Estimate Token Usage Before You Run It
Know how many tokens a job will consume before you send it — input plus an assumed response, costed per call and at scale.
A classification call costs a fraction of a cent — until you run a million of them. This prices a small repeated prompt at the scale that actually bills.
Per-call pricing hides the truth for high-volume work: the number that matters is cost at scale. This loads a short sentiment-classification prompt — the kind you might run on every review or ticket — and prices it per 1,000 calls, where fractions of a cent become real money. For batch and pipeline workloads, the single-call figure is a rounding error and the per-1,000 figure is the budget. The tool leads with the number you will actually be billed on.
Load the unit prompt
The short prompt you run on every record.
Read the per-1,000 line
Fractions of a cent multiply into a real figure.
Project your volume
Scale the per-1,000 number to your actual call count.
It's a character-based estimate, not exact billing. The report ends on "these are character-based ESTIMATES, not tokenizer output. Pricing is approximate as of June 2026; providers change rates — verify before relying on a number." The token count is a range (~53, 48–58) and it assumes a Short response (~200 tokens). For actual charges the notFor points to your provider dashboard. The token-counter gives the ballpark; you confirm rates.
Because for batch and pipeline work the single-call figure is a rounding error and the per-1,000 line is the budget. The report prices this prompt at "$0.002060–$0.002073" combined per call — meaningless alone — which becomes "$2.06–$2.07" per 1,000, where fractions of a cent turn into real money. You then scale that per-1,000 number to your actual call volume.
Know how many tokens a job will consume before you send it — input plus an assumed response, costed per call and at scale.
Paste a prompt, get an honest token estimate — a range, not a fake-precise number — plus the cost across GPT, Claude, and Gemini.
Turn a prompt into a dollar figure: input cost, output cost, combined per call, and the number that actually matters — cost per 1,000 calls.
Token budget planning for real workloads: how much of the window a transcript actually consumes, what is left for the answer, and how much headroom remains.
The "message too long" error has a structural fix: split at paragraph boundaries into sequenced chunks with wait rules, instead of pasting fragments and hoping.
A set of before-and-after examples showing exactly what prompt cleanup removes — and what it deliberately leaves alone.
Estimate how many tokens a prompt is and what it costs — honest ranges across GPT, Claude, and Gemini, with per-call and per-1,000-call pricing.
The full path from idea to a shipped SaaS MVP — define and scope the requirements, design the architecture, API, and data model, then build it reviewed, tested, secured, cost-controlled, and deployed.
The full path to a support agent you can put in front of customers — write its instructions, ground it in your docs, route and handle tickets, then evaluate and cost-control it before it goes live.
Cut what an AI feature costs without dumbing it down — price the prompt as it runs today, see where the tokens go, trim the waste, and re-measure to prove the saving holds at scale.