Google Cloud Vision vs Document AI: Which Google OCR to Use

Last updated July 2026.

Use Google Cloud Vision when the text itself is the answer, and Google Document AI when the layout means something. Cloud Vision is an image API: it returns characters, words, blocks, and bounding boxes for about $1.50 per 1,000 units. Document AI is a document API: it returns named fields, table structure, and trained document types, from about $10 per 1,000 pages for prebuilt parsers up to about $30 per 1,000 for the Form Parser and Custom Extractor. Vision cannot extract form fields at any price. That single fact decides most projects.

The difference in one table

Rates were read from Google's own published pricing pages in July 2026. Google revises them and varies them by region, so confirm before you build a budget on them.

What is the difference between Cloud Vision and Document AI?

Cloud Vision is a general image understanding API that happens to include excellent OCR. You hand it a picture and it tells you what characters are in it and where they sit. It will also label objects, find faces, detect logos, and read handwriting. It is built around images, and text is one of many things it can find in one.

Document AI is built around documents. It knows that a page has a structure, that a label sits next to a value, that a table has rows and columns that mean something, and that an invoice is a different kind of thing from a bank statement. You send it a document and choose a processor: Enterprise Document OCR for text, the Form Parser for key-value pairs, a prebuilt Invoice Parser for invoices, or a Custom Extractor you train on your own document type.

The naming does not help. Both read text. Both are Google Cloud services. Both quote a rate per 1,000. The distinction only becomes obvious when you look at what comes back in the response body, and by then you have often already written code against the wrong one.

Can Google Cloud Vision extract form fields?

No. Cloud Vision returns text and coordinates, never named fields. It will happily tell you that the string "Invoice Total" appears at one position and that "$4,318.00" appears 180 pixels to the right of it. Working out that the second belongs to the first, on every vendor template you receive, is code you write and own. Google's answer for that job is Document AI.

This is the single most common mistake buyers make with the Google stack. Cloud Vision is cheaper per unit, it appears first in most search results, and its OCR quality on printed text is genuinely good. Teams pick it, get clean text back, and only then discover that turning that text into an invoice number and a total is the entire project. The $8.50 per 1,000 pages they saved by avoiding the Invoice Parser buys a few hours of an engineer's time, once.

How much does each one actually cost?

Cloud Vision charges about $1.50 per 1,000 units for text detection and document text detection, after 1,000 free units a month, dropping to about $0.60 per 1,000 above five million units a month. A unit is one feature applied to one image, so asking for two features on one image bills two units, and every page of a PDF counts as one image. The full breakdown, including the billing traps, is in our Google Cloud Vision API pricing guide.

Document AI charges per page and per processor. Enterprise Document OCR is about $1.50 per 1,000 pages, the same as Vision. The Layout Parser is about $10 per 1,000. The Form Parser and Custom Extractor are about $30 per 1,000, falling to about $20 above a million pages. Prebuilt invoice and expense parsers bill $0.10 per 10 pages, which is $10 per 1,000, but they bill in blocks of ten, so an 11-page invoice bills as 20 pages. There is also a hosting charge of about $0.05 an hour for each deployed custom processor version, roughly $438 a year, and it accrues whether or not the processor sees a single page. We cover that in detail in our Google Document AI pricing guide.

Which is better for invoices and receipts?

Document AI, without much argument. Google publishes prebuilt Invoice and Expense parsers that return named fields for about $10 per 1,000 pages. Using Vision for the same job means paying $1.50 per 1,000 pages and then building the field extraction, the template handling, and the validation yourself. The rate difference is $8.50 per 1,000 pages. The engineering difference is measured in weeks.

That said, both are still APIs, and an API is a component rather than a workflow. If you are an accounts payable or bookkeeping team whose real goal is coded, posted invoices rather than a JSON response, neither Google product is the finished thing. Sometimes the smaller purchase is the right one: if what you have is a folder of expense receipts and what you want is a spreadsheet, pulling the receipt totals straight into Excel is a far shorter path than standing up a processor and writing an integration around it.

When Cloud Vision is the right choice

• The input is a photograph, a screenshot, a product image, or signage, and the text is the whole answer.
• You want raw text at very high volume and will do your own downstream processing anyway.
• You need image features beyond text: labels, logos, object localization, safe search.
• You are reading dense printed pages and only need a searchable text layer, not fields.

In those cases Vision is correct and cheap, and you should use it. At about $1.50 per 1,000 units, with a free 1,000 units a month for testing, it is one of the better values in the Google Cloud catalog.

When Document AI is the right choice

• The input is a business document: an invoice, a bank statement, a claim form, a contract, a pay stub.
• You need specific values as named fields, not a wall of text you have to hunt through.
• The layout carries meaning, especially tables and multi-column forms.
• You have a document type nobody publishes a parser for, and you are willing to train a Custom Extractor.

Budget for the hosting fee if you go the custom route. One deployed processor version at about $0.05 an hour is roughly $37 a month, and teams routinely leave three or four versions deployed after testing. That is over $1,700 a year of pure idle cost, and it does not appear anywhere in the per-page rate you used to justify the project.

What neither of them gives you

Both products stop at the response body. Neither classifies a mixed batch of documents so the right parser sees the right page. Neither validates that the line items on an invoice add up to the total. Neither gives you a screen where a person reviews the values the model was unsure about, or an audit trail showing who changed what. Neither exports clean records into your ERP.

That surrounding work is the actual document pipeline, and for most US mid-market teams it costs more in the first year than the API does, then recurs every year in maintenance. It is worth pricing honestly before the per-page rate decides the architecture. If you would rather not build it, a finished platform like DocuOCR includes classification, extraction, validation, human review, and export at about $14 to $20 per 1,000 pages, and you can call it as a single OCR API instead of assembling one.

How do I decide in under a minute?

Ask what you would do with a perfect response. If a perfect response is the complete text of the page and you already know what to do with it, use Cloud Vision. If a perfect response is a small set of named values pulled out of a layout, use Document AI. If a perfect response is a validated, reviewed record sitting in your accounting system, neither API is the product you are shopping for, and you should compare finished platforms instead.

For the rates across the whole market, including AWS Textract, Azure AI Document Intelligence, and the surprisingly cheap structured extraction tier on Mistral OCR pricing, see our OCR API pricing comparison.