RAG vs Knowledge Graphs
When structured relationships beat semantic similarity, and the build cost that decides it.
The short answer
They are not the same kind of thing, so “RAG or knowledge graph?” is usually the wrong binary. RAG is a retrieval pattern that fetches external context for a language model. A knowledge graph is a structured store of entities and labeled relationships. GraphRAG is RAG that retrieves over a graph — often alongside vectors. If answers need relationship traversal or multi-hop paths, reach for a graph (or GraphRAG). If answers are flat document Q&A over mostly independent passages, stay on vector RAG. The one question that decides it is below. (As of July 2026.)
When should you use RAG vs a knowledge graph?
Every multi-factor comparison buries the decision under four or five axes. It usually reduces to one: does answering require traversing explicit relationships between entities, or is semantic similarity over passages enough?
- Passage similarity / independent documents → vector RAG. “Find the policy paragraph about parental leave” or “What does this runbook say about restarts?” Chunks stand alone; a vector (or hybrid keyword) index is enough. Atlan’s 2026 comparison frames setup as days to weeks for a vector pipeline versus weeks to months of ontology work for a knowledge graph.
- Relationship traversal / multi-hop / audit path → knowledge graph or GraphRAG. “Which suppliers sit two hops upstream of this delayed SKU?” or “Which cases share the same accused across reports?” need edges, not just nearest neighbours. Memgraph (October 2025) puts the same split as retrieving the right data versus retrieving the right connected context. When flat retrieval fails these questions, the failure often shows up as multi-hop retrieval misses.
Ask that first. Most of the time it answers before you reach the table. For the full set of architecture choices, return to RAG decisions.
What’s the difference between RAG and a knowledge graph?
When the deciding question lands in the middle, score both on the factors that matter. This is a directional comparison, not a benchmark — the ratings say which approach the factor favours, and why. No invented latency or dollar races: where a public source exists, it is named; elsewhere the cell stays qualitative.
| Factor | Vector RAG | Knowledge graph / GraphRAG | Why |
|---|---|---|---|
| Multi-hop / relationship questions | Weak | Favoured | Graphs traverse labeled edges; similarity search has no native path unless the whole path sits in one chunk. |
| Flat document Q&A | Favoured | Overkill | Independent FAQs and runbooks do not need an ontology — Towards Data Science (Sarkar, Nov 2025) flags this as the case where GraphRAG is unnecessary. |
| Explainable reasoning path | Weaker | Favoured | A graph path is inspectable; a vector match is a score humans cannot read as a sentence. |
| Upfront build cost | Lower | Higher | Atlan (2026): vector RAG in days–weeks; knowledge graphs weeks–months of entity and relationship design. |
| Keeping it current | Re-index / re-embed | Reconcile entities & edges | Weaviate’s GraphRAG note (2024–2025 blog): community-summary GraphRAG often needs costly reindexing when the corpus moves; chunk RAG updates more cheaply. |
| Global sensemaking over a corpus | Weak | Favoured (GraphRAG) | Edge et al. (2024, Microsoft GraphRAG) target query-focused summarisation via community summaries — not top-k chunks alone. |
Read the first row. If your questions live there — relationship traversal — the rest of the table rarely overturns it. If they do not, the build-cost row usually keeps you on vector RAG. What a graph stores as a retrieval substrate is covered on knowledge graphs for retrieval; popular vector stores for the RAG side include Weaviate, Pinecone and OpenSearch.
Is a knowledge graph the same as GraphRAG?
No — a knowledge graph is the store; GraphRAG is a retrieval pattern that uses that store. A knowledge graph holds entities as nodes and named relationships as edges. GraphRAG is retrieval-augmented generation where the retrieval path walks a knowledge graph — Emil Eifrem (Neo4j, AI Engineer Summit 2024), as quoted in Atlan’s 2026 comparison, defined it as “RAG where on the retrieval path you use a Knowledge Graph… maybe in combination with other technologies like vector search.” Autocomplete harvest for this page (July 2026) surfaces “is knowledge graph and graph rag same” across engines — the SERP routinely collapses the terms. Vector RAG alone is not GraphRAG. How to build and query GraphRAG lives on GraphRAG.
When should you use GraphRAG — or both RAG and a graph?
