A data framework for LLM applications focused on ingesting, structuring, and querying private or domain-specific data using RAG and agentic pipelines.
- Best-in-class RAG (Retrieval Augmented Generation) tooling
- 150+ data connectors for ingesting from any source
- LlamaParse handles complex PDF and document parsing accurately
- Supports agentic workflows on top of indexed data
- Primarily data and retrieval focused — less suited for pure agent orchestration
- Rapid API changes can break production code between versions
- LlamaCloud paid tiers can be expensive for high-volume document processing
| Free tier | ✓ Free tier |
| Pricing model | open_source |
| Features | |
| API | ✓ Available Docs ↗ |
| Pricing Plans | Open SourceFreeFull framework, self-hosted, MIT license LlamaCloud Free$0/moManaged parsing and indexing, 1,000 pages/month LlamaCloud Pro$97/mo10,000 pages/month, faster processing, support EnterpriseCustomUnlimited, on-prem, SLA, dedicated support |
| Platforms | |
| Integrations | OpenAI, Anthropic, Google Gemini, Hugging Face, Pinecone, Weaviate, Qdrant, MongoDB, Notion, Google Drive, Slack, GitHub |
| Homepage | https://www.llamaindex.ai |
AI Commentary
LlamaIndex is the leading framework for building RAG (Retrieval Augmented Generation) systems, providing a comprehensive set of tools for ingesting documents from diverse sources, parsing and chunking them intelligently, indexing them in vector stores, and querying them with LLMs. Its LlamaParse service has become a popular choice for extracting structured data from complex PDFs, tables, and documents that standard parsers handle poorly. While LlamaIndex also supports agentic workflows, its primary strength and developer mindshare is in the data pipeline and retrieval side of AI applications. LlamaCloud extends the open-source framework with managed infrastructure for teams that prefer not to self-host.