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

Hi HN, we built an open source model gateway. It's a single place to manage our own self hosted, frontier, and open source models in one place.

It’s is rust native, built for concurrency, and implements all the config quirks across models and providers (streaming formats, tool calls, model parameters, rate limits, and different error behavior).

The gateway adds under 1 ms for BYOK requests and under 2 ms when Experiential supplies the provider key. It has every major inference provider, and 1000+ models refreshed daily via a codex agent that opens a PR.

Compared to other similar projects we’re open source, take no markup, allow you to mix local models with a marketplace, and use your traffic to (opt in) train you a model. Simple routing doesn’t warrant a 10% token markup.

The way we do this is given standardized OTel traces, we mine representative real tasks, use text world models to simulate rollouts for various models, apply an LLM judge, and fit a nearest neighbor classifier on top of an embedding of a prompt to decide the optimal model for each request. Usually this can map out a better pareto curve on cost/quality than just calling single models but it’s not perfect.

Using these simulations we can also do things like suggesting cache hit optimizations, new model suggestions, and training models.

It’s open source, so you can deploy it on your own infrastructure, use our hosted version with 0 markup, or read how we design for maximum availability on our website.




Comments URL: https://news.ycombinator.com/item?id=49471407


Points: 104


# Comments: 16

guid

https://news.ycombinator.com/item?id=49471407

source_url

https://github.com/experientiallabs/experiential

author_name

SilenN

id: 6180
uid: IgOoK
insdate: 2026-08-28 02:05:17
title: Show HN: We built open OpenRouter that turns usage into a better model
additional:

Hi HN, we built an open source model gateway. It's a single place to manage our own self hosted, frontier, and open source models in one place.

It’s is rust native, built for concurrency, and implements all the config quirks across models and providers (streaming formats, tool calls, model parameters, rate limits, and different error behavior).

The gateway adds under 1 ms for BYOK requests and under 2 ms when Experiential supplies the provider key. It has every major inference provider, and 1000+ models refreshed daily via a codex agent that opens a PR.

Compared to other similar projects we’re open source, take no markup, allow you to mix local models with a marketplace, and use your traffic to (opt in) train you a model. Simple routing doesn’t warrant a 10% token markup.

The way we do this is given standardized OTel traces, we mine representative real tasks, use text world models to simulate rollouts for various models, apply an LLM judge, and fit a nearest neighbor classifier on top of an embedding of a prompt to decide the optimal model for each request. Usually this can map out a better pareto curve on cost/quality than just calling single models but it’s not perfect.

Using these simulations we can also do things like suggesting cache hit optimizations, new model suggestions, and training models.

It’s open source, so you can deploy it on your own infrastructure, use our hosted version with 0 markup, or read how we design for maximum availability on our website.




Comments URL: https://news.ycombinator.com/item?id=49471407


Points: 104


# Comments: 16


category: Hacker News
md5:
guid: https://news.ycombinator.com/item?id=49471407
source_url: https://github.com/experientiallabs/experiential
updated:
image:
author_name: SilenN
author_link:
Add Comment
Type in a Nick Name here
 
AI Testing

Autonomous AI API, a cutting-edge platform that leverages advanced AI technologies to enable self-modification and self-repair of its core files. This innovative site utilizes machine learning algorithms to detect and correct errors, ensuring maximum uptime and performance. With its autonomous capabilities, the AI API can adapt to changing requirements, learn from user interactions, and continuously improve its functionality.
Page Views

This page has been viewed 2 times.

Search HNews
Search HNews by entering your search text above.
Category List HNews