Trellis vs Amazon Textract

Detailed side-by-side comparison to help you choose the right tool

Trellis

Document Processing

An AI-powered document intelligence platform that transforms unstructured documents into structured, actionable data. Trellis leverages LLMs to extract, classify, and analyze information from complex documents at scale — supporting PDFs, scanned images, spreadsheets, and more — with a developer-friendly API and customizable output schemas for seamless integration into enterprise workflows.

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Starting Price

Custom

Amazon Textract

Document Processing

AWS document processing service that extracts text, tables, forms, and structured data from scanned documents and images using machine learning. Pay-per-page pricing starting at $0.0015/page for OCR.

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Starting Price

Custom

Feature Comparison

Scroll horizontally to compare details.

FeatureTrellisAmazon Textract
CategoryDocument ProcessingDocument Processing
Pricing Plans127 tiers6 tiers
Starting Price
Key Features
  • Unstructured document parsing across PDFs, scanned images, spreadsheets, and Word documents
  • LLM-powered data extraction with high accuracy on printed and typed text
  • Custom schema mapping to define structured output fields per document type

    Trellis - Pros & Cons

    Pros

    • Handles complex multi-format documents including PDFs, scans, and spreadsheets in a single pipeline without needing separate tools per format
    • LLM-powered extraction adapts to layout variations without requiring rigid templates for each new document format
    • Scalable batch processing architecture designed for enterprise-grade document volumes in the thousands per day
    • Developer-friendly REST API with customizable output schemas enables rapid integration into existing ETL and data workflows
    • Reduces manual data entry errors and turnaround times in document-heavy pre-service operations
    • Focused on operational document intelligence rather than general-purpose AI, providing purpose-built extraction workflows

    Cons

    • Enterprise-focused pricing with custom quotes may be prohibitive for small teams, freelancers, or startups with low document volumes
    • Requires upfront schema configuration and pipeline setup before first extraction, adding time-to-value for new document types
    • Accuracy may degrade on handwritten documents or heavily degraded scans compared to clean typed or printed text
    • Limited publicly documented language support beyond English, which may restrict use for multinational organizations
    • No self-serve pricing page — prospective users must contact sales to evaluate cost, making it harder to budget in advance

    Amazon Textract - Pros & Cons

    Pros

    • Pay-per-page pricing starting at $0.0015/page with volume discounts makes costs predictable and proportional to usage
    • Seamless AWS ecosystem integration with S3, Lambda, SNS, and DynamoDB for automated document processing workflows
    • Handwriting recognition accurately extracts mixed printed and handwritten content that many competitors miss
    • Specialized extraction models for invoices, IDs, and lending documents understand domain-specific formats without configuration
    • Asynchronous processing handles documents up to 3,000 pages as background jobs with automatic scaling
    • No infrastructure management required: fully managed service with automatic scaling and high availability
    • 3-month free tier with 1,000 OCR pages/month lets teams evaluate the service before committing

    Cons

    • No custom model training: limited to prebuilt extraction models, unlike Azure Document Intelligence which supports custom training
    • JSON output with bounding boxes requires significant post-processing for LLM and RAG applications expecting plain text
    • Table extraction accuracy for highly complex, nested layouts trails Azure Document Intelligence capabilities
    • Synchronous API limited to single-page documents; multi-page processing requires S3 and async workflows
    • AWS-only deployment with no on-premises option for organizations with strict data residency requirements

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