This planner estimates the monthly token volume and model cost for an AI document-processing workflow. It translates document count, average page count, token density, expected output length, and provider pricing into separate input and output token totals. The result helps engineering and operations teams establish a first-pass inference budget before production deployment.
Token usage varies with extraction quality, prompt size, document language, tables, repeated context, and retry behavior. Provider prices can also differ by model and billing tier. Treat the estimate as a configurable baseline, then replace assumptions with measurements from representative documents. Non-token expenses such as OCR, storage, vector search, orchestration, and human review should be budgeted separately.
Inputs
pages
tokens
%
$
$
Result
—
Estimated monthly token cost
Input tokens—
Output tokens—
Total tokens—
1. Estimate monthly document volume
Use the number of files expected in a typical month.
2. Enter average pages
Use a representative mix rather than only small documents.
3. Set tokens per page
Include extracted text plus prompt overhead allocated to each page.
4. Estimate output ratio
Express generated tokens as a percentage of input tokens.
5. Enter current token prices
Use the input and output rates for the selected model.
6. Review volume and cost
Compare the estimate with pilot telemetry and add non-model costs separately.
Pages per document: average pages processed per file
Tokens per page: estimated model input tokens after extraction and prompting
Output ratio: generated tokens as a percentage of input tokens
Token prices: provider prices per one million tokens
Assumptions: Every page is processed once with similar prompt overhead. Retries, embeddings, OCR vendor charges, caching discounts, and batch discounts are excluded unless reflected in the entered token or price assumptions.
What the result means
The main result is an estimate based on the values entered and should be interpreted together with the supporting metrics shown.
Use consistent periods and units, and replace planning assumptions with observed data when available.
This is model-token spend only and should be combined with OCR, storage, and infrastructure costs.
How can I estimate tokens per page?
Measure a sample after extraction using the tokenizer for the intended model. Scanned forms and dense tables can differ substantially from plain text pages.
Should prompt instructions be included?
Yes. Allocate system and task prompt tokens across each request, especially when every page or document repeats the same context.
Does this include embeddings?
No. Add embedding token volume and pricing separately if the workflow creates or queries vector representations.
How should retries be modeled?
Increase document volume or tokens per page by an observed retry factor. A ten percent retry rate can be approximated by multiplying the baseline by 1.10.
Why might the provider invoice be lower?
Caching, batch processing, committed-use discounts, or lower actual output length can reduce billed cost relative to the baseline.