If you're using LLMs daily — Claude, GPT-4, Gemini, whatever — you're probably hemorrhaging money and don't even realize it.
I didn't. For months I was copy-pasting massive files into chat windows, writing sprawling prompts, and wondering why my API bill kept climbing. Then I started actually counting my tokens.
The Wake-Up Call
I ran a quick audit on one week of my Claude API usage. Turns out I was sending 40% more context than I needed to on almost every request. Redundant system prompts, full file contents when snippets would do, conversation histories that should have been truncated ages ago.
The fix wasn't some complex optimization. It was just... awareness.
Making Tokens Visible
The problem is that tokens are invisible by default. You type a prompt, you get a response, and somewhere a meter is running that you never see. It's like coding without a profiler — you can't optimize what you can't measure.
I wanted something dead simple: a real-time token count sitting in my menu bar while I work. Not buried in a dashboard I'd check once a month. Right there, always visible, like a speedometer.
That's exactly what TokenBar does. It's a lightweight macOS menu bar app that shows your token usage across providers in real time. $5 lifetime, no subscription. I've been running it for weeks now and it's genuinely changed how I write prompts.
What Actually Changed
Once tokens became visible, three things happened:
- I got better at prompting. Shorter, more precise instructions. Less "here's my entire codebase, figure it out."
- I started chunking work differently. Instead of one massive conversation, I break tasks into focused sessions.
- My API costs dropped ~35%. Not from using AI less — from using it smarter.
The Takeaway
If you're building with LLMs, measure your token usage. Make it visible. You'll write better prompts, spend less money, and probably build better products too.
What tools are you using to monitor your AI usage? Curious what others have found useful.
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