Top 10 Data Extraction APIs for AI Agents (2026)

The complete market map and weighted assessment of 44 structured data extraction solutions for AI agents: document AI, invoice parsers, schema validators, and the 10 best ranked by what actually matters for production reliability.

80-90% of all business data is unstructured - IDC/Gartner. Invoices, receipts, contracts, emails, PDFs, scanned forms. It sits in inboxes and file servers, invisible to every system of record. The Intelligent Document Processing market hit $14.16 billion in 2026 and is growing at 26.2% CAGR toward $91 billion by 2034 - Fortune Business Insights. That growth is being driven almost entirely by AI agents that need to turn this unstructured mess into clean, validated JSON that can land in a CRM, ERP, or database.

Here is the problem that most agent builders underestimate: LLMs can extract structured data from documents. Claude, GPT-4o, and Gemini all score 90-94% accuracy on invoice extraction benchmarks - Koncile. That sounds good until you do the math. At 95% accuracy across 100,000 invoices per year with 14 fields each, you get approximately 70,000 field-level errors requiring human review. At 98%, that drops to 28,000 errors, a 60% reduction in exception handling workload - IntellSolution. The difference between 95% and 98% is the difference between "your team reviews most documents" and "your team handles only flagged exceptions."

This guide maps 44 providers across the structured data extraction landscape and ranks the top 10 by weighted score. We cover document AI platforms, invoice parsers, PDF-to-markdown converters, schema validation libraries, and the hybrid approach that 2026 consensus considers optimal. Every price is verified against official pricing pages, and accuracy claims are cross-referenced against independent benchmarks.

Written by Yuma Heymans (@yumahey), who builds agent infrastructure at O-mega.ai where document extraction is a core pipeline component for autonomous business workflows.

Contents

1. The Master Ranking: Top 10 Extraction APIs, Weighted and Ranked
2. Why LLMs Alone Are Not Enough
3. The Three-Layer Hybrid Architecture
4. The Top 10: Detailed Profiles
5. The Full Provider Directory (44 Services)
6. Schema Validation: The Missing Layer
7. Cost Analysis: Manual vs Automated vs LLM
8. Accuracy Benchmarks: LLMs vs Specialized APIs
9. How to Choose: Decision Framework

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1. The Master Ranking: Top 10 Extraction APIs, Weighted and Ranked

How to read this table: Each cell is a raw score (0-10). The Final Score is the weighted average: (Accuracy x 0.25) + (Schema Reliability x 0.20) + (Cost x 0.20) + (Doc Types x 0.15) + (Agent Ready x 0.10) + (Scale x 0.05) + (Validation x 0.05). The "What It Does" column captures each provider's identity, standout metric, and key fact. Ordered by final score, best first.

Criteria definitions and weight rationale:

• Accuracy (25%): Field-level extraction accuracy on standard benchmarks (ParseBench, invoice benchmarks). Highest weight because extraction errors cascade into downstream systems. 95% vs 98% is the difference between reviewing most documents vs handling only exceptions.
• Schema Reliability (20%): Does it guarantee valid, typed output every time? A parser that returns clean JSON 99% of the time and malformed data 1% of the time creates a production nightmare. 10 = compile-time guarantees (BAML, Outlines), 8 = structured JSON with confidence scores, 5 = best-effort JSON.
• Cost (20%): Price per page/document at production volume. 10 = under $0.005/page, 7 = $0.01-0.05, 5 = $0.05-0.10, 3 = $0.10+. High weight because document processing at scale (100K+ docs/month) compounds cost fast.
• Document Types (15%): How many formats can it handle? PDF, images, DOCX, XLSX, email, handwritten, scanned. 10 = 50+ types, 7 = 15-50, 5 = 5-15, 3 = specialized (invoices only).
• Agent Readiness (10%): API quality, SDKs, async processing, webhook support. 10 = async API + multiple SDKs + webhook + MCP, 5 = REST API only.
• Scale (5%): Can it handle 1M+ pages/month? Provisioned capacity, async batch, enterprise SLA.
• Validation (5%): Built-in confidence scores, human-in-the-loop routing, error handling. 10 = HITL + confidence + auto-retry, 5 = confidence only, 1 = none.

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2. Why LLMs Alone Are Not Enough

The first-principles question: if Claude, GPT-4o, and Gemini can all extract structured data from documents, why do specialized extraction APIs exist?

