Intelligent document processing, or IDP, reads invoices, contracts, and forms with AI, then extracts fields into systems your team already uses. The cloud APIs are the cheapest way to start: Textract and Google Document AI charge $1.50 per 1,000 pages for plain text, and Azure charges $10 per 1,000 pages for prebuilt invoice models. Nanonets starts at $0.30 per page with no monthly commitment, which suits irregular volumes, while enterprise platforms such as ABBYY, Hyperscience, and Rossum are priced by quote.
Accuracy is the number that matters in document processing. A tool that reads 99 out of 100 invoice totals correctly still sends one wrong total to your books, so the best platforms pair a strong extraction model with a review step that catches the misses. The eight tools below cover two paths: packaged platforms that handle the whole workflow, and cloud APIs that extract text for a price per page.
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Best idp platforms for data extraction accuracy: comparison table
Swipe the table sideways on a phone to see every column.
| # | Name | Best for | Starting price | Free plan / trial | Link |
|---|---|---|---|---|---|
| 1 | ABBYY Vantage | Enterprise document AI | quote only | No | Visit |
| 2 | Hyperscience | High-volume back-office automation | quote only | No | Visit |
| 3 | Rossum | Transactional document automation | quote only | No | Visit |
| 4 | UiPath Document Understanding | UiPath automation users | quote only | No | Visit |
| 5 | Microsoft AI Document Intelligence | Microsoft-centered teams | $10 per 1,000 pages | Yes | Visit |
| 6 | Amazon Textract | AWS-centered teams | $1.50 per 1,000 pages | Yes | Visit |
| 7 | Google Document AI | Google Cloud teams | $1.50 per 1,000 pages | Yes | Visit |
| 8 | Nanonets | Pay-as-you-go extraction | $0.30 per page | No | Visit |
The 8 best best idp platforms for data extraction accuracys, in detail
ABBYY Vantage
Best for: Enterprise document AI
ABBYY has worked on document capture for decades, and Vantage is its low-code platform for building document skills that extract fields from invoices, contracts, and forms. The platform includes pre-trained skills for common documents, plus tools for training custom skills on your own layouts. Accuracy controls include confidence scores and a review queue where staff correct low-confidence fields before data moves downstream.
- The main limit is access.
- ABBYY Vantage is priced by quote, and the platform fits enterprises with large document volumes and a team to manage it.
- A small company with a few hundred pages a month will find the sales process heavy.
- For a large operation processing thousands of documents a day, the pre-trained skills and the review workflow can cut manual keying without giving up
Pros
- Long history in document capture
- strong table and handwriting handling
Cons
- Pricing is only available by quote
- enterprise deployment takes planning
Hyperscience
Best for: High-volume back-office automation
Hyperscience focuses on back-office documents at scale: claims, applications, invoices, and forms that arrive in many layouts. Its platform reads handwriting and print, classifies documents, and extracts fields, then routes low-confidence results to staff for review. The review step is part of the product, not an add-on, and corrections feed back into the model so accuracy improves over time.
- The trade-off is the same as other enterprise IDP tools.
- Pricing is quote-based, and the platform earns its keep at volume, where a small accuracy gain is worth a lot of money.
- A company processing a few hundred documents a month will not see the return.
- A bank, insurer, or government agency with a constant flow of forms will find the combination of classification, extraction, and review hard to replac
Pros
- Strong accuracy on varied layouts
- human-in-the-loop review built in
Cons
- Pricing is only available by quote
- best for large, steady volumes
Rossum
Best for: Transactional document automation
Rossum takes a template-free approach: its AI learns to find fields on a document without a fixed layout for each supplier, which helps when invoices arrive in hundreds of formats. Extracted fields appear in a validation screen where staff confirm or fix values, and the platform tracks accuracy per field so a team can see where errors cluster. It connects to common finance and ERP systems to post approved data automatically.
- The limits are price transparency and scope.
