LlamaParse
Hosted document-parsing API from LlamaIndex that turns complex PDFs — tables, charts, figures, handwriting — into clean, LLM-ready Markdown for RAG.
LlamaParse is LlamaIndex's hosted parsing API that converts messy documents — PDFs with tables, charts, figures, and handwriting — into clean, structured Markdown for RAG. It is layout-aware, supports 130+ file types and 100+ languages, offers four cost-vs-accuracy tiers from Fast up to Agentic Plus, and plugs straight into LlamaIndex ingestion.
LlamaParse is the document-ingestion service from LlamaIndex, built to solve the unglamorous but decisive first step of RAG: getting clean, structured text out of messy source files. It is a hosted API — you send a PDF, PowerPoint, Word doc, spreadsheet, or image and get back LLM-ready Markdown that preserves headings, reading order, and (crucially) tables. In February 2026 LlamaIndex began renaming its LlamaCloud platform to LlamaParse, which now groups parsing with Extract, Classify, Split, and Index services.
The differentiator is complex-PDF and table accuracy. Ordinary text extraction scrambles multi-column pages and collapses tables into unaligned tokens that wreck retrieval downstream. LlamaParse is layout-aware and offers four parsing tiers that trade cost against accuracy — Fast, Cost Effective, Agentic, and Agentic Plus — from fast text-only extraction up to agentic parsing with LLMs or vision-language models. It supports 130+ file types and 100+ languages.
Because it is made by LlamaIndex, it drops directly into the broader LlamaIndex ingestion pipeline — parse, then chunk, embed, and index — but the Markdown it returns is framework-agnostic and works just as well feeding LangChain, a raw vector store, or any other stack. The parsing service itself is proprietary and hosted; the official SDK clients are open-source.
Pricing is freemium: new accounts get a monthly free credit allowance, then usage is metered in credits, with cost per page scaling by the tier you choose. Pair it with a deliberate chunking strategy and read how RAG works to see where parsing fits in the pipeline.
Frequently asked questions
- What is LlamaParse?
- LlamaParse is a hosted document-parsing API from LlamaIndex; in February 2026 LlamaIndex began renaming its wider LlamaCloud platform to LlamaParse. You upload a file via the web UI, REST API, or Python/TypeScript SDK and it returns clean Markdown or structured text — preserving tables, headings, and reading order — so the output is ready to chunk, embed, and retrieve in a RAG pipeline.
- Is LlamaParse free?
- It is freemium. New accounts get 10,000 free credits a month (as of September 2026), after which parsing is metered in credits. Cost per page depends on the tier you pick — basic parsing costs as little as 1 credit per page, while the agentic tiers cost more per page but handle the hardest layouts.
- How is LlamaParse different from plain PDF text extraction?
- Naive extractors flatten complex layouts — they scramble multi-column text and turn tables into unaligned token soup. LlamaParse is layout-aware and offers agentic tiers that parse pages with LLMs or vision-language models, so embedded tables, charts, and figures survive as usable Markdown instead of garbage.
Filed under
document-parsing · pdf · rag · ocr · tables · llamaindex
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