Best document extraction APIs for developers in 2026

You need to extract structured data from documents. There are now a dozen APIs that claim to do it. Here's what actually matters when choosing one — and how the top options compare in 2026.

What to evaluate in a document extraction API

Before comparing products, know what matters for your use case:

• Schema flexibility: Can you define custom fields, or are you locked into pre-built extractors?
• Output format: Raw text, markdown, or typed JSON matching your schema?
• Format coverage: PDFs only, or also spreadsheets, images, websites?
• Accuracy signals: Confidence scores per field? Source citations?
• Beyond extraction: Can it compute answers, or only read literal values?
• Pricing model: Per page, per API call, or monthly subscription?

The 2026 landscape at a glance

1. The Drive AI

Best for: AI agents that need extraction, reasoning, and cross-document analysis across files and URLs.

The Drive AI is a file intelligence API with three levels: extract (pull literal values), analyze (compute answers with reasoning traces), and cross-analyze (compare multiple documents). Handles 107+ file formats plus live websites with JavaScript rendering. The only API that can extract data, verify the math, and cross-reference across documents — all without custom agent orchestration.

What sets it apart: Most APIs stop at extraction. The Drive AI adds a reasoning layer that computes derived answers (growth rates, verification, cross-checks) and a cross-analysis layer that compares information across multiple files in one call. Your agent doesn't need to be a file agent — it calls one.

Limitation: Newer product, smaller team. No SOC 2/HIPAA yet. No document splitting. Analyze is slower than pure extraction (trades speed for accuracy on complex questions).

Pricing: Extract: $0.01/page. Analyze: $0.02/page. Cross-analyze: $0.05/doc + $0.03/page. Free: 100 credits/month. No minimum spend.

2. Reducto

Best for: Teams building RAG pipelines who need the cleanest possible document-to-text conversion.

Reducto's multi-pass OCR + vision model pipeline produces excellent text quality. Their Deep Extract feature uses an agentic loop to iteratively refine extraction. SOC 2 and HIPAA compliant. Trusted by Harvey and Scale AI for high-stakes document work.

Limitation: Core output is text/markdown for RAG ingestion. Schema-based extraction exists but reasoning and computation don't. No website support. Deep Extract is powerful but adds latency.

Pricing: Per-page, varies by feature. Enterprise focus.

3. Extend

Best for: Enterprises processing high volumes of known document types who need a managed workflow.

Extend is a full document processing platform — classify, split, extract, edit. Their Composer agent learns from corrections to auto-refine schemas. Human-in-the-loop review built in. Strong for operational workflows (claims processing, invoice automation).

Limitation: Starts at $300/month. No reasoning or computation. No website extraction. No cross-document analysis. Better for operational workflows than for AI agent tools.

Pricing: Starting at $300/month + per-page credits.

4. AWS Textract

Best for: Teams already in AWS who need basic OCR, table detection, and form extraction at scale.

Textract is the reliable workhorse. It reads documents, detects tables, and extracts key-value pairs from forms. It's fast, scales with AWS infrastructure, and costs $0.015/page for tables+forms.

Limitation: Returns raw blocks and cells — you write the code to map them to your schema. No semantic understanding. If a field is labeled differently across vendors, your mapping code breaks. No computed answers, no website support.

Pricing: $0.015/page (forms + tables), $0.01/page (text only). Free: 1,000 pages/month for 3 months.

5. Google Document AI

Best for: Teams processing known document types (invoices, receipts, W-2s) who want pre-trained extractors.

Google offers pre-built "processors" for common document types. Invoice processor, receipt processor, W-2 processor — these work well out of the box for their specific document types. Custom processors require training examples.

Limitation: Locked into Google's schema unless you train a custom processor (requires labeled training data). No reasoning, no websites, no cross-document analysis.

Pricing: $0.01-0.065/page depending on processor type. Free: 1,000 pages/month.

6. Azure Document Intelligence

Best for: Microsoft/Azure shops who need pre-built models with custom model training capabilities.

Similar to Google's approach — pre-built models for invoices, receipts, and IDs, plus custom model training. Strong on handwriting recognition. Good enterprise features (private endpoints, managed identity).

Limitation: Same pattern — pre-built schemas or train your own. No reasoning, no websites. Custom models require labeled training data.

Pricing: $0.01-0.05/page. Free: 500 pages/month.

7. LlamaParse

Best for: LlamaIndex users who need clean text for vector embeddings.

LlamaParse is a document parser, not an extractor. It converts documents to clean markdown optimized for LLM consumption and vector embedding. Native integration with LlamaIndex. Good format coverage.

Limitation: Output is markdown, not structured JSON. No schema-based extraction. No reasoning. No websites. Great for search, not for typed data.

Pricing: Free: 1,000 pages/day. Paid plans for higher volume.

Which API should you use?

The right choice depends on where you are in the pipeline. Parsing for RAG? Reducto or LlamaParse. Operational workflows with known doc types? Extend or Google Document AI. AI agents that need to understand, reason over, and cross-reference files? Try The Drive AI.