Data extraction tools turn information locked inside websites, PDFs, scans and emails into structured data you can query, analyze or feed into other systems. The category covers two very different jobs. Web data extraction tools crawl and parse pages from the public web, while document data extraction tools read files you already hold, such as invoices, purchase orders, forms and statements. Many teams need both, and the best choice depends on where your data starts.
This list ranks the top data extraction tools in 2026 across both types. For every entry we state whether it handles web extraction, document extraction or both, and we checked features and pricing on each vendor’s own site. Several are AI data extraction tools that let you describe the fields you want in plain English instead of writing selectors or drawing templates, which has changed how quickly a new source can go live.
If your project is purely about scraping websites, our deeper roundup of web scraping tools for data extraction covers more options on that side. Here the focus is on choosing the right data extraction software for the job, whichever kind of source you have.
Data Extraction Tools At A Glance
Company
Headquarters
Best For
Standout Trait or Pricing Model
Apify
Prague, Czech Republic
Web extraction at any scale, code or no-code
Free $5 monthly usage; paid plans from $19 per month
Diffbot
Menlo Park, California, USA
Web extraction without per-site rules
Free 10,000 credits; paid from $299 per month
Amazon Textract
Seattle, Washington, USA (AWS)
Document extraction inside AWS pipelines
Pay per page, priced by API feature
Nanonets
San Francisco, California, USA
Document extraction for finance and operations workflows
$50 free credits, then usage-based
Firecrawl
Not published
Web extraction for AI agents and LLM apps
1,000 free credits; paid from $16 per month billed annually
Rossum
Czech Republic (now part of Coupa)
Document extraction for transactional documents
Custom pricing
Octoparse
Irvine, California, USA
No-code web extraction for analysts
Free plan; Standard from $69 per month billed annually
Parseur
Not published (Parseur Pte. Ltd.)
Email and document extraction into business apps
Free 20 pages; paid from $49 per month
Docparser
Not published (SureSwift Capital)
Rule-based document extraction from fixed layouts
From $39 per month; 14-day free trial
Airparser
Not published
LLM-based extraction from emails and PDFs
Free trial; paid from $33 per month billed annually
Best Data Extraction Tools In 2026
1. Apify — The Broadest Web Extraction Platform, With 90,000+ Ready-Made Actors
Extraction type: web. Apify is a full-stack platform for building, running and scheduling web data extraction jobs. Its core unit is the Actor, a packaged scraper or automation that runs in Apify’s cloud, and the Apify Store now lists more than 90,000 of them, so for many common sources you can start from an existing Actor rather than writing code. Developers can also build their own in JavaScript or Python and deploy them to the same infrastructure.
The platform handles the operational layer that usually eats engineering time: scheduling, storage of results, proxy management, monitoring and integrations with tools such as Zapier, Make and AI agent frameworks. That makes Apify equally useful to a growth analyst who needs a one-off dataset and an engineering team running hundreds of recurring jobs.
Pricing is usage-based. The free plan includes $5 of platform usage each month with no card required, Starter costs $19 per month plus pay as you go, Scale is $199 and Business $999, with lower compute unit rates on higher tiers. Enterprise plans add custom scraping solutions and a dedicated team.
More than 90,000 Actors in the Apify Store
Build your own in JavaScript or Python on the same cloud
Free tier with monthly usage credit
Best for: teams that want one platform for both off-the-shelf and custom web data extraction.
2. Diffbot — Structured Web Data Without Writing Per-Site Rules
Extraction type: web. Diffbot takes a different approach from template-based scrapers. Its Extract API classifies a page, such as an article, product or discussion, and returns structured JSON without you defining selectors for each site. Its Crawl product walks an entire site from a single seed URL and returns thousands of pages as one structured dataset, and its Natural Language API pulls entities, relationships and sentiment out of plain text.
Diffbot also maintains a Knowledge Graph of public web entities, including companies, people and news, that you can query directly. For many research and enrichment jobs, querying the graph is faster than extracting the data yourself. The company notes on its site that its extraction is not an LLM, which matters to teams that need consistent, repeatable output rather than generated text.
There is a free plan with 10,000 credits a month across all four APIs. Paid plans start at $299 per month for 250,000 credits, with Plus at $899 adding Crawl access and more seats, and custom Enterprise terms above that.
Best for: data and research teams extracting from many different sites who do not want to maintain a rule per domain.
Extraction type: documents. Amazon Textract is AWS’s machine learning service for pulling text, handwriting, layout elements, forms and tables out of scanned documents and images. It goes beyond plain OCR by returning key-value pairs and table structure, and it offers specialized APIs for expenses such as invoices and receipts, identity documents and mortgage and lending packages, plus Queries that let you ask for a specific field in natural language.
Its main advantage is fit. If your documents already land in S3 and your workflows run on AWS, Textract slots in with no extra vendor, scales up or down with demand and inherits AWS security and compliance controls. You can use pretrained features or customize them for your own document types.
