AWS Textract Alternatives

AWS Textract alternatives are worth a look when the per-page bill climbs as you switch on more features, when your documents do not fit its limits, or when you want fields returned in a shape your application already expects. Textract is a capable service and a sensible default on AWS, so this guide starts with what it does well and where teams run into trouble. It then compares eight AWS Textract alternatives using each vendor's own published pricing and limits, and closes with a migration checklist and a pilot plan. Every price below comes from a vendor page and should be confirmed before you commit.

What Amazon Textract Does Well

Textract is a managed AWS service for reading documents. It offers plain text detection, tables, forms, queries, signature detection, layout, and dedicated operations for expenses (invoices and receipts), identity documents, and lending packages. It integrates directly with the rest of AWS, which makes it easy to adopt if your storage, queues, and permissions already live there.

Pricing is per page and varies by feature. In the US West (Oregon) region, AWS lists text detection at $0.0015 per page for the first million pages, tables at $0.015, forms at $0.05, queries at $0.015, expense analysis at $0.01, and ID analysis at $0.025 for the first 100,000 pages. New AWS customers get a three-month free tier. If your documents are clean, your volume is steady, and you want plain OCR or a standard expense parser, Textract is cheap and reliable enough that you may not need to move.

Why Teams Look for an AWS Textract Alternative

The reasons come up repeatedly in practice, and several are visible in AWS's own documentation.

Cost stacks as you add features. Each analysis feature is billed separately. Tables plus queries are listed at $0.020 per page, and forms, tables, and queries together at $0.070 per page, which is $70 per 1,000 pages. A per-document cost that depends on which features you enable is harder to forecast. DeepRead's breakdown of per-document vs per-page pricing shows how the billing unit changes your total.
Synchronous calls handle one page. Per AWS's quotas, synchronous PDF and TIFF processing is limited to 1 page and 10 MB. Multi-page files need asynchronous jobs, which accept up to 3,000 pages and 500 MB, and that changes your integration design.
Language coverage is narrow. Text detection supports English, French, German, Italian, Portuguese, and Spanish. Handwriting recognition and queries are English only, and vertical text, as used in some Japanese and Chinese documents, is not supported.
You assemble the structure yourself. Textract returns blocks, key-value pairs, and tables. Turning them into your own schema, with your field names and types, is code you write and maintain. Queries help, but they are limited to 15 per page synchronously and 30 asynchronously. The approach behind schema-driven extraction is covered in DeepRead's guide to structured data extraction.
Separate operations for separate documents. Expense, ID, lending, and generic analysis are different operations, so a mixed document stream means routing logic before you extract anything. For invoice-heavy streams, DeepRead's guide to AI document processing for AP automation compares the invoice-specific options.
Cloud-only deployment. Textract is an AWS cloud service, so it does not fit teams that need on-premises or air-gapped processing.
Input restrictions. Password-protected PDFs and XFA-based PDFs are not supported.

None of these are flaws for every team. They are the points to test against your own documents.

What to Look for in an AWS Textract Alternative

Output shape: do you receive text and layout, or fields typed against a schema you define?
Pricing unit and total cost: per page, per document, per credit, or per feature, and what your real documents cost end to end.
Accuracy on your documents: not a vendor demo set. DeepRead's review of the most reliable data extraction service shows how to test this with a small pilot.
Uncertainty signals: confidence scores, review flags, or neither, so you can route only doubtful values to a person.
Traceability: whether each value comes with its page and location, so a reviewer can check it quickly.
Limits: pages and file size per document, requests per minute, and supported file types and languages.
Deployment and compliance: hosting region, retention, self-hosting, BAA, and certifications.

Types of Alternatives

Other cloud OCR services such as Google Document AI and Azure AI Document Intelligence offer similar building blocks, plus prebuilt parsers for invoices and receipts. They suit teams already on those clouds.
AI extraction APIs such as Reducto, Extend, LandingAI, Mistral OCR, and DeepRead take a document and, for most of them, a schema, and return structured data. They skip much of the post-processing Textract leaves to you. DeepRead's comparison of LLMs vs OCR software explains how these differ from classic OCR.
Specialized document platforms such as Nanonets add intake, validation, review, and posting to other systems around the extraction.
Open-source tools such as Tesseract give you full control and no per-page fee, at the cost of running, scaling, and maintaining the pipeline yourself. They suit clean documents and modest volumes.

