The best document extraction APIs in 2026 are AWS Textract, Google Document AI, and Mindee for broad language and format support at scale. Reducto and Extend lead for LLM-ready output and complex document structures. For teams that want extraction without building a pipeline, Lido and Nanonets offer APIs backed by full workflow platforms. Your choice depends on whether you need raw extraction primitives or a complete processing stack.
A document extraction API takes a file (PDF, image, scan) and returns structured data. That sounds simple, but the differences between APIs are significant once you move past "Hello World" demos with clean invoices.
The dimensions that matter: accuracy on messy real-world documents, how the API handles tables and nested structures, pricing at volume, latency for synchronous workflows, and what the output format looks like. Some APIs return raw OCR coordinates. Others return semantically labeled fields. The gap between those two outputs is weeks of engineering work.
This comparison is written for developers and technical teams evaluating extraction APIs for production use, not proof-of-concept demos.
Best for: teams already on AWS who need scalable text and table extraction
AWS Textract offers three main capabilities: text detection (basic OCR), document analysis (tables and forms), and specialized "queries" where you ask natural-language questions about a document. The Queries feature is the most interesting. Instead of parsing raw output to find an invoice total, you ask "What is the total amount?" and Textract returns the answer with a confidence score.
Pricing is granular but gets confusing. Text detection runs $0.0015/page. Table extraction costs $0.015/page. Form extraction costs $0.05/page. Queries add another $0.015/page. Processing a single invoice through the full pipeline can cost $0.08/page, which adds up differently than competitors who quote a single per-page rate.
The biggest limitation: Textract's table extraction struggles with complex layouts. Tables without clear borders, tables that span multiple pages, and tables with merged cells often produce garbled output. If your documents have clean, grid-lined tables, Textract works well. If your tables are more free-form, expect to write post-processing code.
Integration with other AWS services (S3, Lambda, Step Functions) is smooth, which matters if your infrastructure is already there. The async API handles large batches well through SNS notifications.
Best for: multilingual documents and specialized industry processors
Google Document AI takes a different architectural approach than Textract. Instead of general-purpose endpoints, Google offers specialized "processors" for specific document types: invoices, receipts, bank statements, W-2s, driver's licenses, and more. Each processor is tuned for its document type, which means higher accuracy on supported formats but no coverage for unsupported ones.
The multilingual support is strong. Document AI handles 200+ languages and mixed-language documents out of the box, which matters for organizations processing documents across regions. The general OCR processor also includes quality scoring, telling you when a scan is too blurry or skewed to process reliably.
Pricing runs $0.01/page for the general processor, $0.10/page for specialized processors, and $0.03/page for custom-trained models. There's a free tier of 1,000 pages/month. Custom processor training requires a Google Cloud project and enough labeled training data (typically 100+ documents).
The main gap: Document AI is a collection of individual processors, not a unified extraction system. You need to classify documents first, route them to the right processor, and stitch the results together. That orchestration layer is your responsibility.
Best for: Microsoft-stack teams processing forms and structured documents
Azure Document Intelligence (formerly Form Recognizer) offers pre-built models for invoices, receipts, ID documents, and business cards, plus a custom model training interface. The Layout API does a solid job of preserving document structure, including reading order, section headings, and table relationships.
The Studio interface lets you label training data visually by drawing bounding boxes on uploaded documents. You need 5+ labeled samples per document type, which is a lower barrier than most custom training approaches. Trained models are versioned and deployable to specific Azure regions.
Pricing is $0.01/page for read (OCR), $0.05/page for pre-built models, and $0.05/page for custom models. The F0 free tier provides limited monthly page quotas for testing. Azure's regional deployment options matter for data residency requirements in regulated industries.
The downside: Azure's extraction output is verbose. The JSON responses include detailed bounding box coordinates, confidence scores per character, and nested span references. This is useful for building review interfaces but adds parsing complexity if you just want field values.
Best for: LLM pipelines that need structure-preserving document input
Reducto is the newer entrant that's gained traction with AI teams. Their core insight: most document extraction APIs were designed to output data for databases. Reducto is designed to output data for language models. The parsed output preserves document structure (headings, sections, tables, lists) in a format that LLMs can reason over.
The API offers Parse (structure-preserving conversion), Extract (schema-based field extraction), Split (multi-document separation), and Edit endpoints. Their "Agentic OCR" pipeline runs multiple passes over complex documents, using vision models to verify and correct extraction results. This multi-pass approach is slower but produces higher accuracy on documents where single-pass OCR fails.
Pricing starts at $0.015/page for parsing, with credits-based billing. They're YC-backed ($24.5M Series A), SOC2 and HIPAA compliant, and available on AWS Marketplace. The open-source RolmOCR model they released gives them developer community credibility that most enterprise vendors lack.
The tradeoff: Reducto is a parsing and extraction API, not a workflow platform. You get structured output, but you're responsible for validation, routing, and system integration.
Best for: developers who want drop-in SDKs with pre-trained models and fast integration
Mindee focuses on developer experience. Their SDKs (Python, Node, Ruby, PHP, Java, .NET) are genuinely well-designed, with typed responses and good error handling. You can go from signup to extracting invoice fields in under 30 minutes, which is faster than any cloud-provider API.
Pre-trained models cover invoices, receipts, bank statements, passports, driver's licenses, and US mail. Each model returns typed, named fields (not raw OCR text), so you get vendor_name as a string and total_amount as a float without post-processing. Custom model training is available through their docTR engine.
