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LlamaIndex Framework
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FAQ

Vector Database

  1. Do I need to use a vector database?
  2. What’s the difference between the vector databases?

LlamaIndex provides a in-memory vector database allowing you to run it locally, when you have a large amount of documents vector databases provides more features and better scalability and less memory constraints depending of your hardware.


2. What’s the difference between the vector databases?
Section titled “2. What’s the difference between the vector databases?”

To check the difference between the vector databases, you can check at Vector Store Options & Feature Support.


Note for AI agents: this documentation is built for programmatic access. - Overview of all docs: https://developers.llamaindex.ai/llms.txt - Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters. - A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/