AI Data Extraction Software: 5 Vendors Compared in 2026

The first decision in this category is not which vendor to buy. It is whether you are buying an extraction engine or an extraction process — because those are sold by different companies at very different prices, and most shortlists mix them up.

AI data extraction software is any tool that reads a document a person would otherwise retype — an invoice, a bill of lading, a W-2, a claim form — and returns machine-readable fields instead of a picture of text. TL;DR: the three hyperscalers sell metered engines priced per page, Rossum sells a document-processing product with published outcome claims and no published price, and none of them decides what happens to a field that comes back wrong.

That last gap is where implementations stall, because the engines have largely converged. Reading a printed invoice is close to solved. Deciding that a $0.00 total is a parsing failure rather than a credit memo, routing it to the right person, and getting the correction back into the ERP is not, and no vendor here claims otherwise.

Key Takeaways

• Amazon publishes the clearest unit economics of the three hyperscalers: Detect Document Text at $0.0015 per page versus the Forms feature of Analyze Document at $0.05 per page, in US West (Oregon).

• Azure's Document Intelligence pricing page rendered every rate cell as $- on July 28, 2026 and states prices are estimates only — so no Azure list price appears here, and you should not accept one from a page that quotes it without evidence.

• Google Cloud's Document AI overview names its processor types but publishes no accuracy percentage, page limit or throughput figure. Nobody comparing "hyperscaler accuracy" is comparing published numbers.

• Every accuracy figure Rossum publishes is a customer case-study claim measured on that customer's own document mix — evidence a document class is tractable, not a benchmark against another vendor.

• Decision rule: if a wrong field costs more than a right field saves, you are buying exception routing rather than OCR, and none of these five ships that as a default.

• Decision rule: price the most expensive API you will actually call. A 2,700-page month on Forms extraction and the same month on plain text detection are not the same purchase.

What the Category Actually Sells You

Three of the five vendors here sell an API you meter. You send bytes, you get JSON, you pay per page, and everything downstream — queueing, retries, confidence thresholds, human review, write-back — is code you write.

According to Google Cloud's Document AI overview documentation, the platform "takes unstructured data from documents and transforms it into structured data (specific fields, suitable for a database)" — precise about the transform, silent about the workflow. The page names Enterprise Document OCR, Form Parser, Layout Parser, Custom Extractor, Custom Classifier and Custom Splitter, plus pretrained specialised processors: 6 named processor types, counted July 28, 2026.

Amazon frames its product the same way. According to Amazon Web Services' Textract product page, Textract is "a machine learning (ML) service that automatically extracts text, handwriting, layout elements, and data from scanned documents" — an extraction service, with no accuracy figure and no page limit published on that page.

How We Evaluated These 5 Vendors

Weights reflect what breaks implementations, not what vendors market. The pass threshold is the bar a vendor clears on published evidence, not on a sales call.

Price legibility scores highest because it is the criterion buyers skip and then regret: a category where one vendor publishes a per-page rate and another publishes nothing cannot be compared on a spreadsheet without the work below. Published evidence quality sits lowest deliberately — it is a tiebreaker, not a gate, because almost no vendor here publishes independently audited numbers, so weighting it heavily would just reward the best marketing team.

Verified Pricing, Read on July 28, 2026

Every figure below was read directly from the vendor's own pricing page on July 28, 2026, on the standard non-promotional tier. Where a rate did not render, the cell says so rather than guessing.

According to Amazon Web Services' Textract pricing page, Detect Document Text is $0.0015 per page for the first million pages in US West (Oregon), and the Forms feature of Analyze Document is $0.05 per page over the same tier. Both figures were confirmed on two separate reads that day.

Azure is the more instructive case. According to Microsoft's Document Intelligence pricing page, the service has a free F0 tier covering 0–500 pages free per month — but on the same read every paid rate cell rendered as $-.

That page states plainly that "Prices are estimates only and are not intended as actual price quotes," and directs buyers to the pricing calculator or an Azure sales specialist. That is a documented consequence of program-specific pricing rather than a gotcha. The practical effect is the same either way: you cannot compare Azure's rate to $0.05 per page without signing in first.

Capability Matrix

This matrix records what each vendor's own pages state, not a score we invented. Read it alongside the priced table above, not instead of it.

According to Microsoft Azure AI Document Intelligence, the custom option "uses five samples to learn the structure of your documents" — a custom model path starting at 5 sample documents, the lowest published sample floor of the three hyperscalers and a real reason to shortlist Azure for an odd internal form.

What Each Vendor Publishes, Counted

According to Rossum (checked July 28, 2026), its proprietary transactional LLM supports 276 languages and handwriting, and the same page names pre-built connectors for SAP, Coupa, NetSuite, Workday and Oracle — the only vendor here publishing named systems-of-record integrations on its own front page.

