LlamaIndex: AI Agents for Document OCR + Workflows
LlamaIndex is the world's most accurate agentic OCR and document processing platform. Parse, extract, index and retrieve 1B+ documents with enterprise-grade accuracy.
LlamaIndex is an AI data framework and cloud platform that helps developers build production-ready agentic applications over enterprise data. It combines an open-source RAG framework with LlamaParse, an advanced document processing service using agentic OCR to parse complex documents with 90+ file type support. LlamaIndex connects large language models to diverse data sources—APIs, PDFs, databases—enabling teams to build knowledge assistants, autonomous agents, and document automation workflows. Available in Python and TypeScript with 300+ integrations, 1B+ documents processed, and proven adoption by Salesforce, Rakuten, and Fortune 500 companies.
Pricing
Free tier: Open-source framework (MIT licensed) with no platform fees; LlamaCloud free tier includes 10,000 credits/month (~1,000 pages). LlamaParse uses credit system: 1,000 credits = $1.25. Fast tier costs ~$1/1,000 pages; Cost Effective ~$3/1,000 pages; Agentic ~$15/1,000 pages; Agentic Plus ~$12/1,000 pages. No public pricing for Starter/Pro subscription tiers (contact sales required).
Frequently Asked Questions
What is LlamaIndex and what does it do?
LlamaIndex is a comprehensive AI platform that connects large language models (LLMs) to your enterprise data. It provides an open-source framework for building RAG (Retrieval-Augmented Generation) applications and knowledge assistants, plus managed cloud services including LlamaParse for advanced document processing. The platform helps you build AI agents that can understand, extract, and reason over unstructured documents, databases, and APIs.
What is LlamaParse and how does it differ from the framework?
LlamaParse is LlamaIndex's enterprise document parsing service. While the open-source framework provides RAG and agent building tools, LlamaParse specializes in handling complex, unstructured documents with agentic OCR. It parses 90+ file types including PDFs with tables, images, handwriting, and complex layouts—then outputs clean markdown, JSON, or structured data with page citations.
Is LlamaIndex free to use?
The core LlamaIndex framework is free and open-source under the MIT license. You only pay for underlying LLM API calls and vector database hosting. LlamaParse has a free tier with 10,000 credits per month (~1,000 pages), and paid tiers starting at ~$1 per 1,000 pages for the Fast tier. Advanced parsing tiers cost $3-$15 per 1,000 pages depending on accuracy requirements.
Which LLMs and vector databases does LlamaIndex support?
LlamaIndex integrates with 40+ LLM providers including OpenAI (GPT-4, GPT-3.5), Anthropic Claude, Google Gemini, Mistral, Groq, and local models via Ollama. For vector databases, it supports Pinecone, Weaviate, Chroma, Qdrant, Milvus, pgvector, and others. The platform also connects to 160+ data sources via LlamaHub integrations.
What are the four LlamaParse tiers and when should I use each?
Fast ($1/1K pages): Quick text extraction from simple documents without LLM processing. Cost Effective ($3/1K pages): Balanced accuracy using LLM reasoning—recommended default. Agentic ($15/1K pages): High accuracy with intelligent reasoning for complex layouts. Agentic Plus ($12/1K pages): Maximum accuracy with 50% cost savings versus Agentic tier for complex financial, legal, and scientific documents.
Is LlamaIndex production-ready? What security certifications does it have?
Yes, LlamaIndex is battle-tested and production-ready with proven adoption by Fortune 500 companies like Salesforce and Rakuten. LlamaParse is SOC 2 Type II certified. For enterprise deployments, LlamaIndex offers both SaaS and private VPC deployment options ensuring data never leaves your tenant. The framework supports on-premise deployment for organizations with strict data residency requirements.
How does LlamaIndex compare to LangChain?
LlamaIndex is specialized for data ingestion, indexing, and RAG workflows, making it simpler and more focused for document processing tasks. LangChain is broader, better for complex agent orchestration and tool use. Many production teams use both: LlamaIndex for the data pipeline and LangChain for agent logic. LlamaIndex generally requires less boilerplate code for RAG applications.