Last updated: 2026-07-01
LlamaIndex is an agentic document AI framework, MIT licensed and open-source, that connects large language models to enterprise data, APIs, and databases for building RAG applications and autonomous agents. Its paid LlamaCloud add-on, LlamaParse, adds agentic OCR that parses 90+ unstructured file types, including tables, images, and handwriting.
About LlamaIndex
LlamaIndex is an open-source AI data framework, founded in 2023 and backed by $27.5M in funding (a $19M Series A plus $8.5M seed), that helps developers connect large language models to enterprise data such as APIs, PDFs, databases, and internal documents. It is released under the MIT license and available in Python and TypeScript. Its managed LlamaCloud service adds LlamaParse, an agentic OCR engine for unstructured documents such as scanned tables, embedded images, and handwritten notes, returning structured markdown or JSON with page citations. LlamaIndex has processed over 1 billion documents in production, with deployments at companies including Salesforce, Rakuten, and Carlyle Group, and supports both cloud SaaS and on-premise or private VPC installs for teams with data-residency requirements.
Pricing
Free tier: the open-source framework (MIT licensed) has no platform fees. LlamaCloud's free tier includes 10,000 credits per month (about 1,000 pages). LlamaParse pricing is credit-based: 1,000 credits equal $1.25. Fast tier costs about $1 per 1,000 pages, Cost Effective about $3, Agentic about $15, and Agentic Plus about $12. Starter and Pro subscription tiers have no public pricing; contact sales.
Key Features
- LlamaParse - Agentic OCR: Industry document parsing for 90+ unstructured file types with support for embedded images, complex layouts, multi-page tables, and handwritten notes. Layout-aware parsing preserves document structure with page citations and confidence scores.
- Multi-Tier Parsing Strategy: Four parsing tiers (Fast, Cost Effective, Agentic, Agentic Plus) that trade off speed and cost for accuracy, with optional version pinning for production stability.
- Structured Data Extraction: Schema-based, LLM-powered extraction agents that turn unstructured content into structured insights with confidence scores and page citations for accuracy.
- End-to-End Workflows: Event-driven, async-first workflow engine that orchestrates multi-step AI processes, agents, and document pipelines with stateful pause/resume and error correction.
- Enterprise-Grade RAG Pipeline: Intelligent chunking and embedding with precision indexing, optimized for production accuracy and relevance in retrieval-augmented generation applications.
- 150+ Data Connectors via LlamaHub: Pre-built connectors for 150+ data sources including S3, Google Drive, SharePoint, Notion, Slack, and SQL databases, plus 300+ additional integration packages for Python and TypeScript.
- 40+ LLM and Vector Database Integrations: Native support for 40+ LLM providers such as OpenAI, Anthropic, and Google Gemini, plus 40+ vector databases including Pinecone, Weaviate, and Qdrant.
Pros
- Parsing quality holds up on messy real-world PDFs, such as scanned tables and handwriting, where many plain OCR tools fall back to raw text
- One framework covers parsing, indexing, and agent deployment, so teams do not need to stitch together three separate tools for a RAG pipeline
- SOC 2 Type II certification and on-premise or private VPC options make it usable for regulated industries like finance and legal, not just prototyping
- Large, active open-source project with 39,000+ GitHub stars, so most integration issues already have a documented fix or example
- Proven in production at large enterprises, including Fortune 500 companies like Salesforce and Rakuten
Cons
- Credit-based LlamaParse pricing can be hard to predict at scale since cost depends on document complexity, not just page count
- Struggles more than specialized document-parsing competitors like Reducto on deeply nested or multi-column layouts
- LlamaCloud's managed features are still maturing, with a learning curve for advanced or non-standard customization
- Cloud-hosted LlamaParse needs an internet connection; fully offline processing requires self-hosting the open-source components instead
Frequently Asked Questions
How much does LlamaIndex cost in 2026?
The core LlamaIndex framework is free, open-source software, so you only pay for the LLM API calls and vector database hosting you choose to use with it. LlamaCloud's LlamaParse service is credit-based: about $1 per 1,000 pages on the Fast tier, up to $15 per 1,000 pages on the Agentic tier for complex documents. Agentic Plus costs about $12 per 1,000 pages, roughly 50% less than the Agentic tier at similar accuracy. Starter and Pro subscription plans are not publicly priced; you have to contact sales.
Is LlamaIndex free to use?
Yes: the open-source LlamaIndex framework is completely free under the MIT license, with no platform fees. LlamaCloud adds a free tier of 10,000 credits per month, enough for roughly 1,000 pages of document parsing through LlamaParse. Usage beyond that requires buying additional credits.
What are the best alternatives to LlamaIndex?
LangChain is the most common alternative for teams that need broader multi-step agent orchestration beyond just document parsing and retrieval. Zerox is a simpler, free alternative for basic PDF-to-markdown conversion if you do not need LlamaCloud's tiered agentic OCR. Many teams still pair LlamaIndex's data layer with LangChain's agent logic rather than picking just one.
How does LlamaIndex compare to LangChain in 2026?
LlamaIndex focuses on data ingestion, indexing, and RAG pipelines, so it typically needs less setup code for document-heavy applications than LangChain. LangChain is the broader framework, built for multi-step agent orchestration and tool use beyond data retrieval. Production teams often run both together, using LlamaIndex for the data pipeline and LangChain for agent logic.
How do you get started with LlamaIndex?
Install the LlamaIndex Python or TypeScript package, then follow the official starter tutorial to load a document and query it with an LLM such as OpenAI or a local model through Ollama. Most developers have a basic RAG example working within minutes using the starter template. For LlamaParse document parsing, sign up separately for a LlamaCloud API key.