Pricing starts at $0.05/page with plans from 500 to 10,000 pages/month. The per-page rate doesn't vary by document complexity or model type, which makes cost estimation straightforward. Accuracy is above 90% on invoices, with 95%+ precision on most individual fields.
Mindee's limitation is scale. Their infrastructure handles mid-volume workloads well (thousands of pages/day), but they don't offer the same batch processing capabilities as AWS or Google for millions of pages. If you're processing at enterprise scale, you may outgrow them.
Best for: teams that need both an API and a visual workflow builder
Nanonets straddles the line between API and platform. The extraction API is solid, with pre-trained models for common document types and custom model training through annotated samples. But the real value is the workflow layer on top: you can build extraction, validation, approval, and export pipelines through a drag-and-drop interface, then call the whole workflow through the API.
This hybrid approach works for teams that want API-level control for integration but don't want to build validation and routing logic from scratch. The "Workflows" feature handles conditional logic (route invoices over $10K to a manager), data validation (flag line items that don't match PO quantities), and multi-system export.
Pricing starts at $0.30/page on the Starter plan. The Pro plan at $999/month/workflow includes higher throughput and priority support. For pure API usage without the workflow layer, the per-page cost is higher than raw extraction APIs like Textract or Mindee, but you're getting more than raw extraction.
Best for: teams that want extraction with built-in validation and spreadsheet-based output
Lido's API reflects its template-free extraction approach. You send a document, and the API returns named fields without requiring templates, training data, or schema definitions. The system identifies the document type and extracts relevant fields automatically.
Where Lido's API differs from pure extraction APIs: the output feeds into Lido's spreadsheet workspace, where you can build validation rules, approval workflows, and export integrations without additional code. For teams that want to process documents into a structured workflow rather than just extract raw data, this eliminates the pipeline-building work.
Pricing is roughly $0.10/page with no per-seat licensing. The API is REST-based with JSON responses. The tradeoff is flexibility: if you need fine-grained control over extraction parameters, custom model training, or raw OCR coordinates, the cloud-provider APIs give you more knobs to turn.
Best for: teams that want multiple processing modes and automatic accuracy optimization
Extend offers Parse, Extract, Split, Classify, and Edit APIs through a credits-based model. The distinguishing feature is processing modes: you can choose low-latency (for real-time use cases), cost-optimized (for batch jobs), or maximum-accuracy (when precision matters). This flexibility lets you balance speed and cost per document type.
Their "Composer" optimization agent runs in the background, automatically refining your extraction schemas and improving accuracy over time. The agentic OCR pipeline uses intelligent routing through vision AI and custom-trained vision language models, targeting 95-99%+ accuracy on complex documents.
Credits-based pricing means per-page costs vary by operation type and processing mode. Higher-tier plans get lower per-credit rates. The pricing structure is more complex than flat per-page models, but it gives you more control over the cost-accuracy tradeoff.
Run this test before committing to any API:
Collect 50 of your actual production documents. Include the messy ones: scanned faxes, photos taken at angles, multi-page documents with inconsistent formatting. Send the same 50 documents through every API you're evaluating. Compare the results field by field against your ground truth.
Pay attention to tables. Table extraction is where APIs diverge most. A single misaligned column can corrupt every row of data. Test multi-page tables specifically, as most APIs struggle with table continuation across page breaks.
Check error handling. What happens when the API can't extract a field? Some return null. Some return their best guess with a low confidence score. Some return nothing and swallow the error. Your downstream pipeline needs to handle all these cases.
Finally, calculate real costs. Per-page pricing is misleading for multi-page documents. A 15-page contract processed through Textract's full pipeline costs $1.20 per document, not the $0.05 you might assume from the per-page form extraction rate. Model your actual document mix before committing.
AWS Textract offers the lowest per-page rate at $0.0015 for basic text detection. For structured extraction with tables and forms, Reducto starts at $0.015/page. However, the cheapest API depends on what you need extracted. Basic OCR is cheap everywhere. Semantic field extraction with named outputs costs more but saves engineering time.
You can, but it gets expensive at volume. General-purpose LLMs charge by token, so a 10-page document might cost $0.50-$2.00 per extraction. Dedicated extraction APIs process the same document for $0.05-$0.15. LLMs also lack built-in table parsing and can hallucinate field values. Use dedicated APIs for production extraction and LLMs for ad-hoc analysis.
Accuracy varies by document type. For standard invoices, most APIs achieve 95%+ accuracy. For complex documents (multi-page bank statements, handwritten forms, scanned faxes), Reducto and Google Document AI tend to perform best due to their multi-pass and specialized processor approaches. Always test with your actual documents rather than relying on vendor benchmarks.
Not necessarily. Pre-trained models handle common document types (invoices, receipts, IDs) well enough for most use cases. Custom training helps when you process specialized documents like industry-specific forms or proprietary templates. Template-free APIs like Lido skip training entirely by using AI to understand document semantics on the fly.
Table extraction quality varies significantly. AWS Textract and Google Document AI identify rows and columns but struggle with borderless tables and merged cells. Reducto preserves table structure for LLM consumption. Mindee returns typed field values from within tables. Test table extraction specifically with your documents, as it is where most APIs fall short.
OCR converts images of text into machine-readable characters. Document extraction goes further by identifying specific fields (invoice number, total, vendor name), understanding table structures, and returning semantically labeled data. Most extraction APIs include OCR as a first step, then apply AI to interpret the recognized text.
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