The best document extraction APIs in 2026

What a document extraction API does

A document extraction API takes a PDF, scan or photo over HTTP and returns structured data, usually JSON. The products in this guide split into two kinds. Parsing APIs turn a whole document into clean text, tables and layout for an AI system to read. Extraction APIs return specific named fields, such as an invoice number, a total or an account holder, often with a confidence score and the location on the page. Many products now do both, but each leans one way. For the basics, see what document data extraction is.

How we compared them

• What comes back: parsed content for AI, or named fields for a business process.
• Where it runs: vendor cloud only, your own cloud account, or your own servers with no internet connection.
• How it is priced: per page, per credit, per document or by annual contract, and whether a free tier exists.
• What happens to uncertain values: a confidence score, a review step, or nothing.

Best for feeding documents to AI applications

Reducto parses PDFs, scans, spreadsheets and slides, extracts typed fields with a citation and bounding box on every value, and lists Parse at $10 and Extract at $20 per 1,000 pages. LlamaParse, from LlamaIndex, reads a wide range of file types and pairs parsing with schema-based extraction. Its pricing page lists a free plan with 10,000 credits and a Pro plan at $500 a month. Unstructured focuses on turning large collections into AI-ready data, with 10,000 free pages and $0.015 a page after that. Choose from these three if your output feeds retrieval or an agent. Compare them with us: Reducto, LlamaParse, Unstructured.

Best developer platforms for structured fields

Extend combines parsing, extraction, classification and splitting with workflows, human review and an evaluation suite, and it publishes benchmarks with public datasets. Credits cost $0.01 each after 10,000 free ones. Sensible prices per document rather than per page, starting at $499 a month on monthly billing. Both suit engineering teams that will own schemas and test sets. See docRefinery vs Extend.

Best for invoices and back-office documents

Mindee sells ready-made APIs for invoices, receipts, ID cards, passports and proof of address on credit plans. Docsumo is a fuller platform for lending, insurance and accounts payable, and lists SOC 2 Type 2 and ISO 27001. Nanonets has moved towards AI agents that run whole processes, and offers on-premises deployment on Enterprise. Rossum focuses on accounts payable workflows, from $18,000 a year. For invoice-specific detail, see our invoice data extraction guide and Docsumo alternatives.

Best if you are standardised on one cloud

Amazon Textract, Azure Document Intelligence and Google Document AI read text, tables and forms reliably and keep everything in one cloud account. Textract's text detection costs $0.0015 a page and Google's Enterprise Document OCR $1.50 per 1,000 pages. Azure is the only one of the three with an offline option: disconnected containers, by application and on a commitment plan. All three leave classification, review and business rules to you. See Textract alternatives.

Best open-source options

Docling is an MIT-licensed converter you run locally. Unstract has an open-source edition, a managed cloud and a self-hosted Enterprise edition. They are the right choice when license cost must be zero and you have engineers to run them. You take on hosting, upgrades and the extraction logic yourself.

docRefinery: where it fits and where it does not

On this list, docRefinery sits with the named-field tools, not the parsers: it returns checked fields from invoices, bank statements, receipts and purchase orders, not content for an AI pipeline. What sets it apart in that group is memory: each sender's layout is learned once and remembered, and re-trained when a form changes. It is our product, so weigh this list accordingly. Several of the others share some of these strengths; the claim is the combination.

• Output you define: each field has a plain-language instruction, a Return as alias that becomes the output key, a type and a required flag.
• One format across every document type, with tables returned as rows, so one parser covers invoices, statements and IDs.
• Governed fields, from a catalogue of 20 document types and 785 field definitions, with owner, admin and member roles. Custom types are trained, hosted and re-trained at no charge.
• Confidence scoring on by default, with per-field thresholds and a review queue, null for absent fields, and page and box citations on extracted values and table cells.
• Corrections become suggested fixes, tested on that layout's own documents before anyone applies them.
• Workflows from one sample document, with rules on values, versions, replay and compare.
• Long, multi-page files segmented into the documents inside them, in 70+ languages and scripts.
• Insights on vendors, flagged fields and auto-approval trends; export to JSON, CSV or XLSX, always with corrections.
• API keys with expiry, revocation and an activity log, plus a Chrome extension that auto-fills web forms (early access, not in the Chrome Web Store yet).
• Managed cloud, private VPC or fully air-gapped on premises (Enterprise), on models that run inside your network. Retention runs from 7 days on Starter to custom terms, encrypted in transit and at rest.
• Starter is free up to 100 pages a month with no card, and paid plans are custom and quoted by sales.

The limits are real too, and the roadmap shows where each stands.

• Choose it when most of your volume comes from senders whose layouts repeat, when you need to control which fields leave a document, or when documents must stay on your own network.
• Look elsewhere if you need a SOC 2 report today. Our SOC 2 Type II audit is in progress with no report yet, SSO is planned for Enterprise and not available yet, and we make no data-residency commitments yet.
• Look elsewhere today if you need native connectors or webhooks, which are in design, or RAG chunking, which is planned. Forgery detection is still research.
• Look elsewhere if you need presentations, an independent accuracy benchmark, or customer case studies. We are in Early Access and have none of these yet.

How to pick in four steps

1. Decide whether you need parsed content for AI or named fields for a process. That halves the list.
2. Rule out anything that cannot run where your documents are allowed to go.
3. Price your real volume, including the features you will turn on, not the entry rate.
4. Test 50 to 100 of your own documents on two or three finalists. Include several from the same sender, and some where a field is missing, and compare field by field.