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If LlamaIndex's pricing doesn't fit your budget, consider these knowledge agents alternatives:
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Open-source Python framework from deepset for building production LLM pipelines, RAG, and agents.
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Unstructured data platform for GenAI that connects to any source, processes 64+ file types, and outputs clean AI-ready inputs.
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Use LlamaIndex when the main product risk is retrieval quality: how documents become chunks or nodes, how metadata is used, which index and retriever strategy is selected, and how retrieved context is assembled for the model. Use LangChain when the harder problem is broad LLM orchestration, tool calling, chains, and application flow across many external services. Some production systems may use both: LlamaIndex for the data and retrieval layer, LangChain for broader application orchestration.
Not for basic use. The open-source framework can handle many standard document and retrieval workflows with available loaders. LlamaParse is positioned for complex documents such as PDFs with tables, charts, or multi-column layouts, and the hosted pricing page lists a Free plan with 10,000 credits per month. LlamaCloud's managed indices are useful for production deployments that want managed infrastructure.
Start with VectorStoreIndex for most use cases — it's the most common fit for semantic retrieval. Use TreeIndex when you need document summarization. KeywordTableIndex can help with exact keyword matching. KnowledgeGraphIndex can support relationship-based queries. In practice, many applications start with VectorStoreIndex and add more specialized strategies only when evaluation shows they are needed.
LlamaIndex supports document-management patterns for inserting, deleting, and updating documents in indices without necessarily rebuilding everything from scratch. For production, combine this with a document tracking system for your data sources and verify behavior against the specific storage, index, and vector database components you use.
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