LlamaIndex is the specialized heavy machinery of the AI world, built explicitly for the "R" in RAG (Retrieval-Augmented Generation). While generalist frameworks like LangChain focus on chaining prompts and managing agents, LlamaIndex focuses obsessively on the ingestion, indexing, and retrieval of private data. It doesn't just connect to your data; it structures it for LLM consumption.
The open-source library is free and MIT-licensed. You only pay for your underlying infrastructure (OpenAI, Pinecone, etc.). However, their managed service, LlamaCloud, which handles complex document parsing (PDF tables, weird formatting), operates on a credit system. You get 10k free credits/month (roughly 3,000–10,000 pages depending on complexity), with a $50/month starter tier for 50k credits. For a company processing 500 complex financial PDFs a day, the managed parsing is a godsend compared to wrestling with open-source PDF parsers, even if it adds a monthly line item.
Technically, the abstractions are deep. You don't just "search"; you configure query engines, retrievers, and re-rankers. The VectorStoreIndex is the default starting point, but the real power lies in their hierarchical indices and "router" query engines that can decide whether to do a semantic search or a SQL query. The Python SDK is the gold standard here; the TypeScript version exists but often lags in advanced features.
The downside is the "magic" abstraction tax. In an effort to make RAG easy, LlamaIndex wraps so much logic that debugging a bad retrieval can feel like peeling an onion with infinite layers. Documentation has improved but often fragments between the core library and the rapid fire of new feature releases. It is overkill for a simple chatbot. But if you are building a legal discovery tool or a financial analyst agent where data accuracy is non-negotiable, this is the industry standard.
Skip it if you just need to paste a text file into a prompt. Use it if your engineering problem is primarily about data quality and retrieval accuracy rather than just prompt orchestration.
Pricing
The core framework is open-source and free. The cost comes from the optional LlamaCloud (managed parsing/storage).
Free Tier: 10,000 credits/month (approx. 3k-10k pages depending on parsing depth). This is enough for prototyping but not production traffic. Starter ($50/mo): 50,000 credits + 5 users. Usage: Simple text parsing costs ~1 credit/page, but "premium" extraction (tables/images) can hit 10+ credits/page.
Hidden Cost: The real cost is usually your Vector DB (Pinecone/Weaviate) and LLM API fees, which LlamaIndex orchestrates but doesn't bill for. Self-hosting the parsing logic is free but engineering-heavy.
Technical Verdict
The Python SDK is production-grade, though the API surface area is massive. You can start with 5 lines of code, but mastering the QueryEngine configurations takes weeks. Latency is purely dependent on your vector store and LLM choices. The TypeScript SDK is viable but clearly secondary. Documentation is extensive but often outdated due to release velocity.
Quick Start
# pip install llama-index
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
# Load data and build index
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
# Query the index
query_engine = index.as_query_engine()
response = query_engine.query("What is the revenue growth?")
print(response)Watch Out
- The TypeScript SDK features lag significantly behind the Python SDK.
- LlamaCloud parsing credits burn fast if you enable 'premium' mode for tables.
- Debugging retrieval issues can be hard due to deep nesting of abstractions.
- Major version updates (e.g., 0.9 to 0.10) have introduced breaking API changes.