tokmon – CLI to monitor your program's OpenAI token costs
CLI to monitor your program's OpenAI API token usage. - yagil/tokmon
tokmon – CLI to monitor your program's OpenAI token costs launched on April 18, 2023 with 11 votes, #1,684 of 4,853 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 3 years on.
In plain words
tokmon is a command-line tool that tracks OpenAI API token usage and associated costs for programs. Developers use it to monitor their application's spending on OpenAI services. The tool integrates directly into workflows to provide visibility into token consumption, helping teams understand and manage their API expenses. It's particularly useful for developers building applications that rely heavily on OpenAI's models.
written from the facts on this page · September 2026
Does a similar job
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Frugon – Find which LLM calls a cheaper model could handle (local, MIT)Jul 2026 · github.com · ▲67I 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…
TokenWatchMar 2026 · tokenwatch-ten.vercel.app · ▲3Hey HN! Built this after watching engineering teams repeatedly get surprised by AI API invoices — no visibility into spend until the bill landed. TokenWatch connects to your OpenAI, Anthropic, and Gemini keys and gives you: - Real-time spend dashboard across projects - Budget thresholds with proactive alerts - Anomaly detection for unusual cost spikes - Duplicate call detection (biggest source of wasted spend I've seen) Free tier supports up to 3 AI systems. Built this in ~2 days as a solo project. Happy to answer anything about the build or the problem space.
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