Pricing is per page and depends on the feature you call. In the US West (Oregon) region, basic text detection is $0.0015 per page for the first million pages, table analysis $0.015 and expense analysis $0.01, with lower rates above a million pages. New AWS customers get a three-month free tier that includes 1,000 pages a month of text detection.
Best for: engineering teams on AWS that need high-volume document data extraction billed by usage.
4. Nanonets — Document Extraction Wired Into Finance And Operations Workflows
Extraction type: documents. Nanonets extracts data from unstructured business documents, including invoices, purchase orders, remittances, supplier forms, claims and faxed or scanned files, using its own OCR model. Where it goes further than a pure extraction engine is in what happens next: it builds AI agents that apply your business rules to the extracted data and post decisions into systems of record.
Named workflows include accounts payable, order management, cash application, supplier onboarding, claims processing, reconciliation and freight audit. Confidence scores decide when an agent acts on its own and when it escalates to a person, and the system learns from human corrections. Integrations cover SAP, Oracle, NetSuite, Dynamics 365, QuickBooks, Xero, Salesforce, Gmail, Outlook and SharePoint, plus an MCP server and REST and GraphQL APIs.
Nanonets lists SOC 2 Type II, GDPR, ISO 27001 and HIPAA (with a BAA on enterprise plans). You can start with $50 of free credits and no card, with usage-based pricing after that.
Proprietary OCR model plus open-source models on Hugging Face
Human-in-the-loop approval gates with confidence scores
Deep ERP and accounting integrations
Best for: finance and operations teams that want extraction and the downstream workflow in one product.
5. Firecrawl — Clean Web Data For AI Agents And LLM Apps
Extraction type: web, with document parsing. Firecrawl is a web data API built for AI applications. Its Scrape endpoint renders JavaScript and returns a page as clean Markdown, HTML, metadata or structured JSON that matches a schema you supply. Crawl follows links from a starting URL with depth and path filters, Map lists a site’s URLs, and Search returns web results with full-page content attached. It can also parse PDF and DOCX files.
That design makes it one of the most practical AI data extraction tools for teams building retrieval-augmented generation, research agents or content pipelines. It ships an MCP server, a CLI and SDKs for Python, Node.js, Go, Rust, Java and Elixir, and its open-source repository is among the most starred projects in the scraping space. The company reports more than 150,000 companies using it.
The free plan includes 1,000 credits a month. Paid plans start at $16 per month billed annually for 5,000 credits, with Standard at $83 for 100,000 credits. Basic scrapes cost one credit per page, and JSON extraction adds four credits per page.
Best for: developers feeding live web content into LLMs and AI agents.
6. Rossum — Transactional Document Automation, Now Part Of Coupa
Extraction type: documents. Rossum is an intelligent document processing platform focused on transactional documents: invoices, bills of lading, sales orders, e-invoices and the paperwork around customs clearance and quality assurance. It ingests documents from email, scanners, PEPPOL and shared drives, extracts the data with a proprietary transactional LLM that supports 276 languages and handwriting, and validates it against master data, ERPs and third-party APIs.
Beyond extraction, Rossum infers fields such as GL, tax and HS codes, standardizes formats, automates approval rules and keeps audit trails and an archive. It integrates with SAP, Coupa, NetSuite, Workday and Microsoft Dynamics. Coupa has announced that it acquired Rossum, which makes it a natural option for organizations already on Coupa for spend management. Pricing is by quote.
Best for: procurement, AP and logistics teams processing high volumes of transactional documents in many languages.
7. Octoparse — No-Code Web Extraction With Hundreds Of Templates
Extraction type: web. Octoparse is a point-and-click web data extraction tool for people who do not write code. You build a task by clicking the elements you want on a page, or start from one of more than 500 preset templates, then run it locally or in Octoparse’s cloud on a schedule. Exports cover Excel, CSV, JSON, HTML and XML, and higher plans push data to Google Sheets, Google Drive, Dropbox or S3 and add API access.
Based in Irvine, California, Octoparse reports more than 4 million registered users. A free plan supports 10 tasks with local extraction and up to 50,000 exported rows a month. Standard starts at $69 per month billed annually with cloud extraction and scheduling, and Professional is $249 per month billed annually with more concurrent cloud processes and monitoring. A paid data service is also available from $599 if you would rather hand the job off.
Best for: analysts and marketers who need recurring website data without engineering help.
8. Parseur — Email And PDF Extraction That Flows Into Your Apps
Extraction type: documents and emails. Parseur turns emails, attachments, PDFs, scans, images, spreadsheets and HTML into structured data and sends it to other applications. Its AI engine extracts the fields you describe in plain English and handles new layouts without a template, and you can combine AI extraction with OCR in more than 200 languages and rule-based templates where you want strict control.
Parseur is built around automation: results flow into Zapier, Make, n8n, Google Sheets, Excel, Airtable, Salesforce, Power Automate and webhooks, with a developer API as well. Typical uses include order confirmations, booking emails, leads from web forms and supplier documents. The free plan covers 20 pages a month, and paid plans start at $49 per month for 100 pages, scaling by monthly page volume.
