The best AWS Textract alternatives in 2026 are anyformat, Google Document AI, Azure Document Intelligence, ABBYY, Nanonets, Reducto, LlamaParse and Unstructured. Teams leave Textract for four reasons: it returns raw OCR rather than fields in your schema, it ships no workflow, review or evaluation layer, it runs only inside AWS regions under US jurisdiction, and its pricing is split across per-page APIs that are hard to forecast. Two of the eight, anyformat and Reducto, can be deployed on-premise in an air-gapped environment; if that is the constraint driving your search, start there.
To be clear about what Textract does well: it is a mature OCR service with strong table detection, dedicated APIs for forms, expenses and identity documents, and native integration with S3, Lambda and Step Functions. If your documents are structured forms and your stack is fully AWS, it remains a serious option.
Method. We evaluated each tool in July 2026 on five criteria: custom field extraction without training data, on-premise deployment, European data sovereignty, workflow orchestration, and pricing model. Facts come from vendor documentation, third-party benchmarks, and our own benchmark on 1,000+ real documents.
Comparison at a glance
| Tool | Best for | Custom fields without training | On-premise | EU sovereignty | Pricing model |
|---|---|---|---|---|---|
| anyformat | Custom schemas on complex documents, with accuracy you can measure | Yes (zero-shot) | Yes, incl. air-gapped | EU-native | Per page, per operator ($25/1,000 pages parsing) |
| Google Document AI | GCP-native pipelines, 200+ languages | Yes (Custom Extractor foundation model) | No | US jurisdiction, GCP regions | Per page, per processor |
| Azure Document Intelligence | Microsoft-stack teams | No (5+ labeled docs + training) | Partial (containers) | US jurisdiction, Azure regions | Per page, per model |
| ABBYY | Large IDP deployments with stable document types | No (Skills + training) | Yes (FlexiCapture) | US-headquartered | License + services |
| Nanonets | No-code automation for mid-market teams | Partial (HITL annotation) | Enterprise contract only | US-based multi-tenant | Per block run ($0.02–$0.30) |
| Reducto | Engineering teams building custom pipelines | Yes (code-defined schemas) | Yes (VPC, air-gapped) | US-based, EU endpoints | Flat $10 / 1,000 pages for parsing (r-1, Sep 2026); extraction and add-ons priced separately |
| LlamaParse | Parsing documents into LLM-ready Markdown | Partial (parsing-first) | Enterprise self-hosted | US-based cloud | Per page (credits); +10 credits per page containing a form |
| Unstructured | RAG chunking, 71+ connectors | No (element arrays) | Yes (self-hosted) | US-based | API + self-hosted |
1. anyformat
If you're looking for an AWS Textract alternative that gives you custom-schema extraction with no training data, a workflow and evaluation layer built in rather than assembled from Lambda and Step Functions, and EU-native jurisdiction instead of a US-governed AWS region, anyformat is built for exactly that gap. It's a European document automation platform that covers the whole pipeline a Textract replacement needs: extraction from a schema with no training data, workflow orchestration, human review, and accuracy evaluation, in one product. You define the fields you want and extraction works on the first document.
In anyformat's published July 2026 benchmark on 1,000+ real documents, anyformat scored 78.1% on parsing quality at $25 per 1,000 pages; Textract scored 65.4% on the same set.
Every extracted value returns a calibrated confidence score together with the evidence it was read from, the review interface highlights that evidence on the page, and a confidence threshold routes uncertain fields to a person while the rest go straight through. Deployment covers cloud, private cloud, on-premise and fully air-gapped environments, and ISO 27001:2022 certification, GDPR tooling and zero-retention processing are included on every plan with no surcharge.
If you need to know whether accuracy is holding up over time, anyformat ships that built in: a dataset of verified ground truth, versioned evaluations that score any workflow change field by field against it, and live production accuracy, confidence and through-rate side by side. Optimizer then improves a workflow's field descriptions automatically against that same dataset. On Textract, that measurement harness is something your team builds and maintains.
Pricing is credit-based, charged per page and per operator: Parse 25 credits per page, Extract 35, Classify 10, Split 25, with agentic variants at 100 and 150; on the Business plan 10 credits cost €0.01, so the $25 per 1,000 pages above is the parsing rate. The free tier includes 50,000 credits with no credit card.
Best for: teams that need custom fields on complex documents with accuracy they can measure, and a review step their operations team can run without code.