Best AWS Textract Alternatives (2026)

Pricing and limits come from each vendor's own pages where noted, and anything not published is marked. Confirm current details before you buy.

DeepRead

What it automates: Replaces the combination of Textract operations with one API call. Extract reads the file into Markdown with layout blocks and bounding boxes and returns the fields you define as JSON, so you describe the output you want and skip assembling it from blocks, key-value pairs, and queries. Deep Extract targets harder documents: skewed scans, photos, and handwriting. It uses two OCR engines, adjudicates where they disagree, and verifies every value.
Pricing: $15 per 1,000 pages for Extract and $35 per 1,000 pages for Deep Extract, chosen per request, with no per-feature add-ons. Free covers 2,000 pages a month without a card, Standard has no monthly page limit, and Enterprise is custom. Credits are reserved when a job starts and charged on completion, so failed jobs cost nothing, and up to $1,000 in migration credits is available for teams moving over.
Limits: Free allows 50 pages per document, 15 MB per file, and 10 requests a minute. Standard allows 1,000 pages, 50 MB, and 100 requests a minute. Enterprise allows 2,000 pages and 500 MB and adds retention controls, PII redaction, bring-your-own model keys, EU and AU data residency, and SSO. Processing defaults to US East, and there is no self-hosted option and no BAA on any plan.
Best for: Teams that run Textract plus their own code to turn blocks into a schema, and want fields returned directly, with proof of where each came from.
Pros: Each field returns its page, bounding box, and source text, values inside tables are located individually, and uncertain values come back flagged with a reason and not silently. It does not return a numeric confidence score. A review UI is included on every plan, and there is a free tier for testing.
Cons: No self-hosting and no BAA, so it does not suit strict on-premises or PHI requirements. The pages reviewed describe schema-driven extraction, not prebuilt identity or lending operations like Textract's.

Google Document AI

What it automates: Enterprise Document OCR, a Form Parser, a Layout Parser, custom extractors, and prebuilt parsers such as an invoice parser, on Google Cloud.
Pricing: Enterprise Document OCR is listed at $1.50 per 1,000 pages with the first 1,000 free. Form Parser and custom extractors are $30 per 1,000 pages, and the invoice parser is $0.10 per count, where one count covers up to 10 pages. Failed requests are not billed.
Limits: Online processing accepts files up to 40 MB, and most processors allow 15 pages online. Batch accepts up to 1 GB per file and 5,000 files per request, and the invoice parser allows up to 200 pages per file in batch.
Best for: Teams on Google Cloud who want a close Textract equivalent, with prebuilt parsers.
Pros: Tiered public pricing, a dedicated invoice parser, and no charge for failed requests.
Cons: You still build the review step, storage, and workflow around it, and processor choice affects both price and limits.

Azure AI Document Intelligence

What it automates: Text reading, layout, prebuilt models for documents such as invoices, receipts, and IDs, custom extraction, and classification on Azure.
Pricing: Billed per 1,000 pages by model type, with a free tier of 500 pages a month. The pricing page I could read did not display the dollar rates, so use Microsoft's pricing calculator for current figures.
Limits: The free tier does not support premium features, according to the page's footnote, so check which features you need.
Best for: Teams on Azure or in governed Microsoft environments, especially those combining it with Azure OpenAI.
Pros: The layout model outputs Markdown, which pairs well with an LLM step, and there is a monthly free tier.
Cons: Custom models and the surrounding Azure services add complexity and cost, and it ties you to the Microsoft ecosystem.

