Document AI Cost at Scale: Pages Are the Small Part

A document AI bill has two lines. The first is the vendor: pages times a price per page. That one's easy to look up.

The second is people. It's the minutes someone spends checking the documents the machine wasn't sure about. At the volumes most teams start with, that second line is several times bigger than the first.

This post collects list prices from the official pages as of September 26, 2026. Then it runs a worked example through the document processing cost calculator to show where the money actually goes. It's part 9 of the Document AI series and builds on the review queue design, which is where most of that second line comes from.

What a page costs

These are list prices for pulling specific fields out of a document, like the vendor and total on an invoice, not just reading its text. That's what an invoice or form pipeline needs. All prices are in US dollars, standard pay-as-you-go unless noted, read on 2026-09-26.

Sources: Datalab pricing and extraction modes, Extend pricing and credit docs, Google Document AI pricing, Azure Retail Prices API, and Mistral's API pricing.

A few details change the math:

• Datalab charges for each step separately. Its docs say extraction prices don't include parsing, which is charged when it runs. So a fast extraction is $6 plus $4 per 1,000 pages. The pricing page also says around 5% of balanced and accurate extractions cost more than the flat rate, usually a dollar or two per document.
• Extend's extraction includes the parse. Its pricing page says an extraction costs 3 credits a page plus 2 for the automatic parse. Its example: a 10-page Performance extraction is 50 credits, about $0.63. The Scale plan, $500 a month with 50,000 credits, drops extra credits to $0.01. Its Review Agent adds 1 credit a page.
• Azure's pricing page hides its numbers. On 2026-09-26 the Azure pricing page loaded its prices with JavaScript, so a plain download of the page showed only the free tier of 500 pages a month. The figures above come from Microsoft's public Retail Prices API for East US. That's why the Lab calculator has no Azure preset. Enter the rate as a custom price.
• Google's specialized parsers charge per document. The Invoice parser is $0.10 per count, and one count is up to 10 pages. For one-page invoices that's 10 cents a page. For ten-page ones it's one cent.

Big monthly commitments lower the price

Azure also sells monthly commitment tiers: you agree to a volume up front and pay less per page. In the Retail Prices API on 2026-09-26, the custom extraction tier for 500,000 pages was $10,500 a month, with extra pages at $21 per 1,000. That works out to $0.021 a page, against $0.030 pay-as-you-go. Google's custom extractor drops from $30 to $20 per 1,000 pages above a million pages a month.

At a few thousand documents, none of this applies. At a few hundred thousand, the tier you sign up for matters more than whose list price looked lowest.

Sending pages straight to an LLM

The other option skips the document AI vendor. You send the PDF to a general model like Gemini or Claude, along with a JSON schema that lists the fields you want back.

The page cost is lower. But you pay for it in engineering time, because you have to build the citations, confidence scores, checks and review hooks yourself.

Sources: Gemini API pricing and media resolution, Claude pricing and PDF support, read 2026-09-26.

Models charge by the token, which is roughly a piece of a word. Here's where the numbers come from.

The Gemini figure is 560 tokens a page, the default setting in its media resolution table for Gemini 3 models. Google's pricing page says the Flash input price doubles to $1.50 on January 1, 2027. Output is $3.75 per million tokens until then.

Claude's PDF docs say each page usually uses 1,500 to 3,000 text tokens, and is also turned into an image that's charged separately. For Haiku 4.5, an image is capped at 1,568 tokens, which gives the top of the range.

Neither figure includes your prompt, your schema or the answer the model sends back. If each answer runs about 400 tokens (an illustrative number), output adds about $1.50 per 1,000 documents on Gemini 3.8 Flash and $2 on Haiku 4.5.

These are by far the cheapest rows in the post. They're also where the page price matters least. How much review you need depends on how well the answers get checked, and on this route nothing gets checked unless you build it.

A worked example with the Lab calculator

The calculator adds up two costs.

• Vendor cost: documents times pages times price per page.
• Review cost: documents times the exception rate (the share flagged for a person) times minutes per review times an hourly wage.

Then it compares the total against doing every document by hand. Its defaults are 2,000 documents a month, 5 pages each, 8% exceptions, 6 minutes per exception, 4 minutes to do a document by hand, and $45 an hour. Those defaults are illustrative, not measured. What matters is the shape of the result.

The figures below come from running the calculator's own code with these inputs.

Doing all 2,000 documents by hand at 4 minutes each costs $6,000 a month with the same inputs, so every scenario saves money.

What moves the total is the exception rate, not the vendor's price. Going from the cheapest extraction in the table to the most expensive adds $525 a month. Going from 8% exceptions to 20% adds $1,080.

When a pricier vendor pays for itself

Each percentage point of exception rate costs documents times 1% times review time times the hourly wage. With the defaults, that's 2,000 times 0.01 times 0.1 hours times $45, or $90 a month per point.

That gives you a simple test for any more expensive extractor. Here, the Extend Performance row costs $525 a month more than the Datalab row. It only pays for itself if it cuts the exception rate by at least 525 divided by 90, about 5.8 points. That means going from 8% to roughly 2.2%.

A pricing page can't tell you whether that'll happen. You find out by running both on the same documents and scoring them, as in measuring extraction accuracy field by field.

The same math works at any size. At 100,000 documents a month with the same inputs, the calculator gives $5,000 of vendor cost against $36,000 of review, and each exception point is worth $4,500. At that volume, a vendor that removes even one point of exceptions can justify a noticeably higher price per page.

What the calculator leaves out

• Spot-checks. The review queue post recommends checking a random sample of the documents that went straight through. At 189 documents a month, 6 minutes each and $45 an hour, that's about $850. At small volume it can cost more than reviewing the flagged ones.
• Building and running the pipeline. The calculator has a field for a one-time build cost, to work out payback. It doesn't cover ongoing upkeep, hosting, or the n8n or cloud bill for the pipeline around the extractor.
• Running documents again. If you change your schema and have to rerun last month's documents, you pay for those pages again.
• Mistakes that slip through. A wrong total that reaches your books costs whatever it takes to find and undo it. That's the cost the review queue exists to prevent, and no vendor's pricing page shows it.

What to do with this

1. Estimate pages per document and monthly volume from your real documents, not a guess.
2. Price the vendor line from the official page on the day you decide, and write down the date. Several of these prices have announced changes coming.
3. Run a pilot on a few hundred of your own documents to measure the exception rate and review time for each option.
4. Put those numbers into the calculator and compare totals, not per-page prices.
5. Use the $90-a-point math, with your own numbers, to decide whether a pricier extractor is worth it.

Next in the series: the Document AI glossary. The previous post covers designing the document review queue. To see each vendor's prices in context, read Extend vs Datalab vs other parsers and Datalab in practice.