Knowledge Graphs vs. Vector RAG: The Fundamental Difference

The Problem with "Simple" RAG

RAG (Retrieval Augmented Generation) is the standard way to connect an LLM to external knowledge. Before the model answers, the system retrieves relevant documents and passes them as context.

This works as long as the answer sits inside a single passage. For these questions it does not:

• "Why is the deal with customer Miller stalled?"
• "Which properties have problems similar to Mozart Street?"
• "Who is the notary for the Hamburg deals?"

These need relationship knowledge. They ask about connections spread across several documents, not about one document.

How Vector RAG works

Step 1: Indexing. Documents are split into chunks (say 500 tokens). An embedding model turns each chunk into a vector, a mathematical representation of its meaning.

Step 2: Retrieval. Your question becomes a vector too. The system finds the chunks whose vectors are closest by cosine similarity.

Step 3: Generation. Those chunks go to the LLM as context, and it answers.

Strengths

• Finds semantically similar content, not just keyword matches
• Simple to implement
• Strong when the answer lives in one passage ("What does the contract say about X?")

Weaknesses

• No relationships: "Customer Miller" and "Deal Mozart Street" are separate chunks. The system does not know they are connected
• Lost context: Chunks are isolated from the document they came from
• Chaining is expensive: "Show me all deals from Hamburg customers" takes several steps. Vector RAG can do it through repeated retrieval, but it needs multiple passes
• Silent failure: If the right chunk is not retrieved, nothing signals that. The LLM answers anyway

How Knowledge Graphs work

A knowledge graph stores entities as nodes and relationships as edges:

[Customer: Miller] --INTERESTED_IN--> [Property: Mozart Street]
       |                                      |
       |                                      |
 --HAS_NOTARY-->                       --MISSING_DOCUMENT-->
       |                                      |
       v                                      v
[Notary: Schmidt]                   [Document: Energy certificate]

Traversal instead of search. To find out why the deal stalled, the system follows the edges:

1. Find customer Miller
2. Follow INTERESTED_IN to property Mozart Street
3. Follow MISSING_DOCUMENT to the energy certificate
4. → "The deal is stalled because the energy certificate is missing"

Strengths

• Relationship knowledge: Stores who is connected to whom, and what to what
• Chained questions: "Show me all deals whose customer's notary is in Hamburg"
• Explicit absence: Either the relationship exists or it does not. When it is missing, the system says so instead of guessing
• Traceability: The path through the graph shows how the answer was reached

Weaknesses

• Needs structure: Unstructured text has to be extracted first
• Schema design: The ontology has to be thought through, and changing it later is expensive
• No semantic matching: Finds exact matches, not "similar" concepts
• Weaker at plain lookup: When the answer sits in one paragraph, the graph is a detour

GraphRAG: both methods together

GraphRAG combines a knowledge graph with vector retrieval: extract the entities in the question, traverse the graph for connected facts, compress that subgraph into context, and let the LLM answer from it.

What the measurements actually show

Many articles put a number like "3.4x more accurate than Vector RAG" here. That figure does not hold up, and we removed it from this page. Here is what can be supported.

Classic RAG wins at plain fact retrieval. GraphRAG-Bench compares graph methods and classic RAG on identical tasks. On straightforward fact lookup, classic RAG with reranking scores 60.9, the best graph method 60.1, and Microsoft's GraphRAG in global mode 36.9.

Chained questions reverse the order. On the same data, for questions that must connect several facts: 53.4 for the best graph method against 42.9 for classic RAG. About ten points, on one task type.

The gain depends heavily on the dataset. HippoRAG lifts Recall@5 on 2WikiMultiHopQA from 68.2 to 89.5. On MuSiQue, also multi-hop, only from 49.2 to 52.1. Same method, very different effect.

Structured representation beats raw database querying. Across 43 questions against an insurance schema, GPT-4 answered 16.7% correctly straight from SQL and 54.2% through a knowledge graph representation of the same data. That comparison is text-to-SQL against text-to-SPARQL, not graph against vector search.

Give classic RAG a fair token budget and the gap shrinks. This is the most important objection and it rarely travels with the numbers. Graph methods pull far more context per query. Han et al. gave classic RAG the same budget: on MultiHop-RAG it rises from 65.8 to 71.0, level with the graph method at 71.2. What survives is temporal and comparative questions: 43.7 for budget-matched classic RAG against 49.1 for the graph.

On token usage the common claim is upside down: graph methods generally use more tokens, not fewer. On GraphRAG-Bench, classic RAG uses 879 tokens per query, Microsoft's GraphRAG 38,707 in local mode and 331,375 in global mode. Microsoft's only documented saving is against map-reduce summarization of source texts (9x to 43x), not against vector search.

Treat LLM-judged win rates with care. Zeng et al. tested the standard evaluation protocol by running a method against an identical copy of itself. The protocol declared one copy the winner 90 to 10. Position and length effects manufacture advantages that are not there, and every win rate in this literature, Microsoft's included, inherits that caveat.

One caveat that rarely travels with the numbers: Microsoft scores answer quality with a second LLM as judge, on comprehensiveness, diversity and empowerment. On directness, Vector RAG wins every comparison.

Which one do you need?

Use case	Vector RAG	GraphRAG
Questions about document contents	Strong	Comparable
Looking up single facts	Strong	Somewhat weaker
Questions spanning several steps	Weak	Core strength
CRM/ERP integration	Limited	Native
Traceable answers	Weak	Path visible
Setup complexity	Simple	Higher

The question decides, not the technology

If your users search documents and look up single facts, Vector RAG is enough and it is the cheaper option. A knowledge graph earns its cost when your questions regularly hop across several steps, when you need to trace how an answer was reached, or when a wrong result costs more than a missing one.

For CRM and ERP data that is usually the case, because the value sits in the connections rather than in the individual records. But it is a decision per use case, not a general superiority.

The strongest production evidence comes from LinkedIn. Their support team was split at random, one group with graph-backed access to past tickets and one without. Median handling time per case fell from seven hours to five, a 28.6% reduction. The construction matters: the graph modelled the structure of the tickets and their explicit links, not entities guessed out of prose. A brokerage's case files have that same shape.

Osiris uses Neo4j as its knowledge graph and combines both methods.

→ How Osiris implements GraphRAG