When is vector RAG enough, and when do you actually need GraphRAG?

GraphRAG is having a moment, and that is exactly why most teams reach for it too early.

It is genuinely powerful — and genuinely expensive, in build time, in query latency, and in the ongoing cost of keeping a knowledge graph in step with your data. Reaching for it when plain vector RAG would already answer the question is one of the most common over-engineering mistakes in production AI right now.

So here is the honest decision rule, from building both. It is not "GraphRAG is better." It is "GraphRAG is better for a specific shape of question, and if your questions are not that shape, you are paying for a capability you will never use."

What each one is actually good at

• Vector RAG retrieves the chunks most similar to your question and hands them to the model. It is fast, cheap, and excellent when the answer lives in a passage or two of text. Most question-answering over documents is this shape.
• GraphRAG builds a graph of entities and the relationships between them, and traverses that graph to assemble an answer. It shines when the answer is not in any single chunk but in the connections — multi-hop reasoning across entities, "how does X relate to Y through Z", global questions that need to see the whole corpus at once.

The difference is not quality in the abstract. It is whether your answer is a passage or a path.

The decision rule: is your answer a passage, or a path?

Ask these five questions about your actual queries, not your hopes for them:

1. Do your questions have the answer in one place? If "what is our refund window for enterprise customers" is answered by one clause in one document, vector RAG is enough. Do not build a graph to retrieve a paragraph.
2. Do they require multi-hop reasoning? "Which suppliers are exposed to the component that failed in the Q3 recall" requires linking recall → component → suppliers across sources. That is a path, and that is GraphRAG's job.
3. Are they global, not local? "What are the main themes across all 4,000 incident reports" cannot be answered by the top-k most-similar chunks — it needs the whole corpus summarised structurally. Vector RAG will confidently give you a partial answer drawn from a handful of chunks.
4. Does your data have rich, meaningful relationships worth modelling? Contracts, org structures, financial holdings, supply chains — yes. A pile of independent FAQ articles — no. No relationships, no reason for a graph.
5. Will the graph stay current? A graph that drifts out of sync with the source is worse than no graph. If your data changes constantly and you cannot keep the graph fresh, that cost lands on every answer.

If most of your answers are passages → vector RAG, and add a re-ranker before you add a graph. If they are paths, global, or relationship-heavy → that is where GraphRAG earns its cost.

The honest middle: hybrid, and the cheaper upgrade first

Most real systems are not pure either way, and the biggest practical mistake is skipping the cheap upgrade and jumping to the expensive one.

Before GraphRAG, the highest-return move on a vector system is almost always better retrieval, not a graph: a re-ranker over the top candidates, and a semantic ranker rather than raw cosine similarity. That recovers most of the "vector RAG is missing things" pain at a fraction of GraphRAG's cost. Only when you have done that and the remaining failures are genuinely multi-hop or global is the graph the right next spend.

And when you do build it, hybrid usually beats pure GraphRAG: vector retrieval for the passage-shaped questions it already handles, graph traversal for the path-shaped ones, routed by the question. Build the graph for the questions that need it, not for all of them.

The deeper comparison

This piece is the decision rule; the full architectural comparison — where each breaks, what the crossover looks like at corpus scale, and how the two combine in production — is in the pillar: [RAG vs GraphRAG](/blogs/rag-vs-graphrag). If retrieval is feeding an agent that has to remember across a long task, that is a different axis again — see [agent memory architectures](/blogs/mem0-vs-graphiti-agent-memory).

If you are weighing this for a real system and want a straight read rather than a vendor's default answer, tell us the shape of your data and your queries — the honest answer is often "you do not need the graph yet."