Mistral OCR

What it automates: Document OCR through an API, returning text with layout, and optional structured annotations.
Pricing: Mistral has listed OCR 4.1 at $4 per 1,000 pages, or $5 per 1,000 annotated pages, and OCR 3 at $2 per 1,000 pages. I could not reopen Mistral's pricing page during the last check, so confirm these rates.
Limits: OCR 4.1 entered public preview in July 2026. The pages reviewed did not describe a self-hosted option. Check current file size and page limits in its documentation.
Best for: Teams that want low-cost text recognition with a modern model and will handle their own extraction logic.
Pros: Low per-page rates, plus paragraph-level bounding boxes and block-level confidence scores in OCR 4.1.
Cons: It is OCR first, so schema-level validation, review, and workflow are yours to build.

Reducto

What it automates: Parsing, schema-based extraction, splitting, and classification, with citations on extracted values. Deep Extract is an agentic mode that extracts, verifies against the source, and re-extracts until output meets a defined accuracy criterion.
Pricing: Extract is listed at $20 per 1,000 pages with parsing included, and Deep Extract at $40 per 1,000 pages. Parse alone is $10 per 1,000 pages, and batch processing is 20% off Parse and Extract. The Standard plan lists $150 in free usage, and up to $5,000 in credits is offered to teams moving from another processor.
Limits: The pricing page does not say whether Deep Extract includes parsing, and it does not mention confidence scores. A BAA, zero data retention, and EU and AU residency are on Growth, and VPC or on-premises deployment is on Enterprise.
Best for: Teams that want schema-based extraction with an agentic option for the hardest documents.
Pros: Flat per-page rates and citations on extracted values.
Cons: Deep Extract is priced at twice the Extract rate, so use it only where the hardest documents justify it.

Extend

What it automates: Parsing, extraction, splitting, classification, and a document fraud detection API, with workflows, schema tooling, and evaluation.
Pricing: Pay as you go includes 10,000 free credits, worth $100, then $0.01 per credit. Parse costs $5 per 1,000 pages in Light mode and $20 in Performance mode. Extract costs $7 in Light, $30 in Performance, and $50 in Operator-1 mode, with parsing billed separately. Growth and Enterprise are custom.
Limits: Growth adds HIPAA compliance with a BAA, and Enterprise adds BYOC or VPC deployment, SSO, and custom SLAs. On pay as you go, a BAA is an add-on.
Best for: Teams that want a full pipeline with evaluation tools and several accuracy and cost modes.
Pros: Several speed and accuracy modes and a multi-pass review agent that flags possible output errors, according to Extend.
Cons: Cost depends on the mode and separate parsing charges, so model your own pipeline before comparing.

Nanonets

What it automates: Document workflows that take in files from email, PDFs, scans, and supplier portals, extract and match data, and send low-confidence items to reviewers before posting to systems such as SAP and NetSuite.
Pricing: Charged by workflow block. Starter includes $50 in free credits, then $100 a month for 100 credits. Blocks cost $0.02 for simple operations, $0.10 for standard AI, and $0.30 for complex AI such as extraction. Nanonets estimates a typical invoice workflow at under $2 per document.
Limits: Starter is limited to 3 users. Classification, generative AI blocks, and ERP integrations begin on Growth.
Best for: Teams that want intake, extraction, review, and ERP posting in one product and not an API to assemble.
Pros: Several intake channels, built-in review routing, and published block rates.
Cons: Cost depends on how many blocks your workflow uses, so estimate a per-document cost first.

LandingAI Agentic Document Extraction

What it automates: Document parsing and schema-based extraction through its agentic document extraction APIs.
Pricing: The Explore plan is pay as you go at $0.01 per credit with 1,000 free credits that expire after 90 days. Team credit packs start at $250 a month, and Enterprise is custom. Credit use per page depends on the API and the content, so see its credit consumption documentation.
Limits: Zero data retention and a HIPAA BAA are available on Team and Enterprise but not on Explore, and VPC or on-premises deployment is Enterprise only.
Best for: Teams that want credit-based pricing with a compliance path for regulated data.
Pros: Published credit rates, a zero data retention option, and a BAA from the Team plan.
Cons: Per-page cost varies with page content, which makes budgeting harder than a flat rate.

Open-Source Option: Tesseract and Friends

If you have clean, mostly text-based documents and engineering time, open-source OCR such as Tesseract removes the per-page fee entirely. You trade that for the work of preprocessing, layout handling, scaling, and monitoring. It does not return fields against a schema, so you add extraction logic yourself. For anything with complex tables, handwriting, or mixed languages, expect to spend the savings on engineering.

