Knowledge Graph vs. Vector Database for RAG: When Each One Wins
Pick by question shape. Similarity questions want vectors; path questions want a graph; most real systems ask both kinds.
The honest answer is that this is not a versus. Vectors and graphs solve different retrieval problems, most production systems end up with both, and the useful question is which one should handle which class of question in your system.
When does a vector database win?
When the question is answered by text that exists somewhere, and the hard part is finding it.
Vector search is very good at fuzzy recall over unstructured prose. It tolerates paraphrase, handles synonyms you never enumerated, and degrades gracefully when the phrasing is unlike anything in the corpus. Setup cost is genuinely low: chunk, embed, index, query. You can be useful in an afternoon, and for a large class of internal search and question-answering, that is the whole project.
It also wins on breadth. If your material is a decade of support tickets, policy documents and meeting notes with no stable entity structure, a vector index gets value out of that corpus in a way a graph will not without substantial modelling work you may never recoup.
When does a knowledge graph win?
When the answer is not written down anywhere and has to be assembled by following relationships.
Graphs win on multi-hop questions, where the result depends on a path across several entities. They win on precision, because a traversal either connects or does not, whereas similarity always returns its best guess and never tells you the best guess was poor. And they win on explainability: the path is the audit trail, so the system can show its reasoning rather than assert relevance.
They also win wherever the same real-world thing appears in several systems under different identifiers. Entity resolution is a graph-shaped problem, and no amount of embedding quality substitutes for having decided that these three records are one customer.
How do you choose between them?
By the shape of the question, not the shape of the vendor pitch.
| If the question is | Use |
|---|---|
| answered by a passage that exists | vector retrieval |
| about paraphrase, synonyms, loose recall | vector retrieval |
| a path across several entities | graph traversal |
| dependent on identity resolution | graph traversal |
| subject to audit or explanation | graph traversal |
| high-volume and latency-sensitive over prose | vector retrieval |
Practitioners route around vendor comparison pages on this topic for a reason, and the reason is that most of them conclude in favour of whatever the publisher sells. Take the table above as a genuine recommendation in both directions: if your questions are similarity questions, a graph is an expensive way to answer them, and you should not build one.
What do most production systems actually do?
Both, with the graph handling structure and identity and the vector index handling recall over text. The graph resolves who and what; the index finds the passage. Hybrid is not a hedge here, it is the shape of the problem.
The part neither one solves on its own is authority. Retrieval finds material; it does not decide which version of a definition the company stands behind. That is a governance question, and it is what context management addresses. If you want the mechanics of a graph that carries rules and processes as well as entities, see what a context graph is, and GraphRAG for the retrieval pattern itself.