Rossum also publishes customer outcomes, and they need careful handling. According to Rossum (checked July 28, 2026), Morton Salt reports 71% STP and 95% time saved per document, and the Port of Rotterdam Authority reports 810 AP days saved a year plus 90% accuracy after only 10 documents.

The same page credits Fugro with invoice handling cut from 2 minutes to 35 seconds, and Wolt with 44% fewer error rates across 100K invoices a year. Rossum says those things; nobody independent has verified them.

Vendor Profiles

Amazon Textract

Best fit: teams already on AWS that want a metered engine and will write the surrounding workflow themselves. The per-API price granularity is the real advantage — you can call plain text detection on the simple 80% of pages and reserve the $0.05 Forms call for pages that need it.

Limitations: the product page publishes no accuracy figure and no discrete API list; you have to read the pricing page to see the API surface. There is no built-in review queue, so confidence thresholds and escalation are yours to build.

Implementation: budget for the asynchronous path if documents are multi-page. The free tier is time-boxed at 3 months for new accounts, so it sizes a pilot rather than a rollout.

Azure AI Document Intelligence

Best fit: Microsoft-estate organisations with unusual internal forms. The 5-sample custom path is the lowest published training floor here, and the ongoing F0 tier at 0–500 pages per month proves a document class before anyone signs.

Limitations: pricing opacity is the disqualifier for a fast procurement cycle. If approval requires a written unit rate before a pilot, you will be in the calculator or on a sales call first.

Implementation: the prebuilt set on the page we read covers receipts, invoices, forms, cards, utility bills and purchase orders. Confirm your class is prebuilt before committing — a class needing a custom model is a different project.

Google Cloud Document AI

Best fit: teams that need classification and splitting as first-class steps. Custom Classifier and Custom Splitter are named processor types, which matters when intake is a 40-page PDF containing six documents.

Limitations: the overview documentation publishes no accuracy percentage, page limit or throughput figure, and we could not read a rate table on July 28, 2026. That combination makes Google the hardest of the three to compare on paper.

Implementation: design around the batch method for volume; single-document calls will not carry a backlog.

Rossum

Best fit: accounts-payable teams that want a product rather than a primitive, with a review interface and named ERP connectors included. If the problem is specifically supplier invoices, this is the shortest path on this page.

Limitations: no published price at all, so every comparison against a $0.05-per-page API becomes a negotiation. Every published outcome figure is a customer's own measurement.

Implementation: verify the connector list first. If your system of record is not SAP, Coupa, NetSuite, Workday or Oracle, ask what the integration path actually is before the pilot.

US Tech Automations

Best fit: operations teams whose extraction accuracy is adequate but whose exception handling is a shared inbox. This is the orchestration layer above whichever engine you choose: it takes the engine's output, applies your confidence threshold, routes below-threshold fields to a named reviewer, holds the record until it resolves, and writes corrected values back with an audit trail. It is not an OCR engine — see the data extraction agent page for where the boundary sits.

Limitations: if documents are clean and volume is low, an orchestration layer is overhead. It earns its keep on exception rate, not page count.

Costing a 2,700-Page Month

Take a distributor processing 900 supplier invoices a month at 3 pages each — 2,700 pages — where 12% arrive as fax scans that need form extraction rather than plain text detection. Splitting 2,376 clean pages onto Detect Document Text at $0.0015 per page from 324 problem pages onto Analyze Document Forms at $0.05 per page is a two-lane design, and the lane assignment is the whole trick; on Google Cloud the same batch runs through projects.locations.processors.batchProcess overnight instead of 900 synchronous calls.

Now the part no engine covers. At a 6% below-threshold rate, 54 invoices a month land in an exception queue, each needing a person to compare a field against the source image. At 4 minutes apiece that is 3.6 hours of clerical review — small. But those same 54 invoices also hold up payment runs, and the cost that shows up in the P&L is a lost early-payment discount, not the labour. That queue, its owner, its SLA and its write-back is the part you either buy or build.

Where Accuracy Claims Break Down

Extraction accuracy is not a platform number. It is a number per document class, measured on a specific mix, and every published figure in this category was measured on the publisher's mix rather than yours. A vendor reporting 93% average accuracy on typed European invoices tells you nothing reliable about handwritten US delivery tickets. That is why the pass threshold in our criteria table asks for a traceable, dated primary figure instead of a headline percentage.

Our own publishing data makes the same point from the other direction. Every post we publish clears a fixed set of automated quality checks before it goes live — but no check of ours or anyone else's can tell you whether a vendor's accuracy figure transfers to your document mix. Only a sample of your own documents answers that.

