Nicheloom

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We built open OpenRouter that turns usage into a better model

Details

External ID
49471407
Source
HN
Company
—
Product
We built open OpenRouter that turns usage into a better model
Website domain
github.com
Launched
Aug. 27, 2026
Cohort
—
Upvotes
222
Upvotes percentile
0.9744623655913979
Tags
—
Fetched at
Sept. 10, 2026, 5:31 a.m.
Updated at
Sept. 10, 2026, 5:31 a.m.

Description

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.

Enrichment

Theme
lightweight and on-device AI runtimes
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI feature
Project type
Commercial product
Normalized one-liner
api router that improves models based on usage
Manually corrected
False

Could you build this?

Partial Building an API proxy/router in Rust for model orchestration is straightforward, but setting up the automated pipeline that uses production telemetry to reliably train and continuously improve a better model requires ML engineering.

What it would actually take: The proxy itself is an asynchronous Rust service using Axum/Tokio to route requests, normalize payloads across LLM providers, and collect prompts/completions into a structured warehouse (e.g., ClickHouse). The hard part is the self-improving flywheel: automated data cleaning, synthetic evaluation/filtering, and continuous LoRA/full-weight fine-tuning pipelines using PyTorch and orchestration frameworks (Ray/Kubernetes). Requires ML engineers experienced in RLHF, instruction tuning, and production gateway infrastructure.

Discussion

20 comments analyzed.

Competitors mentioned: OpenRouter, LiteLLM, Bifrost, ngrok AI gateway, vLLM Semantic Router

Concerns raised: Telemetry enabled by default (misleading documentation), UI only available in hosted version, not in GitHub repo, Claude's verbose token output, Unclear differentiation from existing model routers, Potential rug-pull/suspicious business model

Feature requests: Model switching within existing sessions, Better handling of conversation branching with dynamic context, Integration with cloud provider encryption for training data

Competitors

Other products that read as similar to this one — 154 launches clear the similarity bar, closest 8 shown.

Attention rank: #13 of 155 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 299 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a model & infra tool for Fintech yet.