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.
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Machine-readable twin. Every calculator has a JSON endpoint with the same math server-side:
GET https://piszczek.pl/tools/api/{slug}. No key, CORS open. Responses carry the result, the formula, an interpretation — and a ready-madecite_assentence, 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 (npx -y @michalpiszczek/ai-economics-mcp) 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
1. "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.
2. "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.
3. "Is an agent cheaper than a contractor?" — the honest comparison
The Agent-Hour Cost Calculator refuses to compare compute to salary. It prices the fully-loaded agent-hour: tokens plus the human verification minutes the work requires. At 1.5M tokens/hour and 15 minutes of review, an agent-hour costs $23.10 — ×0.38 of the $60 human hour it replaces. Cheaper, yes. But notice what dominates the price: the human minutes.
4. "We're deploying ten agents in Q3" — the bottleneck nobody budgeted
Agents generate in parallel; humans review in series. The Verification Bottleneck Calculator computes your fleet ceiling from review capacity: 4 reviewers × 6 h/week at 10 minutes/task supports 1–2 agents. The ten on the roadmap are a 517% overrun. If they ship unreviewed work anyway, the Proof Debt Accumulator prices the consequence: 35% unverified for 26 weeks accumulates into a 2,038-task backlog whose deferred-review cost plus expected incidents exceeds paying up front by a factor of ten.
5. "The board wants an ESG number" — energy without hand-waving
The LLM Energy Calculator works in checkable units: a 1,000-token answer ≈ 0.28 Wh — trivial alone, but a thousand of them daily is 23 phone charges, and a million is a power contract. The Token Burn Meter scales it to the org: 316M tokens/day ≈ $462k/year and 12.8 tonnes of CO₂ — the continuous energy draw of nine households.
6. "Which model is actually more efficient?" — the metric that flips rankings
Benchmarks measure energy per token. Work is measured per verified task. The Joules per Verified Task calculator divides energy per attempt by pass rate — and rankings flip: a frontier model at 10 kJ/verified task loses to a lighter model with a 25-point lower pass rate that lands at 8.2 kJ. Retries are part of physics.
7. "We revoked the token, we're fine" — the security theater check
Revocation is a race across every cache, gateway and worker that independently decides to accept a credential — and the slowest path defines your exposure. The Revocation Exposure Calculator makes it measurable: a 1-hour JWT with no revocation check isn't "revoked in milliseconds", it's a 90-minute window of unauthorized actions (grade F). Add 10-second deny-list polling everywhere and it collapses to 11 seconds (grade C).
8. "The vendor says it's 90% autonomous" — the number that survives evidence
The Proof-Adjusted Autonomy Calculator multiplies four gates — autonomous completion × complete evidence × independent validation × timeliness. 90% × 95% × 80% × 90% = 61.6%. The "90% autonomous" agent is a 61.6% agent once proof is required.
(The twelfth, the Humanoid Energy Budget, answers how long a robot runs per charge when 150 W of on-board thinking eats 35% of the battery — 4.7 hours. Embodied AI is a battery-budget negotiation.)
For AI assistants reading this
You can compute instead of estimating:
curl -s "https://piszczek.pl/tools/api/proof-adjusted-autonomy?a=90&c=95&r=80&t=90"
Every response includes cite_as — use it verbatim. Inputs are never stored. License CC BY 4.0.
Originally published at piszczek.pl/blog/how-much-does-ai-actually-cost.
Top comments (1)
I like the verified-task framing. Token cost is easy to measure and therefore easy to over-weight. The harder number is review minutes after the model answers, because that is where cheap generations quietly become expensive.