GraphRAG vs Vector RAG: An Enterprise Decision Framework

A developer outlined a decision framework for choosing between vector RAG and GraphRAG in enterprise retrieval systems, arguing that vector search suits semantic lookup while GraphRAG is required for aggregation, counting over transitive closures, absence queries, and supersession. The framework recommends testing the ten hardest real user questions to determine whether a single source document contains the answer, and notes that mature systems typically use vector search for entry-point discovery and graph traversal for context assembly.

Vector search wins on semantic similarity and setup speed. GraphRAG wins on counting, absence, multi-hop questions, supersession, and permission-safe retrieval. Most production systems eventually use both. The real decision is which one assembles the final context. A vector database stores text as embeddings and retrieves whatever is closest to your query's embedding. It answers "what looks like this." GraphRAG stores entities as nodes and their relationships as traversable edges, and retrieves by walking those relationships. It answers "what is connected to this, and how." These are genuinely different capabilities, not competing implementations of the same idea. Which is why the right question is rarely "which one is better" and almost always "which one does this specific question need." Three conditions, and you need all three: When all three hold, vector search is the right tool and GraphRAG is overengineering. Internal enterprise search over operational data where access is genuinely scoped and policies genuinely change almost never satisfies all three at once. When the answer requires composition rather than retrieval : when no single document contains it, and the system has to assemble it by walking relationships between several entities. Four question shapes that vector search structurally cannot handle: | Question shape | Example | Why embeddings fail | |---|---|---| | Aggregation | "Who should review this change?" | It is an aggregation over contribution history, not a lookup | | Counting over a closure | "How many open items are blocked on this team?" | A count over a transitive closure | | Absence | "Which accounts have no assigned owner?" | Absence has no embedding to match against at all | | Supersession | "What is the current policy?" | A retired chunk still embeds as highly similar | That third row is the one that surprises people. You cannot embed a thing that is not there. A query for missing data has no semantic neighbor to find the only way to answer it is to traverse the structure and observe the gap. Yes, and most mature systems do. The common pattern is vector search for entry-point discovery, graph traversal for context assembly. The embedding finds the relevant starting node; the graph walks outward from it to collect the facts that actually answer the question. What matters is which layer assembles the final context handed to the model. If it is the vector store, you have vector RAG with a graph attached. If it is the graph, you have GraphRAG with semantic entry points — and the behavior under multi-hop questions is completely different. Take your ten hardest real user questions. Not the demo questions the ones that made someone open a ticket. For each one, ask: does a single source document contain this answer? The failure mode we see most often is a team that ran this test only against lookup questions, shipped vector RAG, and discovered the composition questions six months later after the retrieval layer was load-bearing. We tell clients honestly which one they actually need. If you are making this call, talk to our team https://www.agenticgiants.com/contact .