Vector databases and knowledge graphs are two different approaches to storing information in ways that AI systems can use effectively. Vector databases store embeddings - the numerical representations of text, images, or other content - and enable fast similarity search. When a RAG system needs to find documents relevant to a user's question, it converts the question into a vector and searches the database for the closest matches. This is search based on meaning rather than keywords, which is why it can find relevant results even when the exact words don't match. Knowledge graphs take a different approach: they store information as explicit relationships between entities. "London is the capital of the United Kingdom" becomes a structured connection between three concepts. Knowledge graphs excel at representing facts, hierarchies, and complex relationships in ways that are precise and queryable. They're particularly valuable for domains where accuracy and logical consistency matter more than fuzzy similarity. Many production AI systems use both: vector databases for flexible semantic search and knowledge graphs for structured factual knowledge. For businesses, the choice between them depends on the use case - for fuzzy or meaning-based search, vector databases are the starting point; if factual queries and relationship traversal are needed, knowledge graphs are the better fit.