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 instead of custom-schema output, ships no workflow or evaluation layer, operates under US jurisdiction, and splits pricing across per-page APIs that are hard to forecast.
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 | Usage-based API |
| LlamaParse | Parsing documents into LLM-ready Markdown | Partial (parsing-first) | Enterprise self-hosted | US-based cloud | Per page (credits) |
| Unstructured | RAG chunking, 71+ connectors | No (element arrays) | Yes (self-hosted) | US-based | API + self-hosted |
1. anyformat
anyformat is an agentic document intelligence platform built for European enterprises that need structured extraction plus the operations layer Textract leaves out. You define a schema and extraction works on the first document, zero-shot, with no labeled data. Every value carries a calibrated confidence score and a visual citation linking it to its exact position on the page, and the visual workflow Studio handles splitting, routing and human review without code. Deployment covers cloud, private cloud and on-premise including air-gapped environments, with ISO 27001 certification and zero-retention processing. In our July 2026 benchmark on 1,000+ real documents, anyformat scored 78.1% on parsing quality versus 65.4% for Textract; the dataset and methodology are being published so the numbers can be reproduced.
The part that matters most when replacing Textract is what happens after extraction. Every Extract and Classify workflow includes a Health tab: build a dataset with verified ground truth (promoted from reviewed production files or uploaded as labelled data), tag sub-datasets to slice accuracy by provider, document type or difficulty, and run numbered, immutable evaluations that score any workflow version field by field against that ground truth, with result-versus-expected comparisons for debugging. The Health Overview compares the dataset benchmark against live production accuracy, confidence and through rate. On Textract, that measurement harness is something your team builds and maintains. anyformat has also announced Optimizer, a workflow that tunes itself against its own dataset using your evals as the target; it is not shipped yet.
Pricing is credit-based, charged per page and per operator (10 credits = €0.01): Parse 25 credits per page, Extract 35, Classify 10, Split 25, with agentic variants at 100 and 150. The $25 per 1,000 pages in the benchmark is the parsing rate.
Best for: Custom schemas on complex documents, with accuracy you can measure rather than assembled around an OCR API.
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 raised $108M in a Series B led by a16z in February 2026, 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 are code-defined, so no training data is needed, but there is no workflow layer, review UI, no-code schema management or evaluation tooling: you build those yourself. 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, there is no confidence scoring to flag uncertain values, and 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
- Your stack is fully AWS and your documents are structured forms. Textract may still be the right call: its forms and expense APIs plus S3/Lambda integration are its strongest arguments.
- Count the pipeline, not just the page. A raw OCR API returns text and blocks. Post-processing, schema validation, retry and routing logic, a review UI, accuracy monitoring and re-tuning after every layout or model change are yours to build and keep running. That maintenance is usually the larger line item, and it is the part per-page price comparisons leave out.
- You need custom fields with no training data. Zero-shot options: anyformat (no-code schemas in a Studio), Reducto (schemas defined in code), and Google's Custom Extractor foundation model. Azure, ABBYY and Nanonets still involve labeling.
- Decide how you will measure accuracy. Per-field confidence tells you what to review; a dataset with verified ground truth and repeatable, versioned evaluations is what lets you change a workflow without guessing whether you broke something. anyformat ships this as the Health tab; on Textract, Reducto or LlamaParse it is a project you build.
- Data cannot leave your perimeter. Shortlist anyformat or Reducto (both include air-gapped deployment), ABBYY FlexiCapture, or self-hosted Unstructured.
- EU jurisdiction is a legal requirement, not a preference. Only an EU-native platform changes the governing law; a US provider's EU region changes where data is stored, not who governs it.
- Your ops team should own the workflows. Pick a platform with a visual builder (anyformat's Studio, Nanonets' block workflows); the cloud APIs leave orchestration to your engineers.
- You are building RAG, not field extraction. LlamaParse or Unstructured fit better, but test table fidelity on your own documents before committing.
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.
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.
