GraphRAG vs Vector RAG: Choosing the Right Retrieval Approach for Enterprises

The article argues that vector search suits semantic similarity lookups and quick setup, while GraphRAG excels at counting, detecting absences, multi-hop reasoning, handling superseded information, and permission-safe retrieval. It explains that vector databases match what looks similar to a query, whereas GraphRAG walks relationships between entities to assemble answers. Most mature production systems combine both, using vectors to find entry points and graph traversal to build the final context. The author recommends testing a team's hardest real user questions to see whether answers sit in single documents or require composition, and warns that teams testing only simple lookups often discover the gap after retrieval is already critical.