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title: APIs.io On-Premise, And Why I Am Looking For Design Partners
description: APIs.io, a public API catalog launched in 2014, is expanding into an on-premise appliance for companies that want a private inventory of their own APIs without...
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# APIs.io On-Premise, And Why I Am Looking For Design Partners

**[API Evangelist](https://daily.dev/sources/apievangelist)** · 5 min read · 0 upvotes · 0 comments

## Summary

APIs.io, a public API catalog launched in 2014, is expanding into an on-premise appliance for companies that want a private inventory of their own APIs without sending source code to a hosted AI provider. The system now runs the profiling and scoring pipeline on gpt-oss-120b, OpenAI's open-weight model, quantized to a 59GB GGUF file served locally via llama-server, eliminating per-token costs and keeping data on-premises. Testing against Claude on six well-documented providers showed the local model landed within 1-10 points of Claude's output, in about half the time and for $0 versus $55.25. The appliance spiders Git repos, Confluence, and portals for API definitions, aggregates them into a Git-based system of record, scores them with a reconfigurable Kin Score, and publishes a static site. The author is seeking small and medium-sized company design partners to shape the product before a general release.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://apievangelist.com/2026/09/29/apis-io-on-premise-looking-for-design-partners>

## Questions this post answers

### How does the gpt-oss-120b open-weight model compare to Claude for automated API documentation profiling?

Running the same profiling pipeline on six well-documented API providers, gpt-oss-120b landed within about one to ten points of Claude's Kin Score results, tying on one provider. It completed the work in roughly half the wall-clock time and cost $0, compared to $55.25 for the equivalent Claude runs, since it runs locally with no per-token billing.

_Anyone weighing local versus hosted models for internal tooling can compare real cost and accuracy tradeoffs on daily.dev._

### What model and quantization does APIs.io On-Premise use to run locally without sending data to a hosted AI provider?

It uses gpt-oss-120b, OpenAI's open-weight model, in MXFP4 quantization as a 59 GB GGUF file, served locally through llama-server. This lets the API profiling and scoring pipeline run entirely on hardware the operator owns, starting on a Mac mini, with no tokens leaving the machine and no per-token billing.

_Teams evaluating self-hosted LLM inference for sensitive internal data can track setups like this on daily.dev._

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---

Tags: [#openapi](https://daily.dev/tags/openapi)

[View this post on daily.dev](https://daily.dev/posts/apis-io-on-premise-and-why-i-am-looking-for-design-partners-a55sh5idj)

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