2. Google Document AI
Google Document AI is GCP's document processing service, a natural fit for teams already on Google Cloud with standard document types. Its Enterprise Document OCR supports 200+ languages with handwriting recognition in 50, per Google's documentation, and it integrates tightly with BigQuery and Vertex AI. Custom fields no longer require labeling to get started: the Custom Extractor runs on a foundation model that extracts from a schema alone, with few-shot accepting up to 5 examples and fine-tuning requiring 50 training plus 50 test documents to reach production accuracy. The constraints are elsewhere: system limits cap many online processing requests at 15 pages, deployed custom processor versions carry hosting fees, and there is no on-premise option. In our parsing benchmark it scored 55.0%, with plain OCR at $1.50 per 1,000 pages, the lowest cost among the cloud providers we tested. See the full anyformat vs Google Document AI comparison.
Best for: GCP-native teams whose documents fit the online page limits and who want custom fields without a labeling project.
3. Azure Document Intelligence
Azure Document Intelligence, formerly Form Recognizer, is Microsoft's document processing service and the default path for organizations on the Azure stack. Its pretrained models for invoices, receipts, IDs and tax forms are among the strongest cloud offerings: it scored 69.9% in our parsing benchmark, ahead of both Textract (65.4%) and Google Document AI (55.0%). Custom fields require labeled documents in Document Studio, five examples minimum per Microsoft's documentation and usually more, plus a training cycle; container deployment covers only a subset of features, and configuration spreads across Document Studio, the Azure portal and API code. See the full anyformat vs Azure Document Intelligence comparison.
Best for: Microsoft-stack teams whose documents match Azure's pretrained models.
4. ABBYY
ABBYY is the longest-standing enterprise IDP vendor on this list, founded in 1989 and aimed at large organizations with stable, high-volume document processes. Its catalog lists 150+ pre-built extraction Skills across two products: Vantage (cloud) and FlexiCapture, one of the most mature on-premise capture systems available. The trade-off is the delivery model: implementations typically run weeks to months with third-party integrators, professional services budgets range from $15K to $200K+, and contracts often require 3-year terms with annual prepayment. Documents outside the Skill library need custom model training. See the full anyformat vs ABBYY comparison.
Best for: Enterprises with stable document portfolios, a hard on-premise requirement, and budget for a traditional deployment.
5. Nanonets
Nanonets is a mid-market document automation platform, founded in 2017, aimed at teams that want no-code workflows without an engineering project. Its block-based workflow builder (import, data-action and export blocks) is well designed for non-technical users, and published pricing runs $0.02 to $0.30 per block run, with Nanonets' own pricing page putting a typical invoice workflow at four to six block runs per document. Nanonets' documentation notes that out-of-box models may require human-in-the-loop annotation to reach production accuracy on non-standard documents. Default deployment is US-based multi-tenant cloud, on-premise is available only via enterprise contract, and data may be retained for around 30 days after termination. See the full anyformat vs Nanonets comparison.
Best for: Mid-market teams automating standard document types at moderate volume, without strict EU sovereignty requirements.
6. Reducto
Reducto is a US-based document parsing API, founded around 2023, built for engineering teams that want a high-accuracy parsing primitive with full code control. It has raised $108M in total, most recently a $75M Series B led by a16z in October 2025; it publishes the open RD-TableBench table benchmark and offers Parse, Split, Extract and Edit endpoints with cloud, VPC and on-premise deployment including air-gapped environments. Extraction schemas need no training data, and since 2026 its Studio adds a visual pipeline builder, auto-generated schemas and evaluations on its Growth tier. What it still lacks is a human review queue with reviewer roles and per-field verification status, so the review step is yours to build. Since September 2026 its r-1 model parses at a flat $10 per 1,000 pages. It holds SOC 2 Type II and offers HIPAA processing with a BAA on higher tiers, but does not list ISO 27001, a frequent blocker in European procurement. See the full anyformat vs Reducto comparison.
Best for: Engineering teams replacing Textract's parsing layer inside a custom-built pipeline.
7. LlamaParse
LlamaParse is LlamaIndex's document parsing API, launched in February 2024, built to turn documents into LLM-ready Markdown for RAG pipelines. Speed is its standout trait: an independent Procycons benchmark measured around 6 seconds for both 1-page and 50-page documents in batch mode. The limits show up in structured extraction: Markdown output flattens merged cells and nested table relationships; confidence scores left beta in August 2026 and are documented as calibrated on three of its five parsing tiers, self-reported without a published evaluation, with no review queue to act on them; the standard offering is US cloud-only with enterprise self-hosting via Kubernetes. It holds SOC 2 Type 2 but does not list ISO 27001. See the full anyformat vs LlamaParse comparison.
Best for: Fast RAG ingestion inside the LlamaIndex ecosystem, not field-level extraction into business systems.
