OCR & Document AI Cost Calculator

OCR & Document AI cost calculator

✓ Last verified: 2026-08-15· Source: official provider pricing page· Auto-monitored — report change →

Reading text off a page is cheap; pulling structured fields off it is not. Enter your monthly page volume and this tool ranks AWS Textract, Google Document AI, Azure Document Intelligence, Mistral OCR and GPT-4o vision by real per-page cost — for plain OCR or form/table extraction — so you don't overpay for structure you don't need.

Plain OCR is a commodity; structured extraction is where the bill grows

Every major OCR provider will read the text off a page for roughly the same money — about $1 to $1.50 per 1,000 pages. At that rate, OCR is essentially a solved commodity and the choice comes down to accuracy on your document type and which cloud you already use. The cost cliff appears the moment you want structure: key-value pairs from a form, cells from a table, line items from an invoice. AWS Textract's AnalyzeDocument, Google's Form Parser and Azure's prebuilt/custom models charge 10–30× the plain-OCR rate because they run layout analysis on top of reading. The single biggest way to cut a document-AI bill is to route only the pages that truly need structure to the expensive path, and everything else to cheap OCR.

Where LLM vision fits

GPT-4o and Gemini vision changed the math. They cost more than commodity OCR per page but can read and extract structured JSON in one call, collapsing an OCR step and a separate parsing step into one. If your pipeline currently does "OCR → regex/parse → clean up", an LLM-vision call can replace all three and often lands cheaper end-to-end than Textract's form pricing — with the caveat that LLM cost scales with image resolution and the length of the JSON it returns. Model the token side with the LLM token cost calculator and the vision input before committing at scale.

Bring OCR into the rest of your stack cost

OCR rarely stands alone — the extracted text usually flows into an LLM for summarizing, classifying or answering questions, which is where most of the real spend lives. Size that downstream cost with the RAG cost calculator or the chatbot cost calculator, and roll the whole pipeline up in the API stack cost calculator. Reading pages at $0.001 each is the cheap part; the tokens you spend understanding them is the number that decides your unit economics.

How to use it

1. Enter how many pages you process per month.
2. Choose plain OCR or forms & tables — this switches every provider to the right rate.
3. Read the cheapest option, the per-page cost, and how much you'd overpay with the priciest provider.

Common mistakes

Paying form rates for OCR work. If you only need the text, never call AnalyzeDocument/Form Parser — the plain OCR endpoint is a fraction of the price. Ignoring free tiers. Azure, Google and AWS all give monthly free pages that cover small workloads entirely. Forgetting resolution on LLM vision. High-detail image mode multiplies token cost per page; downscale where accuracy allows. Reprocessing the same document. Cache OCR output so a re-run of your pipeline doesn't re-bill every page.

Estimate only. Prices are reference figures for 2026 and change — verify current per-page rates with each provider before committing.

How this calculator works

The OCR & Document AI Cost Calculator estimates what you will pay each month to convert scanned pages, PDFs, and images into machine-readable data across major providers. You enter your pages processed per month and select what you are extracting—for example plain text versus structured fields like tables, forms, or key-value pairs—and the tool multiplies your volume by each provider's per-page rate. It then places AWS Textract, Google Document AI, Azure Document Intelligence, and Mistral OCR side by side so you can see how the same workload prices out differently depending on the vendor and the type of extraction you choose.

The main trade-off to watch is that extraction complexity, not just page count, drives your bill: structured document parsing typically costs several times more per page than raw text recognition, so choosing the richest option when you only need plain text can quietly multiply your monthly cost. The practical tip is to enter your realistic page volume and match the extraction type to what your workflow actually consumes downstream, then compare providers at that exact setting. Because per-page rates and free tiers change over time, treat the output as a planning estimate and confirm current pricing before committing to a vendor.

Frequently asked questions

How much does OCR cost per page in 2026?

Plain text OCR is cheap: roughly $1–1.50 per 1,000 pages ($0.001–0.0015 per page) from AWS Textract, Google Document AI, Azure Document Intelligence and Mistral OCR. Structured extraction — pulling out fields from forms or cells from tables — costs far more, typically $10–50 per 1,000 pages ($0.01–0.05 per page) because it does layout analysis on top of reading the text. LLM vision models like GPT-4o sit in the middle at a few tenths of a cent per page and can do both.

Is it cheaper to use GPT-4o vision or a dedicated OCR API?

For plain text at scale, dedicated OCR (Textract, Google, Azure, Mistral OCR) is usually cheaper and more predictable per page. LLM vision like GPT-4o or Gemini becomes attractive when you need OCR plus reasoning in one call — reading a document and returning structured JSON — because it replaces both an OCR step and a separate extraction/parsing step. The trade-off is that LLM cost scales with image resolution and output tokens, so a dense page can cost more than the flat OCR rate.

What is the difference between OCR and form/table extraction pricing?

OCR just returns the text it sees. Form and table extraction (Textract AnalyzeDocument, Google Form Parser, Azure prebuilt/custom models) also understands structure — key-value pairs, checkboxes, table cells — and is billed at a much higher rate, often 10–30× the plain OCR price. Only pay for structured extraction on the pages that actually need it; run everything else through cheap plain OCR.

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