If you're building with LLMs in 2026, you already know the pain: API costs that creep up silently until you get that end-of-month bill that makes you question your life choices.
I've been shipping AI-powered features for the past year, and the single biggest operational headache isn't prompt engineering or model selection — it's knowing what you're spending in real time.
The Problem
Most developers track token usage one of two ways:
- After the fact — checking dashboards on OpenAI/Anthropic/Google after the damage is done
- Manual logging — wrapping every API call with token counters that clutter your codebase
Both approaches suck. By the time you notice a runaway prompt eating through your budget, you've already burned through dollars you didn't plan to spend.
What Actually Helped Me
I started using TokenBar — it's a tiny macOS menu bar app that tracks your LLM token usage across providers in real time. Glance up, see your spend. That's it.
What I like about it:
- Lives in the menu bar, zero friction
- Works across OpenAI, Anthropic, and other providers
- $5 one-time purchase (the irony of a token tracker that doesn't have a recurring fee is not lost on me)
The Bigger Lesson
The real takeaway isn't about any specific tool. It's that visibility into your AI spend should be as automatic as monitoring your server uptime. We wouldn't ship a production app without error tracking. Why are we shipping AI features without cost tracking?
If you're spending more than $50/month on API calls, you need something — whether it's TokenBar, a custom dashboard, or even a spreadsheet. Just stop flying blind.
Quick Tips for Managing LLM Costs
- Cache aggressively — identical prompts should hit cache, not the API
- Use the cheapest model that works — GPT-4o-mini handles 80% of what people throw at GPT-4o
- Set hard budget alerts — every provider offers them, most devs never configure them
- Monitor in real time — don't wait for the monthly invoice
What's your approach to tracking AI costs? I'm curious what setups other devs are running. Drop your approach in the comments 👇
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