GraphRAG vs. Vector RAG: When a Knowledge Graph Beats Top-k

Every RAG pipeline starts the same way: chunk the documents, embed the chunks, retrieve top-k by cosine similarity, stuff the context window. It works surprisingly well - right up to the moment your questions stop being about single passages and start being about how facts relate.

This post walks through where vector-only RAG structurally breaks, what a knowledge graph adds, and how to decide which one your workload actually needs.

What vector RAG is good at

Vector retrieval is a similarity engine. If the answer to a question lives in one or two contiguous passages, embeddings will usually find them:

• "What does the cancellation clause in the Meridian contract say?"
• "Summarize the Q3 incident report."
• "Which document mentions ISO 27001?"

These are lookup questions. The evidence is local, and top-k retrieval is cheap, fast, and hard to beat.

Where top-k structurally fails

The failure mode is not "bad embeddings". It is that some questions have answers which do not exist in any single chunk:

Multi-hop questions. "Which suppliers of our German plants are affected by the new export rule?" requires joining supplier -> plant -> country -> regulation. No chunk contains that join; it has to be constructed. Vector search returns fragments about suppliers, fragments about the rule, and leaves the join to the language model - which will happily hallucinate the missing edge.

Aggregation questions. "How many open contracts reference the old payment terms?" is a count over structure, not a similarity match. Top-k literally cannot see past k.

Entity disambiguation. In a large corpus, "Phoenix" is a project, a city, and a server cluster. Embeddings blur them into one fuzzy region; a graph keeps three distinct nodes with distinct relations.

Conflicting facts. Two documents state different termination dates. Vector RAG retrieves whichever chunk is more similar to the phrasing of your question. A graph can hold both statements as separate, sourced claims and surface the conflict itself.

What GraphRAG changes

GraphRAG runs retrieval over an explicit structure: entities and relations extracted from your documents, with every edge traceable to its source text. Practically, that changes three things:

1. Multi-hop becomes traversal, not luck. The join that top-k had to hallucinate is a walk over real edges.
2. Answers carry receipts. Because each node and edge points back to the chunks it was extracted from, an answer can cite its exact evidence. In GCTRL, every Talk to Graph answer traces to sources - that grounding is what makes AI output verifiable instead of merely plausible.
3. The corpus compounds. New documents merge into the same graph. Duplicate entities get resolved instead of accumulating as near-identical chunks that fight each other in the ranking.

The honest cost: you need an extraction step (GCTRL's KEX does this locally), and an entity-resolution step (that is FUSE). That is real compute at ingest time - which is also why we think that compute should never be metered per token. It runs on your hardware; see pricing.

So which one do you need?

A practical rule of thumb:

The last row matters most in practice. The strongest systems are not "graph instead of vectors" but graph-structured retrieval with embedding search over chunks as one of its tools. That hybrid is what GCTRL ships: chunks, entities, and relations in one governed store, retrieved together.

The part most comparisons skip: access control

The moment RAG serves more than one person, "what can this retrieval see" becomes the hard question. Chunk stores make that painful: permissions live outside the data, and a leaked chunk is invisible in an embedding. A graph can carry classification on every node and edge - GCTRL enforces clearance levels (PUBLIC / INTERNAL / CONFIDENTIAL / RESTRICTED) on every read, with scoped access tokens per colleague or agent.

If your RAG roadmap ends at a demo, skip it. If it ends in production with multiple people and agents on one knowledge base, structure and governance are not optional extras - they are the product.

Want the numbers instead of the argument? Our benchmarks publish extraction and retrieval measurements, including the ~0 ms cost of enforcing access control on every query.