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Reducto vs AWS Textract
Textract is AWS's OCR primitive, solid on clean forms and simple text. Reducto is the agentic document platform for the documents that break OCR — and it runs anywhere, including your own AWS VPC.
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At a glance
How Reducto and AWS Textract compare
Textract wins on AWS-native integration and raw OCR pricing at low volume. Reducto wins on accuracy for complex documents, platform breadth, and output built for LLM pipelines.
| Dimension | Reducto | AWS Textract |
|---|---|---|
| Category | Full platform: parse, extract, split, classify, and edit in one API. | Cloud OCR primitive with fixed feature types (text, forms, tables, queries). |
| Parsing accuracy | Yes: Up to 99–100% zero-shot accuracy on complex documents. | Partial: Reliable on single-column text; degrades on multi-column and irregular layouts. |
| Table extraction | Yes: 0.90 on RD-TableBench; merged cells, multi-level headers, borderless tables. | Partial: Solid on simple grids; degrades on merged cells and irregular structure. |
| Figures, checkboxes & handwriting | Yes: Charts to structured tables; checkbox detection; handwriting in standard pipeline. | Partial: Figures unstructured; checkbox and handwriting are documented weak points. |
| Multilingual support | Yes: 100+ languages with automatic detection, including mixed-language documents. | Partial: Small set of mostly Latin-script languages; handwriting is English-only. |
| Extraction & citations | Yes: Per-field citations and bounding boxes; Deep Extract 99.6% precision and recall. | Partial: Block-level geometry only; no first-class extraction citations. |
| Enterprise deployment | Yes: SOC 2 Type II, HIPAA, zero data retention; VPC to air-gapped. | Yes: AWS-native with SOC 2, HIPAA, FedRAMP under AWS compliance. |
| Pricing | From $0.015/page pay-as-you-go; 15,000 free credits. | Cheap raw OCR; forms, tables, and queries priced per feature. |
Parse one of your hardest documents in Studio and compare the output side by side.
The comparison in depth
Where the differences actually show up
- Accuracy on complex documents, measured
- Textract is dependable on clean, single-column, printed English text. But real document sets include multi-column layouts, dense tables, scans, handwriting, and checkboxes: exactly where Textract's accuracy is a documented weak point. Reducto's multi-pass pipeline scores 0.90 on RD-TableBench and reaches up to 99–100% zero-shot accuracy on complex real-world documents, and in micro1's independent benchmark of 225 human-validated documents, Reducto Deep Extract achieved 100% coverage with 99.6% precision and recall and zero failures. Benchmarks are a starting point, not a verdict: the numbers that matter are the ones on your own documents, which is why we encourage head-to-head evals.
- Fixed feature types vs orchestrated models
- Textract exposes fixed modes (text detection, forms, tables, queries), and you pick the right one per call, manage async jobs, and reconcile the outputs yourself. Reducto instead orchestrates 12+ models behind one API, balancing accuracy, latency, and throughput per document: layout-aware vision models segment the page, and VLMs review and correct the output in context. You send the document; the platform decides how to read it. That matters because production document sets are heterogeneous: a pipeline hard-wired to feature modes breaks on the documents that don't fit them.
- Beyond printed English text
- Textract's documented gaps cluster around everything that isn't clean printed English: figures are returned as unstructured regions, checkbox detection is inconsistent across form styles, cursive handwriting is unreliable, and language support is limited to a small set of mostly Latin-script languages. Reducto converts charts to structured tabular data, extracts checkbox state with spatial positions, handles mixed handwritten and printed text in the standard pipeline, and reads 100+ languages with automatic detection. If your corpus includes scanned forms, figures, or non-English content, this is the gap that forces workarounds.
- Output built for LLM pipelines
- Textract returns blocks and geometry designed for programmatic OCR consumers. Turning that into clean, ordered, LLM-ready input means writing and maintaining significant post-processing code. Reducto returns structured JSON with reading order, block types, and table structure, and every extracted field carries a bounding-box citation you can surface for compliance review or human verification in Studio. For teams feeding RAG systems or agents, the post-processing you don't write is where most of the savings live.
- One platform vs assembled services
- Textract does OCR and structured extraction; classification, splitting, document editing, and workflow orchestration mean composing additional AWS services with custom glue. Reducto ships the complete toolkit (Parse, Extract, Split, Classify, and Edit) in a single API across 30+ file types, plus an MCP server, CLI, Python/Node.js/Go SDKs, and Studio for visual pipelines and citation review. Teams at Harvey, Scale AI, and Vanta run it in production, and the platform has processed 4B+ pages.
- Deployment for AWS-committed teams
- Textract's strongest card is procurement: it lives inside your existing AWS agreement, IAM, and S3 workflows, with SOC 2, HIPAA, and FedRAMP under the AWS compliance umbrella. But choosing Reducto doesn't mean leaving AWS: Reducto deploys hosted, in your own AWS VPC, on-prem, or fully air-gapped, with SOC 2 Type II, HIPAA (BAA available), and zero data retention. Your documents can stay inside your AWS boundary while the accuracy layer improves.
- Migrating from Textract
- Most teams migrate by replacing the Textract call (and usually the mode selection, async job polling, and block-reassembly code around it) with a single Reducto parse call returning structured JSON. The docs cover Python, Node.js, and Go SDKs, and because Extract, Split, and Classify live in the same API, migrations tend to delete post-processing code rather than port it. Our engineers run migration evals with you on your own documents.
Which fits your team
Who should pick which
Different tools fit different stages. Here's the honest split.
Choose Reducto if…
- Your documents include complex layouts, dense tables, scans, handwriting, checkboxes, or figures: the cases where OCR-first services degrade.
- You're feeding LLM pipelines and need structured, citation-backed output rather than raw blocks that require heavy post-processing.
- Your corpus is multilingual; Reducto reads 100+ languages with automatic detection.
- Your workflow extends beyond OCR into classification, splitting, extraction with citations, or writing data back into documents.
- You want document processing inside your own AWS VPC (or on-prem/air-gapped) without assembling and maintaining a multi-service pipeline.
AWS Textract may be a fit if…
- Your documents are mostly clean, single-column, printed English text and raw OCR at low volume is all you need.
- You're deeply committed to AWS: existing enterprise agreements, credits, IAM, and S3 workflows make a native service the path of least resistance.
- You need FedRAMP authorization within the AWS compliance framework for government workloads.
- You have the engineering capacity to own mode selection, async job management, and post-processing as part of a custom AWS pipeline.
FAQ
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