The answer has three parts. The first is accuracy at scale. LLMs achieve 90-94% accuracy on invoice benchmarks in controlled settings - Koncile. That is impressive for a general-purpose model. But "94% accuracy" means 6 errors per 100 invoices. At 10,000 invoices per month, that is 600 errors requiring human review. Specialized APIs with 98%+ accuracy cut that to 200 errors, a 3x reduction in exception handling. For financial documents where every field matters (invoice total, tax ID, line items), the difference between 94% and 98% is the difference between a useful automation and an unreliable one.

The second is cost at volume. Processing a single page with Gemini Flash 2.0 costs approximately $0.0003 - Vellum AI. That seems cheap until you factor in the full pipeline: you need to send the raw document as an image (expensive input tokens), define the extraction schema in the prompt (more tokens), and often retry on malformed output. At 100,000 pages per month, the LLM-only approach costs roughly $30-150 depending on model and complexity. Google Document AI's invoice parser does the same job for $0.01/page ($1,000/month), but with higher accuracy on header fields and validated JSON output. The LLM approach is cheaper per page but more expensive per correctly extracted field when you account for error handling.

The third is schema reliability. An LLM asked to return JSON sometimes returns markdown-wrapped JSON, sometimes includes chain-of-thought reasoning before the JSON, sometimes omits required fields, and sometimes hallucinates field values that look plausible but are wrong. Tools like Instructor and BAML exist specifically to solve this problem: they validate LLM output against a schema, automatically retry on failure, and guarantee type-safe structured data. Without this validation layer, an LLM-based extraction pipeline is a probabilistic system feeding into a deterministic one (your database), which is a recipe for data corruption.

As we explored in our guide to building a Claude chatbot, tool use and structured output are among the most critical capabilities for production systems. Document extraction is where this matters most.

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3. The Three-Layer Hybrid Architecture

The 2026 consensus from both academic benchmarks and practitioner reports is that the optimal extraction pipeline for AI agents is a three-layer hybrid - Vellum AI, Unstract.

Layer 1 (Parsing) handles the physical challenges: OCR, layout detection, table extraction, reading order. This is where specialized APIs outperform LLMs, because layout analysis and OCR on degraded scans are fundamentally different problems from language understanding. Reducto, LlamaParse, Unstructured, Docling, or the hyperscaler Document AI services excel here.

Layer 2 (LLM Extraction) handles the semantic challenges: understanding what a field means, mapping it to your schema, handling variations in how the same information is expressed. This is where LLMs excel, because semantic understanding is exactly what they are built for. Claude with Instructor, GPT-4o with structured outputs, or Gemini with schema mode all work well here.

Layer 3 (Validation) catches what both layers missed. Confidence scores below a threshold get routed to human review. Business rules (total must equal sum of line items, dates must be in the past) catch logical errors. Auto-retry with error feedback to the LLM handles schema violations.

This separation of concerns is the key insight: the parser handles what LLMs are bad at (layout, OCR), the LLM handles what parsers are bad at (semantics, flexibility), and the validator catches what both miss. As we covered in our analysis of the cost of AI agents, the cost of errors in production agent pipelines often exceeds the cost of the extraction itself.

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4. The Top 10: Detailed Profiles

4.1 Google Document AI (Score: 8.4/10)

Google Document AI leads this ranking because it offers the broadest range of pretrained specialized processors (15+ document types including invoices, receipts, bank statements, W-2s, driver licenses, passports) combined with a Gemini-powered Custom Extractor that needs only 10 sample documents for fine-tuning - Google Cloud.

The pricing is remarkably competitive at scale. The invoice parser costs just $0.01 per 10 pages ($0.001/page). OCR runs at $1.50/1K pages ($0.0015/page), dropping to $0.60/1K at 5M+ pages. The Layout Parser costs $10/1K pages and the Custom Extractor $30/1K pages - Google Cloud Pricing.

Accuracy benchmarks place Google Document AI at 96-98% on standard invoices and 85-92% on complex documents with varied layouts. The Gemini-powered Layout Parser identifies reading order correctly 90% of the time, which is critical for multi-column documents - Parsli.

For agent builders, the key advantage is the range of pretrained processors. Instead of building custom extraction logic for each document type, you route documents to the appropriate processor, and it returns structured JSON with confidence scores per field. The provisioned capacity model ($300/page-per-minute/month) handles burst processing without queuing delays.