There is also a gap worth naming, because it is measurable. According to our own Search Console harvest covering 2026-01-01 to 2026-05-20, 66 queries containing "extract" drew 2,432 impressions and 0 clicks, with "ai data extraction" averaging position 70.64 across 468 impressions.

Meanwhile 0 of our 10,250 live blog slugs contain the string "extract" while 107 contain "data-entry" — we had written the adjacent category and not this one. That is the transparent reason this page exists, and it is the same failure a buyer hits shortlisting by vendor marketing instead of by the document class in front of them. Our earlier data entry automation comparison for small businesses covers that adjacent ground.

Who This Is For

You are the buyer for this category if you process at least a few hundred structured documents a month, they arrive from outside your organisation in inconsistent formats, and a wrong field carries a real downstream cost — a mispaid invoice, a rejected claim, a shipment held at a dock. Typically that means 15+ staff, an ERP that is the system of record, and one person whose week is visibly consumed by retyping.

Red flags: skip this category if you process fewer than 100 documents a month, if your documents originate inside your own systems and could be exchanged as data rather than PDFs, or if no one owns an exception queue — automation with no exception owner relocates a backlog instead of clearing it.

The honest alternative most teams try first is no-code: a Zapier or Make flow that watches a mailbox, calls an extraction API, and drops rows into a sheet. That genuinely works for the happy path. Where it breaks at 900 invoices a month is the 6% that fail — per-task pricing makes retries expensive, there is no queue state so a half-processed document vanishes from the run history, and nobody can answer "who changed this total and why" three weeks later in a supplier dispute.

US Tech Automations covers that specific gap: it holds the record in a resolvable state, assigns the exception to a person, enforces the write-back to the accounting system, and keeps the change log — rather than replacing the extraction call. Building the same thing in-house is a real option; budget it as a queue with retries, an audit table and a review interface, because that is what it is.

Our write-up on automating Stampli alternatives for invoice processing walks the invoice-specific version of that build-versus-buy call, and the broader business data entry automation guide covers the intake question that sits upstream of extraction.

Buyer Mistakes That Cost the Most

Frequently Asked Questions

Which AI data extraction software is cheapest?

Amazon Textract, for plain text detection, at $0.0015 per page for the first million pages in US West (Oregon) as read on July 28, 2026. That only holds for the simplest call — Textract's Forms feature is $0.05 per page, and Azure's and Google's rates were not readable on their public pages the same day, so a true cheapest-vendor ranking is not available from published data.

Do the hyperscalers publish extraction accuracy figures?

No. Neither Amazon's Textract product page, Microsoft's Document Intelligence product page, nor Google's Document AI overview published an accuracy percentage, page limit or throughput figure when we read them on July 28, 2026. Any accuracy comparison between them is sourced from somewhere other than the vendors.

Is automated data extraction software accurate enough to remove human review?

Not as a category-wide claim. Accuracy is document-class dependent, and the published figures that exist are customer case-study measurements on that customer's own mix. The workable design is a confidence threshold plus a review queue for what falls below it, sized from your own first month of data.

What is the difference between intelligent data extraction software and plain OCR?

OCR returns text; intelligent extraction returns named fields. Google Cloud's Document AI overview puts it as turning "unstructured data from documents" into "structured data (specific fields, suitable for a database)" — the difference is that a total, a date and a vendor name come back labelled instead of as a page of characters you still have to parse.

When should you not use US Tech Automations for this?

When the engine is the whole job. If you need raw text off 50,000 clean scanned pages with nothing downstream — no approvals, no corrections, no write-back — call Textract directly and skip the orchestration layer entirely. The same answer applies to an AP-only shop whose whole problem is supplier invoices into SAP: Rossum's prebuilt path is shorter. Orchestration earns its cost when documents cross several systems and a wrong field has to be caught, routed and corrected by a named person.

Can AI based data extraction software handle handwriting?

Some of it, and the vendors say so directly. AWS describes Textract as extracting "text, handwriting, layout elements, and data from scanned documents," and Rossum states handwriting support alongside its 276 languages. Neither publishes a handwriting-specific accuracy figure, so pilot handwriting as its own document class rather than assuming a checkbox.

Sources and Further Reading

The Textract pricing page carries the remaining rates read on July 28, 2026: Analyze Expense at $0.01 per page and Analyze ID at $0.025 per page for the first 100,000 pages in US West (Oregon). The Document Intelligence pricing page instructs buyers to "Sign in to the Azure pricing calculator to see pricing based on your current program/offer with Microsoft" — which is exactly why no Azure rate appears above.

Whichever engine you land on, the next decision is the same: who owns the field that came back wrong, and what happens to the document while they fix it. That part is priced openly at ustechautomations.com/pricing.

Written by Garrett Mullins, Workflow Specialist at US Tech Automations, who builds document-intake and exception-routing workflows around third-party extraction engines for operations teams.