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…
In plain words
Frugon is a CLI tool that analyzes OpenAI-style logs to identify which language model API calls could run on cheaper alternatives. It reads usage data locally, compares costs across models using LiteLLM pricing and LMArena performance tiers, and estimates potential savings from switching tasks to lower-cost models. Built with an MIT license, it helps developers reduce AI spending by revealing which routine operations like searches or scans waste the most budget on expensive models.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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.
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