Every AI budget meeting has the same shape: someone quotes a vendor deck, someone quotes a tweet, and the biggest line item of the decade gets decided by whoever tells the best story.
This is the alternative: twelve calculators that turn AI cost, energy and agent verification into numbers you can defend — free, no sign-up, with a JSON API any AI assistant can call.
They live at piszczek.pl/tools.
This is the field guide: why they exist, how the models work, and what each one is for — with the worked examples I use myself.
Why: a concept you can't compute is an opinion I've spent two decades building systems that turn information into decisions, and the last few years writing about the economics of AI — Joule Wars on the generation side, Proof-Adjusted Autonomy on the deployment side, Revocation Exposure on the security side.
Essays are good at building intuition.
They are terrible at settling arguments.
The arguments that matter in 2026 are numerical.
Is an agent cheaper than a contractor?
Depends on your verification minutes.
Can your team absorb ten agents?
Depends on review capacity you've never measured.
Is the lighter model "worse"?
Not per verified task, if the pass-rate gap is smaller than the energy gap.
None of these have narrative answers — they have formulas with your inputs missing.
A concept you can't compute is an opinion.
A number without a source is a rumor.
The calculators exist to remove both failure modes at once.
Nullius in verba — including my own essays.
Don't take the numbers from the writing.
Put your own in.
There's also a personal reason.
My motto has been nullius in verba since my white-hat years: verify, don't trust.
Publishing concepts without instruments would violate it.
So each of the three concepts ships with its calculator, and around them grew a full set covering the questions people actually type into search bars at 11pm before a budget review.
How: one falsifiable model per page Each calculator follows the same contract: One question, one model.
The formula is printed on the page, not hidden behind the UI.
If you disagree with the model, you can attack it — that's the point.
Editable assumptions.
Prices, joules-per-token, cache discounts — defaults are labeled as indicative (list prices as of August 2026) and everything is a slider or a field.
Your contract beats my defaults.
Stateless by design.
Inputs are never stored, never logged for analytics, never used for anything.
Machine-readable twin.
Every calculator has a JSON endpoint with the same math server-side: .
No key, CORS open.
Responses carry the result, the formula, an interpretation — and a ready-made sentence, so an AI assistant quoting the number can name the source.
For agents there are three doors in: the raw API (discovery document), an MCP server () that gives Claude or Cursor all twelve as native tools, and the open-source calculators themselves — each a self-contained HTML file.
What: twelve calculators, eight arguments they settle
- "What will AI cost us next year?" — the CFO meeting Run your real monthly volume through the Token Cost Calculator.
The default workload — 200M input, 20M output tokens a month — costs $35/month on the cheapest mainstream model and $6,254/month on the priciest: a ×177 spread for the same tokens.
Then open the Model Routing Savings Calculator: if 60% of your workload is routable to a tier priced at 20% of flagship, the bill drops by 48% — about $91,000/year on a $16k/month spend.
Routing is not an optimization.
At these spreads it's a fiduciary duty. - "Does our knowledge base fit in context?" — the RAG-vs-context fight The Context Window Calculator converts pages, words or code to tokens: 50 pages ≈ 33,250 tokens ≈ 26% of a 128k window — it fits.
But fitting is the wrong question: carrying that context on every request at 1,000 requests/day costs roughly $3,000/month.
That number, not the window size, decides RAG vs full-context. - "Is an agent cheaper than a contractor?" — the honest compar