If you use GitHub Copilot, Cursor, or any AI coding assistant, you probably have no idea what your team's AI spend actually looks like until the invoice arrives.
I built a small tool to fix that: AI Coding Cost Tracker. Here's how it works and why it matters.
The problem
AI coding tools are great right up until they aren't:
- Tokens scale faster than expected across multiple developers.
- Different projects burn very different amounts of AI budget.
- By the time you see your bill, the sprawl is already done.
Without visibility, AI coding tools turn into a black box of cost.
What the AI Coding Cost Tracker gives you
- Real-time token usage dashboards — see usage as it happens, not at the end of the month.
- Per-developer cost breakdowns — know who is burning the most tokens and why.
- Per-project reporting — allocate AI budget by project, sprint, or feature team.
- Exportable reports — paste them directly into budget reviews and engineering post-mortems.
- Broad compatibility — built for Copilot, Cursor, and other OpenAI-style coding tools.
Why this matters right now
We're in the phase where AI coding is standard, but cost governance isn't. Most teams still treat AI spend as a generic cloud cost. It isn't. It's a per-feature, per-engineer, per-sprint variable.
If you want AI tools to keep scaling, you need to make their cost visible in the same system you already use for engineering metrics.
Pricing
£5 one-time. No subscription. No SaaS renewal reminder every month. You buy it once and get the tracker.
The takeaway
You don't need to ban AI coding tools. You need to measure them.
The same way you track deploy frequency, you can track token frequency. The tool I made lets you do that without writing custom dashboards or wiring up a new BI pipeline.
Get the tracker here: theaisuite.pages.dev/copilot-token-billing/
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