

#26

what's the business model here. How does experiential labs make money

They make money on enterprise plans: https://www.experientiallabs.ai/pricing#enterprise Look at the Intelligence features in the Enterprise plan: * Per-prompt model optimization * Caching * A model you own, trained on your traffic

yep, it will be through enterprise licenses and our own hosted platform built on the repo

Re: Show HN: We built open OpenRouter that turns usage into a better model

#29
post #2

Could you say more about how caching works? One major advantage of sticking with a single model is saving money on cached input tokens. I'd imagine if you swap between a bunch of models, you may improve performance but cost would would balloon out of control

Generally you should only have two models in the pool per domain. I wrote some of my learnings building a router here: https://try.works/first-principles-of-model-routing

Re: Show HN: We built open OpenRouter that turns usage into a better model

#30
post #28

You started it a week ago? I look forward to checking back in 3 weeks when you've exited for $1B

Looks like first PR is June 24th: https://github.com/experientiallabs/experiential/pull/1 So, two months. Still impressive!

impressive only if using pre-gpt era assumptions about saas/products/software.

unfortunately a small team can reproduce it in two months, which greatly lowers value of it.

we, as a collective, have to change our value-judging logic and tune it to post AI world.

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