Providers ranked by monthly cost
Per-page rates for the mode you selected. LLM-vision providers do OCR and structured extraction at the same price because one call does both.
| # | Provider | Per page | Monthly | Free tier |
|---|
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.
FAQ
What's the cheapest OCR API?
For plain text, Mistral OCR and the base tiers of Textract, Google Document AI and Azure all land around $1–1.50 per 1,000 pages. The calculator ranks them for your exact volume and mode, including free tiers.
Does OCR pricing include tables and checkboxes?
No — plain OCR returns text only. Tables, key-value pairs and checkboxes require the structured extraction mode, billed 10–30× higher. Switch the mode selector to see those rates.
Is GPT-4o good for OCR?
It's strong at reading plus reasoning in one step and returning clean JSON, which can replace a whole OCR-then-parse pipeline. For huge volumes of plain text, dedicated OCR is usually cheaper and more predictable.
How do free tiers work?
AWS, Google and Azure each include a monthly allotment of free pages (often 1,000 for structured, more for plain OCR, and larger for the first months). Small projects can run entirely inside the free tier — the table flags which providers offer one.
Estimate only. Prices are reference figures for 2026 and change — verify current per-page rates with each provider before committing.