Cost Comparison on 1,000 Pages

Prices are not like-for-like, because the services return different things. Use this only to see how the billing models differ at the list rates above.

Textract, tables plus queries: about $20.
Textract, forms plus tables plus queries: about $70.
Textract, expense analysis: about $10.
Mistral OCR 4.1, OCR only: $4.
DeepRead Extract: $15, or $35 for Deep Extract.
Reducto Extract: $20, or $40 for Deep Extract.
Extend Extract: $7 to $50 depending on mode, plus parsing.

Real cost depends on page length, how many pages need extra features, and how much human review follows. The cheapest line item is not always the cheapest workflow.

How to Migrate from AWS Textract

Collect a sample. Pick 100 to 200 real documents, including your worst scans and your most important document types.
List the fields you need. Write the schema you wish Textract to return, with field names and types.
Record the right answers. Have one person key the correct values and a second person check them.
Run two or three alternatives in parallel with Textract. Use free tiers where possible.
Score fields and tables separately. Count wrong, missing, and flagged values.
Compare total cost. Include per-page fees, features you would have to switch on, review time, and engineering effort.
Switch one document type first. Keep Textract running for the rest until results hold up.

Which Alternative Fits Your Situation

You are on Google Cloud or Azure and want a close equivalent: Google Document AI or Azure AI Document Intelligence.
You want fields returned against your own schema with proof of source: DeepRead, Reducto, Extend, or LandingAI.
You mainly need low-cost text recognition: Mistral OCR or Google's Enterprise Document OCR.
You want intake, review, and ERP posting included: Nanonets.
You need self-hosting, a BAA, or VPC deployment: check the higher tiers of Extend, Reducto, and LandingAI, which list a BAA on Growth or Team and VPC or on-premises options on Enterprise. DeepRead does not offer these.
Your documents are clean and your volume is modest: Tesseract or another open-source tool.

Conclusion

The right Textract alternative depends on what Textract is costing you. If it is the stacked per-feature bill and the work of turning blocks into fields, schema-driven extraction APIs are the closest fix. If it is cloud fit, Google Document AI and Azure offer the nearest equivalents. If it is the surrounding workflow, a platform such as Nanonets removes more of it. In every case, test on your own documents, compare cost per finished document and not per page, and keep Textract in place for the document types where it already works.

Frequently Asked Questions

What are the best AWS Textract alternatives?

The main options are Google Document AI, Azure AI Document Intelligence, Mistral OCR, Reducto, Extend, Nanonets, LandingAI, and DeepRead, plus open-source tools such as Tesseract. The best fit depends on whether you need plain OCR, schema-based extraction, or a full workflow.

Why is AWS Textract expensive for some teams?

Each analysis feature is billed separately. Forms, tables, and queries together are listed at $0.070 per page, or about $70 per 1,000 pages, so cost rises as you enable more features.

Is there a cheaper alternative to AWS Textract?

For plain text recognition, Mistral OCR 4.1 lists $4 per 1,000 pages and Google's Enterprise Document OCR lists $1.50 per 1,000 pages. For schema-based extraction, DeepRead lists $15 per 1,000 pages for Extract. Compare on your documents, since the services return different things.

What is the best open-source alternative to AWS Textract?

Tesseract is the most common open-source OCR engine. It has no per-page fee, but it does not return structured fields, so you build extraction and validation yourself.

Does AWS Textract support handwriting?

Handwriting recognition is English only, according to AWS's quota documentation. Test any alternative on your own handwritten documents, since results vary widely.

Can I process multi-page PDFs with AWS Textract?

Yes, with asynchronous jobs, which accept up to 3,000 pages and 500 MB. Synchronous calls accept only 1 page and 10 MB for PDFs and TIFFs.

Does DeepRead offer a BAA or self-hosting?

No. DeepRead processes and stores data in US East by default and has no self-hosted option or BAA on any plan. Enterprise adds EU and AU data residency, retention controls, and PII redaction.