Every prompt runs a meter.
Most people never see it.
AI feels free because nothing ticks while you type. But every prompt, upload and re-run spends compute — through tokens, usage caps, credits or an API bill. This is a working tool for spending it well.
By Michael Berger. I built this in my own time, as the resource I kept wishing existed — practical, platform-neutral and free. The same principles apply across ChatGPT, Claude, Gemini, Copilot and Grok. Nothing to sign up for, nothing to buy. Take it, use it, share it. If something here is wrong or out of date, tell me.
The 10 commandments
Most people don't need to understand every technical detail of AI to use it well — they need a few simple rules. Everything further down this page exists to back these up: calculators that show the cost, tools to survey your team and draft a policy, and an honest account of what you can actually track.
Tokens are becoming part of the package
The reason this matters more each month: AI budget is shifting from a central IT line to something allocated per person, like a laptop or a phone. Once you have your own allowance, efficiency stops being the company's problem and becomes your productivity ceiling.
It's now a benefit line
A TechCrunch report in March 2026 put the share of tech companies including some AI credit allocation in benefits packages at over 40% — up from under 5% eighteen months earlier. At GTC in March 2026, NVIDIA's Jensen Huang went further, floating engineer token budgets worth roughly half base salary, framing them as a productivity amplifier rather than a perk.
And a cap
The same shift brings limits. Uber exhausted its entire 2026 AI budget by April after Claude Code adoption jumped from 32% to 84% across 5,000 engineers, with individuals generating $500–$2,000 a month — then capped everyone at $1,500 per month per tool. Reported caps elsewhere run from $250 a month at a defence manufacturer to around $2,000 at Workday and Stripe.
The spread is enormous
Ramp's AI Index, covering 70,000+ businesses, put median spend at about $11.38 per employee per month in June 2026 — while the top 1% spent $7,450. An April cut showed a median of $46 with the middle half falling between $3 and $352. Which end you sit at is driven far more by habits than by headcount.
Cheaper tokens, bigger bills
Token prices have fallen more than 90% since 2023, yet total corporate AI spend has roughly doubled since late 2025 — a textbook Jevons paradox. Cheaper units invited far more use, and agentic tools that call a model repeatedly multiplied it again. Waiting for prices to fall is not a cost strategy.
What actually drives the overruns
Ramp names model tier migration — teams moving from a lightweight model to a frontier one for quality reasons, often without anyone noticing — as the single biggest driver of unexpected cost increases. That is precisely what the ten rules on this page are designed to prevent.
What it means for you
If your allowance is capped, every wasted token is one you can't spend on work that matters. Running routine tasks on a premium model in a long thread doesn't just cost the company money — it burns through your budget and throttles you before month end. Discipline buys you more assistant, not less.
Figures as reported mid-2026 and in US dollars. This area moves quickly — treat them as orders of magnitude, not current benchmarks.
Common mistakes
Most wasted AI capacity comes down to a handful of recurring habits.
Reaching for the strongest model too early
Don't spend premium reasoning capacity organising messy notes — unless the organising itself needs deep judgement.
Pasting too much material
Long context isn't always better. It raises cost and dilutes focus, and the answer often gets worse, not better.
Uploading PDFs by habit
Useful where layout matters, wasteful where only the text does.
Re-running weak prompts
A bad prompt should be fixed, not repeated on an expensive model. Three heavy re-runs cost more than one good prompt.
Using light models for high-stakes calls
Cost control shouldn't come at the expense of quality where the risk is real. Match the model to the cost of being wrong.
Asking for long answers by default
Output tokens are the expensive ones. Ask for three bullets when three bullets will do.
Letting one chat run forever
Every message resends the whole conversation. Start a fresh chat when you start a new task.
Treating every tool as identical
Different tools and tiers have different strengths, costs and limits. Choose deliberately rather than by habit.
Word or PDF?
A Word or text document is like handing someone a typed script. A PDF is often like handing them a photograph of the page — the layout, the headings, the tables, the signatures and sometimes the surrounding clutter too. That extra information is sometimes essential and sometimes just expensive.
| Use Word, text or an extract when… | Use a PDF when… |
|---|---|
| Only the wording matters | The document only exists as a PDF |
| Formatting isn't important | Signatures or dates matter |
| You're drafting, editing or summarising | The layout carries meaning |
| You already know which section matters | Charts or tables need visual review |
| The PDF has many irrelevant pages | You're reviewing evidence exactly as received |
Before uploading a PDF, ask one question: do I need the AI to understand this as a page, or only to understand the words? If it's just the words, send Word, text or a selected extract.
Print it, circulate it, adapt it
Two files you can use without asking. No email required, no form in the way.
Both files are free to use and adapt inside your organisation. The policy template is a drafting aid, not legal advice — have it reviewed before it goes into a handbook.
Three more parts
The rules above are the argument. These are the working parts — tools to size the problem, material to govern it, and the market context behind both.
The words people use
You don't need any of this to use AI well, but it helps when reading a bill or an admin dashboard.
About
I work in portfolio, investments and M&A, which means I spend a lot of time looking at how businesses actually spend money — and increasingly at how they spend it on AI.
This started as a document for my own use. Almost everything written about AI in business was either vendor marketing or abstract strategy, and very little of it answered the practical question people were really asking: which model should I use for this, and what is it costing us? I wrote down what I'd worked out, then kept going until it turned into this.
It's built in my own time and given away deliberately. There's nothing to buy, no email list, and no consultancy behind it. If it's useful to your team, take it.
How it's kept honest
Figures are sourced and dated rather than asserted. Where something is an opinion rather than a fact, it says so.
The vendor and pricing sections age quickly, so each page carries a review date and I'd genuinely rather be told when something is out of date than leave it standing.
Corrections have already come from readers. That's the intention.
Reviews and feedback
If this has been useful, saying so publicly helps other people decide whether it's worth their time. If something is wrong or out of date, that's even more useful — the vendor tracking in particular ages quickly, and I'd rather hear it from you.