- Rossum is priced by quote, so a buyer cannot compare costs without a sales conversation.
- The platform is also built for transactional documents, so a team that needs to process contracts or long legal files may find it less suited.
- For accounts payable and order processing, the template-free extraction and the validation workflow make it a strong pick.
Pros
- Learns document layouts without templates
- clean validation interface
Cons
- Pricing is only available by quote
- no published per-page rate
UiPath Document Understanding
Best for: UiPath automation users
UiPath Document Understanding is the document AI layer of the UiPath automation platform. It classifies documents, extracts fields, and sends uncertain results to a validation station where a person confirms them. The extracted data then flows into a UiPath robot that can enter it into a finance system, a CRM, or a database without manual retyping.
- The platform supports pre-trained models for invoices, receipts, and purchase orders, plus custom models for other documents.
- The main limit is the ecosystem.
- Document Understanding delivers the most value when a company already runs UiPath, since the extraction feeds directly into robots and queues.
- Pricing is quote-based, and a company without UiPath would pay for a platform it only partly uses.
Pros
- Fits UiPath robots and workflows
- strong validation stations
Cons
- Pricing is only available by quote
- most value comes inside UiPath
Microsoft AI Document Intelligence
Best for: Microsoft-centered teams
Microsoft AI Document Intelligence, formerly Form Recognizer, is a cloud service that reads documents and returns text, tables, and fields. Plain text reading costs $1.50 per 1,000 pages, while prebuilt models for invoices, receipts, IDs, and contracts cost $10 per 1,000 pages. Custom extraction models cost $30 per 1,000 pages.
- A free tier covers 500 pages a month, which is enough to test the service on real documents.
- The service is an API, not a finished application, so a team needs to build the workflow around it: upload, extraction, review, and posting to a syste
- That is a fair trade for Microsoft-centered teams, since the service plugs into Azure storage, functions, and Power Automate.
- Accuracy is strong on standard documents, and the per-page price makes the cost easy to model at any volume.
Pros
- Prebuilt models at $10 per 1,000 pages
- free tier of 500 pages a month
Cons
- Building a full workflow takes extra Azure services
- custom models cost more
Amazon Textract
Best for: AWS-centered teams
Amazon Textract is the document analysis service in AWS. It detects text, tables, forms, and signatures, and its Analyze Expense feature pulls fields from invoices and receipts. Plain text detection costs $1.50 per 1,000 pages, layout analysis costs $4, and expense analysis costs $10 per 1,000 pages.
- New AWS customers get a free tier covering 1,000 text pages a month for three months.
- Like the other cloud APIs, Textract returns structured data and leaves the workflow to the buyer.
- A team can pair it with Lambda, Step Functions, and a review interface to build a full pipeline.
- The per-feature pricing is the key detail: tables cost $15 per 1,000 pages and forms cost $50, so a buyer should price the exact features it needs rat
Pros
- Plain text at $1.50 per 1,000 pages
- expense analysis at $10 per 1,000 pages
Cons
- Building a review workflow takes extra AWS services
- forms cost more per page
Google Document AI
Best for: Google Cloud teams
Google Document AI offers OCR and document parsing as a cloud service, with prebuilt processors for invoices, receipts, contracts, and identity documents. Enterprise OCR costs $1.50 per 1,000 pages after 1,000 free pages a month, and the invoice parser charges $0.10 per document of up to 10 pages. Custom extractors cost $30 per 1,000 pages, plus hosting for each custom version.
- The service fits teams already on Google Cloud, since documents can move from Cloud Storage through Document AI into BigQuery or a workflow tool with
- The invoice parser pricing is notable: a 10-page invoice costs $0.10, which beats per-page rates for multi-page documents.
- As with the other cloud APIs, a review step must be built separately, so accuracy depends on how the team handles low-confidence fields.