Best for: operations teams that receive data by email and want it in a spreadsheet or CRM automatically.
9. Docparser — Rule-Based Zonal OCR For Consistent Layouts
Extraction type: documents. Docparser extracts data from Word, PDF, CSV, XLS, TXT, XML and image files using rules you define without code. You can draw a zone around an area for zonal OCR, set column dividers to pull tables, read checkboxes, radio buttons and barcodes, and clean up scanned images before parsing. Preset templates cover invoices, purchase orders, bank statements, contracts, HR forms and delivery notes, and DocparserAI adds an AI engine for less predictable documents.
Documents arrive by upload, email, cloud storage such as Dropbox, Box, Google Drive and OneDrive, or a REST API, and results go to Excel, CSV, JSON, XML or Google Sheets, or onward through Zapier, Workato and Power Automate. The rule-based approach is a strength when layouts are stable and you want predictable output. Plans start at $39 per month, with a 14-day free trial and no card required.
Best for: teams processing the same document layouts repeatedly who value precise, auditable rules.
10. Airparser — LLM-Powered Parsing For Emails, PDFs And Images
Extraction type: documents and emails. Airparser uses large language models to extract data from forwarded emails, native and scanned PDFs, Word files, images, HTML, CSV and even handwritten text. You describe the fields you need in plain language, and the parser chooses among text LLM, vision LLM and AI OCR engines to return structured results.
Integrations run through Zapier, Make and n8n, plus webhooks and a REST API, with destinations such as Google Sheets, Airtable, Excel, HubSpot, Salesforce, QuickBooks and Notion. Pricing is credit-based, where one credit equals one PDF page, email, image or HTML document. A free trial includes 20 credits, and paid plans start at $33 per month billed annually for 100 credits.
Best for: small teams that want quick, template-free extraction from varied inbound documents.
How To Choose Data Extraction Tools
The right data extraction software depends less on feature lists than on your sources, your volume and who will maintain the setup. Work through these questions before you trial anything.
Is Your Data On The Web Or In Documents?
Start here, because it splits the market in two. Web data extraction tools such as Apify, Diffbot, Firecrawl and Octoparse fetch and parse pages, deal with JavaScript rendering and run on schedules. Document data extraction tools such as Textract, Nanonets, Rossum, Docparser, Parseur and Airparser read files you already have. If you need both, consider whether one vendor covers enough of each side or whether two specialist tools will serve you better. For JavaScript-heavy sites specifically, see our list of web scraping APIs for JavaScript-rendered sites.
Rules, Templates Or AI?
Rule-based and template tools give precise, repeatable output when layouts are stable, but every new layout means new setup. AI data extraction tools handle unfamiliar layouts with a plain-language field description, which is faster to start but needs checking for accuracy on edge cases. Many products now combine both, so test each candidate on a sample of your messiest real inputs, not the clean examples in the demo.
How Will Extracted Data Reach Your Systems?
Extraction is only useful if the output lands where people work. Check native integrations with your ERP, CRM, warehouse or spreadsheet, support for webhooks and APIs, and whether connectors such as Zapier, Make or n8n are included. For AI projects, look for Markdown or JSON output and an MCP server so agents can call the tool directly.
What Will It Cost At Your Real Volume?
Pricing models vary widely: per page, per credit, per compute unit or per seat, with different rates for advanced features such as table or form extraction. Estimate your monthly pages or requests, include retries and failed runs, and price each shortlisted tool at that volume. Free tiers are useful for testing but rarely reflect production costs.
Are You Allowed To Extract This Data?
For web sources, stick to publicly available data, respect robots.txt, site terms and sensible rate limits, and prefer an official API when one exists. Our beginner’s guide to web scraping covers these basics. For documents, check where files are processed and stored, and whether the vendor offers the certifications your industry needs, such as SOC 2, ISO 27001 or HIPAA.
Who Will Maintain The Setup?
Websites change their markup and suppliers change their invoice layouts, so every extraction setup needs upkeep. Ask who will notice a broken job, how quickly it can be fixed and what that costs in staff time. Tools with monitoring, alerts and confidence scores reduce the burden, and AI-based extraction tends to cope better with small layout changes than strict rules do. If nobody on your team can own maintenance, a managed service or a vendor with a paid data service option may cost less overall than a cheaper self-serve tool that quietly fails.
Conclusion
The top data extraction tools in 2026 fall into two camps that are slowly converging. On the web side, Apify offers the widest platform, Diffbot the least maintenance, Firecrawl the cleanest path into AI applications and Octoparse the easiest no-code start. On the document side, Amazon Textract suits AWS pipelines, Nanonets and Rossum automate finance and transactional workflows, and Parseur, Docparser and Airparser give smaller teams fast routes from inbox to spreadsheet.
Pick two or three candidates that match your source type, run them against real samples, and compare accuracy, maintenance effort and cost at your expected volume. For a broader view of the scraping side, our roundup of web scraping APIs and tools is a useful next read, and it pairs well with the document tools above when a project spans both sources.
