AWS Textract alternatives for AI agents in 2026

AWS Textract is the default choice for document extraction in the AWS ecosystem. It's reliable, scales well, and handles forms and tables. But if you're building an AI agent, Textract has gaps that matter.

What Textract does well

Credit where it's due:

• OCR accuracy: Strong on printed text, forms, and standard layouts
• Table extraction: Detects and structures tables with rows and cells
• Forms (key-value pairs): Extracts labeled fields from form documents
• Scale: AWS infrastructure, pay-per-page, no rate limit concerns

Where Textract falls short for AI agents

No custom schemas. Textract returns its own structure — blocks, lines, key-value pairs. Your agent has to map Textract's output to the fields it actually needs. That mapping code is fragile and format-specific.

No computed answers. Textract extracts what's on the page. It can't compute a growth rate, verify a total, or cross-check numbers across pages. Your agent has to do all reasoning itself.

No confidence per custom field. Textract has confidence scores per detected block, but not per semantic field. "Is the vendor name correct?" is a different question than "is this text block readable?"

No website support. Textract processes documents. If your agent also needs to extract data from URLs, you need a separate tool.

No citations. Textract tells you where text is on the page (bounding boxes), but not which text was used to determine a specific field value.

What AI agents actually need from document extraction

Textract vs The Drive AI: concrete example

Processing an invoice with Textract:

# Textract returns blocks — you map them yourself
response = textract.analyze_document(Document={...}, FeatureTypes=["TABLES", "FORMS"])
blocks = response["Blocks"]  # hundreds of blocks
# Now write code to find "Total", match it to the right value,
# handle different layouts, parse the number string...

Processing the same invoice with The Drive AI:

# Define what you need, get exactly that
result = client.extract(
    file="invoice.pdf",
    schema={
        "vendor": {"type": "string", "description": "Company name"},
        "total": {"type": "number", "description": "Total amount due"},
    }
)
# result.data = {"vendor": "Acme Corp", "total": 6199.20}
# result.confidence = {"vendor": "high", "total": "high"}

Other alternatives worth considering

Google Document AI — Google's equivalent to Textract. Better pre-built processors for specific document types (invoices, receipts, W-2s), but you're locked into predefined schemas. Custom processors require training data. No computed answers, no website support.

Reducto — focuses on high-quality document parsing for LLM ingestion. Returns clean markdown with good table preservation. Similar to LlamaParse — great for RAG, less useful when your agent needs specific typed fields.

Extend — closest to The Drive AI in feature set. Schema-based extraction, classification, splitting. Starts at $300/month. Has a "Composer" agent that auto-refines schemas. No document reasoning or website extraction.

Pricing comparison for document extraction APIs

When to stay with Textract

• You need bounding box coordinates for visual document processing
• You're deeply integrated into the AWS ecosystem
• You need raw text extraction without semantic interpretation
• You're processing millions of pages and need AWS-scale infrastructure

When to switch

• Your agent needs typed fields, not raw blocks
• You need computed answers or cross-checks
• You process documents and websites
• You want confidence scores per field, not per text block
• You want to stop writing layout-specific mapping code

Try the playground with a document you currently process through Textract. Compare the output.