The binary framing is a trap: production systems often use both, because semantic entry points and relationship expansion are independent levers. Stay on vector RAG for single-hop document questions; add a graph when multi-hop or global-sensemaking queries justify ontology cost. Atlan’s 2026 decision matrix routes single-hop document Q&A to vector RAG with a reranker, and multi-hop or regulated relationship work to hybrid GraphRAG.
GraphRAG is not automatically better. Xiang et al. (2025, arXiv:2506.05690, GraphRAG-Bench) report that recent studies find GraphRAG frequently underperforms vanilla RAG on many real-world tasks: Han et al. (2025), as they summarise, show about 13.4% lower accuracy on Natural Questions, and Zhou et al. (2025) about 2.3× higher average latency, while graph retrieval improved reasoning depth by about 4.5% on HotpotQA multi-hop questions. On the other side, Lettria with AWS (December 2024) reported integrating graph-based structures into RAG improved answer precision by up to 35% over vector-only methods across four sectors in their test — verify that figure on your corpus before you rely on it. Douwe Kiela (Contextual AI), quoted via Atlan (2026), also warns that many stacks labelled “GraphRAG” are closer to hierarchical data augmentation than true graph retrieval. When you do need the architecture, build it from how GraphRAG retrieves over a graph rather than from this decision page.
What are the use cases for RAG and knowledge graphs?
Where the deciding question is obvious, so is the tool:
- Reach for vector RAG: FAQ and documentation assistants, support over mostly independent articles, and any corpus where each document answers alone — Memgraph (2025) and Towards Data Science (Sarkar, 2025) both treat HR-style policy packs as the naive-RAG-enough case.
- Reach for a knowledge graph or GraphRAG: supply-chain and dependency questions, compliance and fraud paths, healthcare or research graphs that link entities across documents, and customer-360 style relationship queries — the use-case lists on Memgraph (2025), IBM’s GraphRAG overview, and Data Science Dojo converge on connected domains rather than flat FAQ search.
If you are still choosing among technique options (fine-tuning, long context, prompts), start from RAG decisions.
Should I use RAG or a knowledge graph?
Ask whether answering requires traversing explicit relationships between entities, or whether semantic similarity over passages is enough. Flat document Q&A and independent docs favour vector RAG — cheaper to stand up (days rather than weeks to months of ontology work, per Atlan’s 2026 comparison). Multi-hop relationship questions, audit paths, and cross-document entity linkage favour a knowledge graph or GraphRAG. That single question decides most cases.
Is a knowledge graph the same as GraphRAG?
No. A knowledge graph is the structured store of entities and labeled relationships. GraphRAG is retrieval-augmented generation that uses a knowledge graph on the retrieval path — often combined with vector search. Emil Eifrem (Neo4j, AI Engineer Summit 2024) framed it as RAG where retrieval uses a knowledge graph, not as a replacement for RAG. Build detail lives on /architectures/graph.
When should I use GraphRAG?
Use GraphRAG when queries need multi-hop relationship reasoning or global sensemaking over a connected corpus, and you can afford ontology design plus ongoing entity curation. Stay on vector RAG for single-hop document questions. Xiang et al. (2025, arXiv:2506.05690) report that recent studies find GraphRAG often underperforms vanilla RAG on many tasks — so justify the graph with your query shape, not the label.
Is GraphRAG always better than traditional RAG?
No. Han et al. (2025), as summarised by Xiang et al. (2025) on GraphRAG-Bench, report about 13.4% lower accuracy for GraphRAG versus vanilla RAG on Natural Questions, and Zhou et al. (2025) about 2.3× higher average latency, while multi-hop suites like HotpotQA can gain reasoning depth. Lettria with AWS (December 2024) reported up to 35% higher answer precision from adding graph structure in their four-sector test — verify on your corpus before you rely on that figure.
Can I use RAG and a knowledge graph together?
Yes — that hybrid is what most production GraphRAG stacks are. Vector retrieval finds semantically relevant entry points; graph traversal expands related entities and paths. Atlan’s 2026 decision matrix routes single-hop document Q&A to vector RAG and multi-hop or regulated relationship questions to hybrid GraphRAG. Neither layer replaces the other when your query mix includes both shapes.