DocumentIQ vs AWS Textract
AWS Textract is a machine learning service from Amazon that automatically extracts text, handwriting, tables, and forms from scanned documents, deeply integrated with the AWS ecosystem.
Feature-by-feature comparison
| Feature | DocumentIQ | AWS Textract |
|---|---|---|
| Extraction approach | LLM-powered semantic extraction. The model reads and understands document context, not just character shapes. | ML-based OCR that detects text regions, tables, and form key-value pairs. Strong at character recognition, less at meaning. |
| Custom fields | Define unlimited custom fields with names, types, descriptions, and natural language extraction prompts. Fully configurable per project. | Extracts generic key-value pairs and tables. Custom queries available via Textract Queries but limited to predefined question formats. |
| Line items | Custom field definitions handle complex line items. LLM interprets context, merges split rows, and handles multi-line entries. | Table extraction detects rows and columns structurally. Works well for clean tables but struggles with merged cells or irregular layouts. |
| Context understanding | LLMs understand document semantics — they can infer values from surrounding text, resolve abbreviations, and follow cross-references. | Extracts what it sees on the page. Limited contextual understanding — cannot infer meaning from surrounding paragraphs. |
| Annotation & training | Annotate PDFs visually to create few-shot examples. No model training needed — examples are injected into LLM prompts. | No built-in annotation or training workflow. Custom models require Amazon Augmented AI (A2I) setup and labeled datasets. |
| Results review UI | Built-in results table with inline feedback (correct/incorrect/corrected), confidence scores, and re-processing from corrections. | No built-in review UI. Textract returns JSON; you build your own review interface or use Amazon A2I for human review loops. |
| Chat & query | Chat assistant lets you query extracted data and raw document text with natural language. Answers cite specific documents. | No chat interface. Querying extracted data requires building your own application layer on top of Textract output. |
| Export options | One-click CSV and Excel export from the results view. Includes confidence scores and feedback status. | Returns JSON/API responses. CSV or Excel export requires custom code or third-party tools. |
| Pricing | Credit-based with per-model rates. Free tier included. Costs scale with extraction complexity, not just page count. | Pay-per-page pricing ($1.50/1000 pages for forms, $15/1000 for queries). Predictable but adds up at scale. |
| Infrastructure | Fully managed SaaS. No AWS account, IAM roles, or infrastructure setup required. Runs on Azure or any cloud. | Requires AWS account, IAM configuration, S3 buckets, and Lambda/Step Functions for processing pipelines. |
Why choose DocumentIQ
No AWS lock-in — DocumentIQ runs on any cloud and does not require an AWS account or infrastructure expertise.
Custom field schemas — define exactly what to extract with natural language prompts instead of generic key-value detection.
Built-in review UI — review, correct, and re-process results without building a custom application.
Chat across documents — ask questions about your entire document set and get cited, contextual answers.
Semantic understanding — LLMs grasp meaning and context, not just character recognition, leading to better accuracy on complex fields.
Where AWS Textract might be better
We believe in honest comparisons. Here are scenarios where AWS Textract could be a better fit.
Textract excels at pure OCR at massive scale — millions of pages per day with consistent per-page pricing.
Deep AWS integration makes Textract ideal if your infrastructure is already on AWS with S3, Lambda, and Step Functions.
Textract's pay-per-page model is more predictable for budgeting than credit-based pricing for some organizations.
Textract supports handwriting recognition and works well with low-quality scans where LLMs may struggle without OCR preprocessing.
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