Vector Databases vs Knowledge Graphs: How to Pick the Right LLM Storage

A software developer working with a Mumbai-based marketplace client found that their LLM support agent failed to answer relational queries — such as identifying suppliers by zone and return rate — because it relied solely on a vector database. Vector databases store text as numeric embeddings and excel at semantic similarity searches, while knowledge graphs store entities and relationships, enabling exact, multi-condition traversals. The developer has deployed both types in production — including Qdrant, pgvector, and Neo4j — and scored them across criteria like retrieval accuracy, reasoning, cost, and operational complexity. Vector databases rated higher on unstructured text handling, scale, and setup simplicity, whereas knowledge graphs outperformed on relational queries, multi-hop reasoning, and answer explainability. The core takeaway is that the two systems are not interchangeable, and most real-world LLM applications require understanding which type of question they need to answer before choosing a storage paradigm.