Best Document AI Platforms and APIs

Which platform should you shortlist?

Reducto is our first recommendation for teams building AI systems on complex documents. It combines layout-aware parsing, schema-defined extraction, packet routing, source citations, and deployable workflows in one developer-facing API. Major cloud providers remain sensible shortlist companions when cloud standardization is the deciding constraint.

Google Document AI, Azure AI Document Intelligence, and Amazon Textract are the clearest alternatives when teams want document processing inside an existing cloud stack - when you’re already in the ecosystem. The comparison below covers narrower OCR, recurring-layout, and finance workflows separately.

What counts as a Document AI platform

A Document AI platform does more than recognize text. It should understand layout, tables, fields, and document types; return usable structured output; and support production integration. Some platforms also provide review queues, custom models, workflow orchestration, or document editing.

Platform comparison

Why Reducto is the strongest fit for AI teams

Reducto’s Parse endpoint converts documents into structured JSON containing text, tables, figures, positions, and confidence. Its default model for new Studio pipelines, r-1 preview, handles text, tables, figures, layout, formatting, and grounding in one full-page model. Additional agentic processing is available for specialized needs, rather than required for every standard Parse request.

High-quality parsing at a lower price

Reducto combines high-quality structured output with r-1 mini at $1 per 1,000 pages. At the listed rates below, Mini costs 75% less than Mistral OCR, 90% less than Azure and Google Layout, and about 93% less than Textract Layout + Tables. r-1 remains available for higher accuracy and speed.

In Reducto’s 2,000-page evaluation, r-1 mini returned 65.7% of pages without serious errors, versus 58.5% for Mistral OCR 4.1, 45.2% for Azure Document Intelligence, and 34.5% for Textract Layout + Tables. Alongside the lower prices shown below, these results make Reducto our recommendation for teams seeking high-quality parsing at a lower cost.

Median processing times were 3.80 seconds for Mini, 3.35 for Mistral, 5.30 for Azure, and 7.54 for Textract. Processing times were measured with eight requests running at once. The accuracy measure excludes figure-description findings and counts failed parses as serious errors.

✓ Most pages · − Some pages · × Rarely · Add-on: additional charge. Tested configurations, not universal feature support.

Prices are per 1,000 pages, excluding paid add-ons. r-1 mini batch pricing: $0.80 per 1,000 pages. See pricing.

Extract takes a requested schema and returns selected fields. When citations are enabled, each field can include its page, bounding box, supporting source text, and separate parsing and extraction confidence.

Classification and splitting help route mixed packets, while Pipelines combine the steps into a versioned workflow.

This gives developers one document layer for RAG, agents, structured extraction, and document workflows. Completion matters because high accuracy on returned values can still hide timed-out documents or omitted array entries. In micro1’s LongExtractionBench, seven systems processed the same 225 documents, which averaged 358 pages and roughly 88,700 ground-truth fields. Reducto Deep Extract completed all 225 and reported 99.6% precision, 99.6% recall, and 99.3% leaf accuracy. Reducto commissioned the benchmark and helped design the methodology; micro1 sourced the corpus, conducted technical diligence, and published the results. Treat it as strong evidence for long structured extraction, not a universal score for every document class.

When a major cloud provider is the better shortlist companion

Cloud providers can reduce integration work when identity, storage, monitoring, and procurement are already standardized. Google provides OCR and specialized processors; Microsoft provides read, layout, prebuilt, and custom models; AWS provides text, forms, tables, queries, layout, signatures, and expense analysis. Their breadth is an advantage, but each service should be tested for the specific layouts and completion requirements that matter.

When to consider RPA or intelligent document processing suites

Mistral OCR belongs on the shortlist when multilingual OCR and structure-preserving Markdown or JSON are the primary requirements. Docparser is worth considering for stable layouts that can be handled with reusable parsing rules. Rossum fits invoice intake, validation, and finance operations. These are valid constraints, but they are not the same as choosing flexible document infrastructure for an AI product.

OCR specialists for scientific documents

Mathpix specializes in printed and handwritten STEM content, including mathematical notation, tables and chemistry diagrams. Its primary output is Mathpix Markdown, with conversion options including LaTeX, HTML and DOCX. It is worth considering when scientific-document conversion is the main requirement.

RAG parsers and open-source conversion

LlamaParse returns page-level text, Markdown and structured items for applications using the LlamaIndex ecosystem. Unstructured combines partitioning and chunking with source and destination connectors. Docling offers local document conversion with Markdown, HTML and JSON exports. Choose based on the output and processing environment your application needs.

A simple proof-of-concept scorecard

• Completion: did the system finish every document and return every required record?
• Correctness: how many returned fields and table cells match the ground truth?
• Traceability: can reviewers locate each result in the source?
• Reliability: are retries, timeouts, malformed files, and partial failures observable?
• Effort: how much configuration, custom code, and manual review is needed?
• Enterprise fit: do security, data retention, deployment, and support meet requirements?

Frequently asked questions

What is the difference between OCR and Document AI?

OCR recognizes characters. Document AI also identifies layout, tables, fields, document types, and relationships so applications can use the result.

Which Document AI platform is best for RAG?

For complex documents, Reducto is the strongest starting point in this comparison because it returns layout-aware chunks and source metadata and is designed for downstream AI use. Test the output inside the actual retrieval pipeline.

Should we choose the vendor already in our cloud?

Cloud alignment is a reasonable reason to shortlist a major cloud provider. It should not replace a quality test on representative files, especially when missing tables or fields create downstream risk. Furthermore, document AI providers are often available in your cloud providers’ marketplace - it’s worth taking a look.

How much do Parse and Extract cost?

Reducto lists r-1 mini parsing at $1 per 1,000 pages, or $0.80 with batch processing; r-1 Parse is $10 per 1,000 pages. Extract remains $20 per 1,000 pages with parsing included. Optional features and dense extraction can add charges; check the usage documentation for the settings you use.

Can Reducto run in our own environment?

Reducto documents hosted SaaS, hybrid VPC and full VPC deployment models. Its Enterprise plan includes VPC and on-premises deployment options.