Pros
- Enterprise OCR at $1.50 per 1,000 pages
- strong invoice parser pricing
Cons
- Building a full workflow takes extra Google Cloud services
- custom models cost more
Nanonets
Best for: Pay-as-you-go extraction
Nanonets is the most accessible entry point in this list for a team that wants a working product without an enterprise sales cycle. Its Starter tier processes pages at $0.30 each with no monthly commitment, using pre-trained models for invoices, receipts, purchase orders, and IDs. A visual workflow builder handles extraction, validation rules, approval routing, and integrations without code, and new users start with free credits.
- The limit is the per-page meter.
- A 12-page document counts as 12 pages, so long documents add up fast, and the Pro tier is quoted at $999 a month per workflow.
- For low and irregular volumes, the Starter rate is the simplest deal here.
- For steady high volumes, a team should compare the per-page cost against a platform with a monthly plan or a cloud API, since the numbers cross over q
Pros
- Simple pay-as-you-go pricing at $0.30 per page
- pre-trained invoice models
Cons
- Per-page billing gets costly on multi-page documents
- Pro workflows are $999 a month
How we chose
We compared IDP platforms that publish extraction accuracy features such as confidence scores, review queues, and validation workflows, then checked each vendor pricing page and product documentation for per-page rates and plan prices. Prices come from vendor pricing pages checked on 2026-11-12, and where a vendor does not publish a price, we say quote only rather than guessing. We did not test any product hands-on, and we did not count vendor marketing claims as facts. Tools that only do plain OCR with no field extraction were left out.
What to check before you buy
Test accuracy on your own documents, not on vendor samples, since layout variety is what breaks most tools. Ask how confidence scores work and whether low-confidence fields go to a human before data is posted. Price the exact features you need, because per-page rates differ sharply between plain text, tables, forms, and custom models. Check which systems the tool connects to, and confirm whether corrections improve the model over time. Finally, count multi-page documents separately, since per-page billing multiplies fast on long files.
Frequently asked questions
What is intelligent document processing?
Intelligent document processing, or IDP, uses AI to read documents, classify them, and extract fields such as invoice totals, dates, and vendor names. The extracted data is then checked, often by a person, and posted to a finance system, CRM, or database without manual typing.
How accurate is IDP software?
Accuracy varies by document type and layout. Standard invoices from known suppliers can reach very high field accuracy, while handwritten forms and unusual layouts score lower. A review workflow matters more than the headline number, because it catches the errors that slip through.
What is the difference between OCR and IDP?
OCR reads text from a page. IDP goes further: it classifies the document, finds the fields that matter, checks them against rules, and routes uncertain results to a person. OCR is a step inside IDP, not the whole job.
How much does IDP cost per page?
Cloud APIs charge $1.50 per 1,000 pages for plain text and about $10 per 1,000 pages for invoice extraction. Packaged tools such as Nanonets charge $0.30 per page on pay-as-you-go plans, while enterprise platforms are priced by quote and usually cost far more at low volumes.
Do I need a human review step?
Yes, for most use cases. Even a strong model makes mistakes, and a wrong invoice total or contract date can cost more than the review time saves. The best tools flag low-confidence fields so staff only check the uncertain ones instead of every page.
Sources
- https://www.abbyy.com/
- https://www.abbyy.com/pricing/
- https://hyperscience.com/
- https://hyperscience.com/pricing/
- https://rossum.ai/
- https://rossum.ai/pricing/
- https://www.uipath.com/
- https://www.uipath.com/pricing
- https://azure.microsoft.com/
- https://azure.microsoft.com/en-us/pricing/details/document-intelligence/
- https://aws.amazon.com/
- https://aws.amazon.com/textract/pricing/
- https://cloud.google.com/
- https://cloud.google.com/document-ai/pricing
- https://nanonets.com/
- https://nanonets.com/pricing
Prices and features were read on each company’s own website in November 2026. They change often, so confirm the current offer on the official site before you sign up or buy.