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Frugon

Find which LLM calls a cheaper model could handle (local, MIT)

Details

External ID
48816724
Source
HN
Company
—
Product
Frugon
Website domain
github.com
Launched
July 7, 2026
Cohort
—
Upvotes
67
Upvotes percentile
0.8763440860215054
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

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 general 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.

Enrichment

Theme
Vertical
Horizontal
Function
Agent / copilot
Audience
Developer
AI stance
AI feature
Project type
Hobby / open-source project
Normalized one-liner
find cheaper llm models for ai calls
Manually corrected
False

Could you build this?

Yes Analyzing token usage and benchmarking prompt responses against smaller LLMs to suggest model downgrade opportunities is a standard script or local proxy dashboard.

Discussion

20 comments analyzed.

Competitors mentioned: GoModel - AI Gateway, RouteLLM, OpenRouter's auto mode, Wayfinder Router

Concerns raised: Human review steps not visible in logs, hard to quantify retry costs, Accuracy sensitive to nonce and temperature without hyperparameter variance, Only supports OpenAI-style jsonl format currently, Self-bias risk when judge model auto-selected from highest-tier

Feature requests: Response time as dimension for judging local model performance, Support for OpenInference/OpenTelemetry schema for agent sessions, Improved UX for local and unpriced models, Effective cost per judged success metric combining dollar cost with judge outcomes

Competitors

Other products that read as similar to this one — 188 launches clear the similarity bar, closest 8 shown.

Attention rank: #30 of 189 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 248 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a agent / copilot tool for Agriculture yet.