Why batch token analysis matters
A single prompt sample can hide the real cost of production traffic. Batch analysis lets teams inspect many rows from support tickets, user questions, document chunks, product descriptions, or evaluation prompts before those rows become paid API calls.
How this tool reads files
The first version reads the first column from a CSV or XLSX file and estimates token count and input cost for each row. Legacy XLS files should be exported to CSV or XLSX before upload. This keeps the workflow simple for early budgeting while still helping teams find unusually long or expensive prompt samples.
Example: finding expensive support prompts
If most support questions are under 200 tokens but a few rows include full chat histories or copied documents, those rows can dominate monthly spend. Batch analysis helps you catch those outliers and decide whether to trim context, summarize documents, or route them to a cheaper model.
Formula used for each row
Row input cost = estimated row tokens / 1,000,000 x selected model input price. The first release estimates input cost only, because many teams use batch analysis to understand prompt and context size before modeling expected output length.
Frequently asked questions
What does the batch token calculator analyze?
It reads the first column of a CSV or Excel file and estimates token count and input cost for each row.
Does batch analysis call an AI API?
No. The first version estimates token usage locally. If Supabase is configured, upload metadata and results can be saved to your workspace.
Why should I analyze prompts in bulk?
Bulk analysis helps teams find expensive prompts, long retrieved contexts, and high-volume rows before those costs appear in production bills.