If you're building with LLMs in 2026, you already know the pain: API costs creep up silently. You ship a feature, usage spikes, and suddenly your OpenAI bill looks like a car payment.
The problem isn't that tokens are expensive — it's that most developers have zero visibility into what they're spending while they work.
The Invisible Cost Problem
Most of us check usage dashboards after the fact. By then the damage is done. You already shipped the prompt that sends 8K tokens when 2K would've worked. You already ran that chain-of-thought loop 50 times during testing.
What if you could see token counts and costs ticking up in real time, right in your menu bar?
Enter the Menu Bar
I've been using TokenBar for a few weeks now and it changed how I think about prompt engineering. It sits in your macOS menu bar and gives you a live counter of tokens flowing through your LLM calls.
Here's what actually changed for me:
- I started noticing waste. Seeing tokens tick up in real time made me instinctively trim prompts. No more "just in case" context stuffing.
- Testing got cheaper. When you can see the cost of each test run live, you stop running things carelessly.
- Budget conversations got easier. Instead of "I think we're spending around $X," I could say exactly what each feature costs.
The Bigger Point
Developer tools that surface hidden costs aren't a luxury — they're infrastructure. The same way we monitor CPU and memory, we should monitor token usage. It's a real resource now.
If you're spending more than $50/month on LLM APIs, you owe it to yourself to know exactly where those tokens go. TokenBar is $5 lifetime at tokenbar.site — probably the cheapest productivity upgrade you'll make this year.
What tools are you all using to track LLM costs? Curious what other approaches people have found.
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