The 3 Best Reducto Alternatives in 2026

Docling is an open-source Python toolkit, MIT licensed with about 69,700 GitHub stars, that started in IBM Research Zurich and is now hosted by the LF AI & Data Foundation. It parses PDFs (layout, reading order, tables, formulas), DOCX, PPTX, XLSX, HTML, EPUB, images, emails and audio into a unified document model, then exports Markdown, HTML or JSON. It can run small vision-language models such as GraniteDocling for hard pages, plus OCR for scans, all on your own machine, which suits sensitive or air-gapped data. There is no paid tier: it ships as a library, a CLI, an API server and an MCP server, with integrations for LangChain, LlamaIndex, CrewAI and Haystack. The cost is your own compute; a GPU speeds up the vision models considerably but is not required for plain PDFs. Individual models keep their own licences. As of October 2026.

• Free and MIT licensed, with no per-page bill
• Documents never leave your infrastructure
• Very wide format support, including audio

LlamaParse is the document parsing service in LlamaCloud, from the team behind the LlamaIndex framework. It turns PDFs, Office files, spreadsheets, images and audio into clean Markdown or JSON, with tables and layout preserved, ready to chunk and embed. Each document runs through one of four tiers: Fast for plain text, Cost-effective for most business documents, and Agentic and Agentic Plus, which use LLMs and vision models for complex layouts, charts and scans. The same credits pay for Extract (structured fields from a schema), Classify and Split. Credits cost $1.25 per 1,000. Parsing a page costs 1 credit on Fast, 3 on Cost-effective, 10 on Agentic and 45 on Agentic Plus, so 1,000 pages run from about $1.25 to $56. Free gives 10,000 credits a month, Starter is $50 a month with 40,000 included, and Pro is $500 with 400,000. Re-parsing the same file within 48 hours is free. LiteParse is a separate open-source parser that runs without a vision model. As of October 2026.

• Four tiers let you pay 1 credit for plain pages and more only for hard ones
• Built into LlamaIndex, with Extract and Classify on the same credits
• Monthly free credits

Unstructured started as an open-source Python library, Apache-2.0 with about 15,600 GitHub stars, for partitioning PDFs, Office files, HTML, emails and images into typed elements such as titles, tables and list items. The hosted platform wraps that into full pipelines: pull files from S3, Google Drive, SharePoint or Confluence, parse them, chunk, enrich, embed, and write the result to a vector database or warehouse on a schedule. That connector-and-pipeline layer is the main reason teams pick it over a parse-only API. The platform gives 10,000 free pages with no card, then charges $0.015 a page ($15 per 1,000) with all features included. Business is custom and adds multi-user accounts, dedicated support, and deployment as a dedicated instance, in your VPC or on bare metal. The open-source library is free to run anywhere but has fewer models and no hosted pipelines. As of October 2026.

• Open-source library you can run anywhere
• Source and destination connectors for full ingestion pipelines
• Simple flat per-page price