LlamaParse

Category

Coding & Development

Pricing

Credit-based: Free (10K credits/mo), Starter $50/mo (40K credits), Pro $500/mo (400K credits), Enterprise custom; 1,000 credits = $1.25, pages cost 1-45 credits depending on parse tier.

Best for

LlamaParse is a strong fit for engineering teams building RAG systems, document search, or extraction agents that need production-grade parsing of complex PDFs, tables, and scanned files, especially those already using LlamaIndex.

Official site

cloud.llamaindex.ai

Last updated

August 2026

LlamaParse is the flagship document ingestion product inside LlamaCloud, the commercial platform built by LlamaIndex. It takes unstructured or semi-structured files (PDFs, Word docs, PowerPoint, spreadsheets, scanned images, and more) and turns them into clean, structured markdown, plain text, JSON, or annotated PDFs that large language models can actually reason over. Under the hood it offers several parsing tiers: a fast, low-cost text extraction mode with no AI assistance for simple digital-native documents, a 'Cost-Effective' LLM-assisted tier for everyday accuracy, and 'Agentic' and 'Agentic Plus' tiers that use vision-language models and multi-step reasoning to handle dense tables, nested layouts, charts, and low-quality scans. An Auto Mode feature routes each page to the cheapest tier that will still produce accurate output, which LlamaIndex claims can cut credit spend by up to 80% versus always using the top tier. LlamaParse differentiates itself from generic OCR or PDF-to-text libraries by focusing specifically on LLM and RAG use cases: its output preserves document structure, table relationships, and layout context so downstream retrieval and extraction pipelines perform better.

LlamaParse is built and operated by LlamaIndex (originally GPT Index), the company founded by Jerry Liu and Simon Suo in 2023 in San Francisco after they identified data ingestion and indexing as the biggest bottleneck in building reliable LLM applications. LlamaIndex is best known for its widely used open-source Python/TypeScript framework for connecting LLMs to external data, and LlamaParse (alongside LlamaExtract and LlamaCloud Index) forms the commercial, hosted layer built on top of that open-source foundation. The company has raised venture funding from investors including Greylock and positions LlamaCloud as the enterprise document-intelligence layer for use cases in insurance, finance, healthcare, and manufacturing, with named customers such as Jeppesen (a Boeing company). LlamaParse is SOC 2 Type II, HIPAA, and GDPR certified, and offers VPC or hybrid-cloud deployment for enterprises that cannot send documents to a shared SaaS tenant.

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Frequently asked questions