When do you need GraphRAG instead of plain vector search?

When the answer requires composing facts across entities rather than retrieving one document: questions involving counts, absence, multi hop relationships, or whether something is still current rather than superseded. The practical test: if you can name several genuine multi hop questions your users actually ask, you have a graph problem. If you can't, you have an indexing problem.

Can you use both vector search and GraphRAG together?

Yes, and production systems typically do. The standard architecture uses vector search to find entry points and graph traversal to assemble the structured context around them: vector search is excellent at 'what looks like this,' graph traversal is exact at 'what is connected to this, and how.'

What can't vector search do, no matter how good the embeddings are?

It can't count or aggregate reliably, it can't retrieve absence because a missing thing has no embedding, it can't distinguish a current record from one that was later superseded, and it can't enforce permissions without leaking: because filtering happens after retrieval rather than before it.

How do you decide between the two for a new project?

Answer three questions in order. Can you name several multi hop questions users genuinely ask? If not, use vector search and stop there. Do any answers involve counts, absence, or whether something is still true? If yes, you need a graph regardless of the first answer. Can you faithfully reproduce your source systems' permissions in the new store? If not, don't centralise the content at all: that's a veto, not a tradeoff.

Enterprise Knowledge Graphs

GraphRAG vs Vector RAG: An Enterprise Decision Framework

Q: When is vector search sufficient for an enterprise AI application?

When every user can see the entire corpus (or permissions are coarse enough to express as a single filter), when no answer requires a count or an aggregate, and when stale content is harmless because nothing in the source data ever gets reversed. Public documentation search fits cleanly. Internal enterprise search over operational data almost never fully satisfies all three.

Published 2026-08-31 · Agentic Giants

TL;DR

Agentic Giants' engineers lay out when plain vector search is enough for an enterprise AI application and when it genuinely isn't. 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 decision is which one assembles the final context.

What is the difference between vector search and GraphRAG?

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.” The two capabilities are genuinely different, 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.”

When is vector search sufficient for an enterprise AI application?

Vector search alone holds up when three conditions are all true: your questions are fundamentally document lookups (“find me something about X”), your permission model is coarse enough to express as a single filter or is effectively open, and stale content is harmless because nothing in your source data gets reversed or superseded later. Public documentation search fits this cleanly. Internal enterprise search over operational data (where access is genuinely scoped and policies genuinely change) almost never satisfies all three at once.

When do you need graph traversal instead?

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. “Who should review this change” is an aggregation over contribution history, not a lookup. “How many open items are blocked on this team” is a count over a transitive closure. “Which accounts have no assigned owner” is absence, which has no embedding to match against at all. “What is our current policy on X” requires knowing that an earlier record was superseded by a later one. The practical test: if you can name several genuine multi hop questions your users actually ask, you have a graph problem. If you can't, you have an indexing problem, and adding a graph will cost more than it returns.

Can you use vector search and GraphRAG together?

Yes, and in production most systems eventually do. The standard architecture uses vector search to find entry points (the fuzzy, semantic part of the problem) and then lets typed graph traversal assemble exact, structured context around those entry points. Modern graph databases increasingly support vector indexes, full text indexes, and traversal within the same engine, which lets both phases run against one system rather than requiring an application layer to join results from two separate stores.

How do you decide, in practice?

Three questions, answered in order. First: can you name several multi hop questions your users genuinely ask? If not, use vector search and stop: you don't have a graph problem yet. Second: do any of your answers involve counts, absence, or whether something is still current? If yes, you need a graph, regardless of how the first question came out. Third (and this one is a veto, not a tradeoff): can you faithfully reproduce your source systems' permissions in the new store? If you can't, don't centralise the content at all. Building a system you can't safely scope is worse than building nothing.

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Pillar

GraphRAG vs Vector RAG: An Enterprise Decision Framework

What is the difference between vector search and GraphRAG?

When is vector search sufficient for an enterprise AI application?

When do you need graph traversal instead?

Can you use vector search and GraphRAG together?

How do you decide, in practice?

Keep reading

Enterprise GraphRAG: implementation patterns →

Enterprise knowledge graph ROI →

Neo4j vs vector databases →

GraphRAG implementation →