4.2 Reducto (Score: 8.2/10)

Reducto raised a $108 million Series B in 2026 and has positioned itself as the parser purpose-built for LLM pipelines. Its Parse, Extract, Edit, and Split APIs handle 30+ file types with structure-preserving parsing that maintains headings, tables, and semantic layout - Reducto.

What sets Reducto apart is agentic correction: the system uses AI to detect and fix parsing errors autonomously, achieving 67.8% on ParseBench's Agentic score, the highest among commercial APIs - Reducto. The provenance tracking feature maps every extracted field back to its source location in the original document, which is critical for audit trails.

Pricing starts at $0.015/page with 15,000 free credits. HIPAA and SOC2 compliance are included, making it viable for healthcare and financial document processing. Available on AWS Marketplace for enterprise procurement - Reducto Pricing.

4.3 Azure AI Document Intelligence (Score: 8.2/10)

Azure (formerly Form Recognizer) claims 99%+ accuracy on prebuilt models for 12+ document types and offers the deepest volume discounts: commitment tiers at $0.53/1K pages (50% savings) for organizations processing 8M+ pages/month - Azure Pricing.

The Microsoft ecosystem integration (Power Automate, Logic Apps, Azure Functions) makes it the default choice for organizations already on Azure. The free tier provides 500 pages/month, enough for evaluation.

4.4-4.10: AWS Textract, LlamaParse, Instructor, Unstructured, Mindee, Nanonets, BAML

AWS Textract (7.9) wins on raw cost: $0.0006/page at 1M+ volume for basic text detection. LlamaParse (7.6) scores highest on ParseBench (84.9%) and offers the widest cost/accuracy range ($0.004/page basic to $0.11/page agentic). Instructor (7.6) is the schema validation standard with 3M+ monthly downloads and automatic retry on validation failure. Unstructured.io (7.4) handles the widest range of file types (65+) with an open-source core. Mindee (7.1) offers the simplest developer experience with per-page billing. Nanonets (7.1) provides the most complete workflow automation with built-in human-in-the-loop. BAML (7.5) gives compile-time type safety across 6 languages with Schema-Aligned Parsing that handles real-world LLM output quirks.

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5. The Full Provider Directory (44 Services)

Beyond the top 10, we mapped 34 additional providers. Here are the highlights by category.

Cloud Document AI (3): Google Document AI, AWS Textract, Azure Doc Intelligence. All offer pretrained models, per-page pricing, and enterprise scale.

Specialized Extraction APIs (7): Reducto ($108M raised, agentic correction), Mindee (developer-first), Nanonets (workflow automation), Veryfi (3-5 second processing, privacy-first), Rossum (AP automation, $18K/year), Docsumo (financial docs focus), Affinda (model memory, 400+ integrations).

PDF/Document Parsing (5): LlamaParse (highest ParseBench), Unstructured.io (65+ file types), Docling (IBM open source, Apache 2.0), Marker (best open-source default), Chunkr (YC-backed, $0.008/page).

Schema Validation Libraries (6): Instructor (Pydantic for LLMs, 3M+ downloads), BAML (multi-language typed DSL), Outlines (constrained decoding, impossible to produce invalid output), Guardrails AI (community validators), OpenAI Structured Outputs + Zod, Marvin AI (high-level extract/classify API).

Invoice/Receipt Specific (5): Tabscanner (9 years, 99.99% with HITL), Taggun (fraud detection), Dext (accountant-focused), Sypht ($0.05-1.00/page), Klippa (UiPath marketplace).

Email/Communication Parsing (4): Parseur (5,000+ app integrations), Parsio (template + GPT dual mode), Mailparser (simple email parsing), Nylas (full email/calendar infrastructure).

Web Data Extraction (3): Diffbot (Knowledge Graph), Apify (25K+ scrapers), Import.io (pricing intelligence).

OCR Engines (5): Tesseract (open source, CPU-only), PaddleOCR (best open-source accuracy on complex layouts), EasyOCR (simplest API), Google Vision API ($1.50/1K images), AWS Rekognition ($0.001/image).

Enterprise IDP (3): ABBYY Vantage ($0.02-0.08/page at volume), Hyperscience (99.5% claimed), Eden AI (unified API for 100+ models).

MCP-Native (2): Koncile MCP OCR Server (first MCP-native OCR, 24 tools), LandingAI ADE (vision-based MCP extraction).

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6. Schema Validation: The Missing Layer

Most extraction discussions focus on the parsing layer (how to read the document) and skip the validation layer (how to ensure the output is correct and typed). For AI agents, this is the most dangerous gap. An agent that receives malformed JSON from an extraction API will either crash, produce garbage downstream, or silently corrupt data in a system of record.

