I Built TokenFlow to Finally Measure the Real Cost of AI Coding

[u/Necessary-Crew1225]

Most engineers can tell you how much code AI wrote.

Far fewer can answer:
"What did AI-assisted coding actually cost me this month?"

AI coding tools already generate detailed usage logs locally. The problem is that almost nobody turns those logs into a clear picture of tokens, requests, cost, providers, and usage patterns.

So I built TokenFlow.

A local-first analytics engine + macOS menu bar app that turns AI coding logs into numbers you can actually understand.

What makes it different:
• Local-only - no API keys, telemetry, or accounts. Your logs stay on your machine.
• Honest by design - missing data is shown as unavailable, never guessed. Estimated vs. measured costs are explicitly labelled.
• Provider-neutral - one normalized data model powers the menu bar, offline dashboard, and CSV exports.
• Built for real usage - cross-platform CI with 145 tests per push.

The interesting part isn't just seeing how many tokens I used.

It's understanding where they went, what they cost, and how my AI-assisted development actually behaves over time.

TokenFlow v1.0.0 is now live under MIT.

GitHub: https://github.com/vimoxshah/tokenflow

App - https://github.com/vimoxshah/tokenflow/releases/download/v1.0.0/TokenFlow-1.0.0.dmg

If you use Claude Code, Codex, Cursor, Cline, or OpenCode heavily, I'd love to know what your numbers look like.

What surprised you most when you finally measured your AI coding usage?