8. Unstructured
Unstructured is a partially open-source parsing platform that chunks documents into element arrays for RAG pipelines, aimed at AI teams building retrieval systems. It has the widest connector ecosystem in the category, with 71+ connectors including Databricks, Elasticsearch, S3 and Google Drive, and its commercial platform holds SOC 2 Type II, ISO 27001 and HIPAA certifications (the open-source library is not in scope) with a self-hosted deployment option. The key caveat: it does not do field-level structured extraction, so if you need invoice totals or contract dates in your ERP, it solves a different problem. The Procycons 2025 benchmark also measured 51 seconds to process a single page, versus about 6 for the fastest alternatives. See the full anyformat vs Unstructured comparison.
Best for: RAG preparation with maximum connector coverage; complementary to, not a replacement for, an extraction platform.
How to choose the right one for your needs
- If your whole stack is on AWS and your documents are structured forms, Textract remains viable: the switching cost may not be worth it yet.
- Count the full pipeline cost, not just the page price: post-processing, validation, routing, a review UI and monitoring all cost engineering time to build if the vendor doesn't ship them.
- If you need custom fields without training data, anyformat (schemas defined in Studio), Reducto (code-defined) and Google's Custom Extractor all work from a schema alone.
- If you need to measure accuracy over time, look for a vendor with a dataset-backed evaluation tool, not a one-off benchmark number.
- If documents can't leave your infrastructure, anyformat, Reducto, ABBYY FlexiCapture, or a self-hosted Unstructured deployment all support that.
- If EU governing law is a hard requirement, only an EU-headquartered vendor actually changes the jurisdiction. An EU region on a US company doesn't.
- If your ops team needs to own the workflow without engineering, look for a visual builder (anyformat Studio, Nanonets' block editor).
- If you're building for RAG, LlamaParse or Unstructured are built specifically for that output shape.
Frequently asked questions
What is the best alternative to AWS Textract?
It depends on the constraint driving the switch. For an end-to-end platform with zero-shot extraction, workflow orchestration, human review and built-in evaluation, anyformat is the most complete option. For a code-first parsing API, Reducto is the closest fit. Teams consolidated on Google Cloud or Azure usually evaluate their own provider's document service first.
What is the best European alternative to AWS Textract?
anyformat is the only EU-native platform on this list: European governance, ISO 27001 certification covering the processing pipeline, zero-retention processing, and on-premise deployment including air-gapped environments. US providers offer EU regions, but region selection changes where data sits, not the jurisdiction that governs it.
Is there an open-source alternative to AWS Textract?
Partially. Unstructured has an open-source core, and self-hosted engines like Docling and PaddleOCR are free to run. In our parsing benchmark both scored below Textract (52.3% and 43.2% versus 65.4%), so budget for accuracy validation and the engineering effort to operate them in production.
Why do teams switch away from AWS Textract?
Four reasons come up consistently: Textract returns raw OCR that needs a custom post-processing pipeline before applications can use it; it has no workflow or evaluation layer, so orchestration and accuracy monitoring mean assembling Lambda, Step Functions, queues and your own measurement harness; pricing splits across per-page APIs, with combined forms and tables analysis at $65 per 1,000 pages and $70 with queries added (AWS pricing, 2026); and the service is governed under US jurisdiction regardless of region selection.
Is there an on-premise or self-hosted alternative to AWS Textract?
Yes: anyformat and Reducto both offer on-premise deployment including air-gapped environments, ABBYY FlexiCapture is a mature on-premise capture system, and Unstructured can be self-hosted, while Textract itself runs only inside AWS regions.
Which AWS Textract alternative works without training data?
anyformat, Reducto and Google's Custom Extractor extract from a schema alone, while Azure Document Intelligence, ABBYY and Nanonets still require labeled example documents.
What does AWS Textract cost compared with the alternatives?
Textract's forms and tables analysis lists at $65 per 1,000 pages; among the alternatives, Reducto parses at a flat $10 per 1,000 pages, anyformat parses at $25 per 1,000 pages with extraction and classification priced separately per operator, and Google's plain OCR lists at $1.50 per 1,000 pages.
Does an EU region make AWS Textract GDPR-compliant?
An EU region can support GDPR compliance through in-region storage and standard contractual clauses, but it does not change which country's law governs the provider: a US company processing data in an EU region remains subject to US law regardless of where the servers sit. Buyers whose requirement is EU governing jurisdiction, not just GDPR compliance on paper, need an EU-headquartered vendor.
If you have narrowed the decision down to two options, start with the deep dive: anyformat vs AWS Textract compares extraction approach, workflow orchestration, sovereignty and pricing side by side.