Three open-source tools have emerged as the standard validation layer in 2026.

Instructor (3M+ monthly downloads, 11K GitHub stars) wraps any LLM call with Pydantic validation. You define a Pydantic model, pass it to the LLM call, and Instructor guarantees the response matches your schema. If the LLM returns invalid data, Instructor automatically retries with the validation error as feedback, giving the model a chance to self-correct - Instructor.

BAML (BoundaryML) goes further with a domain-specific language for typed LLM functions. You define your extraction schema in BAML, and it generates type-safe clients for Python, TypeScript, Ruby, Java, Go, and Rust. The key innovation is Schema-Aligned Parsing: BAML handles the real-world messiness of LLM output (markdown-in-JSON, chain-of-thought before the response, trailing commas) without failing - BAML.

Outlines (dottxt-ai) takes the most radical approach: it constrains token generation during inference so that the model physically cannot produce invalid output. By building a finite state machine from your JSON Schema and masking invalid tokens during sampling, Outlines guarantees structural correctness at zero additional cost - Outlines.

For agent builders using platforms like Suprsonic, which provides unified API access to document extraction alongside 15+ other agent capabilities, the validation layer still matters. Even when the extraction API returns structured data, you need schema validation before writing to your system of record.

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7. Cost Analysis: Manual vs Automated vs LLM

The economics of document extraction are unambiguous. U.S. companies lose an average of $28,500 per employee annually to manual data entry - Parseur. Invoice processing costs drop from $30/invoice (manual) to $5/invoice (automated), an 83% reduction - Nodewave. Most businesses see 240% ROI on data entry automation with payback in 6-9 months.

The hybrid approach (specialized parser + LLM extraction + validation) costs more per document than LLM-only, but the reduced error rate cuts human review costs by 60-80%. At 100K invoices per year, the math favors hybrid: approximately $15,000 total (extraction + reduced review) vs $45,000 for LLM-only (cheaper extraction but 3x more review). As we documented in our AI agent cost analysis, the cost of agent errors in production always exceeds the cost of preventing them.

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8. Accuracy Benchmarks: LLMs vs Specialized APIs

The most comprehensive independent benchmark is ParseBench (April 2026), which evaluates document parsing accuracy across standardized test sets - arXiv.

For invoice extraction specifically, the benchmark landscape shows that simple fields (vendor name, invoice total, date) reach 99%+ accuracy across all modern approaches. The gap appears on line items and nested tables, where accuracy drops to 95-97% for the best systems and below 90% for basic OCR - CodeSOTA. This is exactly the scenario where the hybrid approach wins: the specialized parser handles the table structure, and the LLM handles the semantic mapping.

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9. How to Choose: Decision Framework

If you process invoices/receipts at scale (AP automation, expense management): Google Document AI invoice parser at $0.001/page or AWS Textract expense analysis at $0.01/page. Both return structured JSON with confidence scores. Add Instructor for schema validation before writing to your ERP.

If you need to feed documents into LLM/RAG pipelines: LlamaParse ($0.004/page basic, $0.11/page agentic) or Reducto ($0.015/page) for parsing. Unstructured.io ($0.03/page) if you need the widest file type support (65+). Docling (free, open source) if you have GPU infrastructure and want to self-host.

If schema reliability is your primary concern: Instructor (Python, Pydantic, 15+ LLM providers) or BAML (multi-language, compile-time type safety). For the most extreme guarantee, Outlines constrains token generation so invalid output is physically impossible.

If you need full workflow automation (not just extraction): Nanonets (built-in HITL, routing, and business rules) or Rossum (end-to-end AP automation with ERP integration).

If you are already on a cloud platform: Use your platform's native service. Google Document AI for GCP, AWS Textract for AWS, Azure Document Intelligence for Azure. The ecosystem integration and volume pricing make switching to a third-party API rarely worthwhile.

For most AI agents, the answer is the hybrid approach: a specialized parser (Reducto, LlamaParse, or your cloud platform's Document AI) for the parsing layer, an LLM with Instructor/BAML for the extraction layer, and Pydantic/Zod validation before writing to your system of record. This three-layer architecture is more complex to build but dramatically more reliable in production.

This guide reflects the structured data extraction landscape as of April 2026. Pricing, accuracy benchmarks, and capabilities change rapidly. Verify current details on official pricing pages before purchasing.