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Rodiun
frugon
Python2026–2026
Rodiun / frugon

frugon

Free, local, open-source LLM cost analyzer - see where your LLM bill leaks, on your machine.

2Distinct people
2Mentions
0Explicit recommenders
219GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Free, local, open-source LLM cost analyzer - see where your LLM bill leaks, on your machine.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by day2 dated mentions

Observed mentions: 2026-07-07 12:20:54 UTC to 2026-07-17 23:04:18 UTC.

012026-07-07 00:00:00 UTC to 2026-07-08 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJul 72026-07-08 00:00:00 UTC to 2026-07-09 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-09 00:00:00 UTC to 2026-07-10 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-10 00:00:00 UTC to 2026-07-11 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-11 00:00:00 UTC to 2026-07-12 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-12 00:00:00 UTC to 2026-07-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJul 122026-07-13 00:00:00 UTC to 2026-07-14 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-14 00:00:00 UTC to 2026-07-15 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-15 00:00:00 UTC to 2026-07-16 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-16 00:00:00 UTC to 2026-07-17 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-17 00:00:00 UTC to 2026-07-18 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJul 17

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jul 72026-07-07 00:00:00 UTC2026-07-08 00:00:00 UTC110
Jul 82026-07-08 00:00:00 UTC2026-07-09 00:00:00 UTC000
Jul 92026-07-09 00:00:00 UTC2026-07-10 00:00:00 UTC000
Jul 102026-07-10 00:00:00 UTC2026-07-11 00:00:00 UTC000
Jul 112026-07-11 00:00:00 UTC2026-07-12 00:00:00 UTC000
Jul 122026-07-12 00:00:00 UTC2026-07-13 00:00:00 UTC000
Jul 132026-07-13 00:00:00 UTC2026-07-14 00:00:00 UTC000
Jul 142026-07-14 00:00:00 UTC2026-07-15 00:00:00 UTC000
Jul 152026-07-15 00:00:00 UTC2026-07-16 00:00:00 UTC000
Jul 162026-07-16 00:00:00 UTC2026-07-17 00:00:00 UTC000
Jul 172026-07-17 00:00:00 UTC2026-07-18 00:00:00 UTC101

What Hacker News says

2 sources
jarodrh·Jul 7, 2026Submission

Show HN: Frugon – Find which LLM calls a cheaper model could handle (local, MIT)

I started leaning in on AI heavily this year, as I wanted to get more done autonomously, but then my token usage climbed dramatically to the point where my weekly quota would run out before the end of the week, sometimes a couple of days into the week. I realised I had to do something about it else I'd have to double my spend. So I decided to start tracking my cost per task type. This revealed that a lot of my spend went to searches/scans or simple things like scouting tasks. I then decided to turn this into a simple CLI tool that can be used to read your OpenAI-style logs locally, and analyze the cost and compare this spend to other models, then show you how much you could potentially save by switching those calls to a cheaper model. When you run analyze you get an offline estimate priced against LiteLLM and gated by LMArena tiers. The g
Read the rest
eneral savings bands come from the research published by RouteLLM; but you can confirm this yourself using 2 commands --measure (shows the prompt-response output side by side) and --judge (a model chosen to do the comparisons). These send a sample of the prompts from the logs to the candidate models - either the default choice or set by you. This call goes directly to the model provider (never through me) as any normal LLM call would, and the response is shown and judged to either be better or worse or a tie. It's deliberately small, because I tend to over complicate/think things sometimes: analyze + capture + a few commands, doing three jobs. Cost, quality visibility, routing recommendation. Nothing is hosted. capture is an optional local proxy on your own machine, and there's no endpoint in the path of your data. You can confirm this by checking the source. I included a demo so you can check out the output. It has a synthetic 56k call log (a month's worth) showing how costs can drop from $549.46 to $343.91 a month. A 37.4% saving. Try it: uvx frugon analyze --demo or uv tool install frugon Then point it at your own logs. All feedback is welcome, especially any on the routing/quality logic, or anything else, good or bad.
67 points · Open discussion

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