Knowledge Graph vs Vector Database for AI: Which One Should You Use?
A clear comparison of knowledge graphs and vector databases for AI: data model, retrieval, relationships, updates and cost, and when to combine them.
Quick answer
A vector database finds things that are similar in meaning: it stores embeddings of text, images or records and returns the nearest ones to a query. A knowledge graph finds things that are connected: it stores entities and typed relationships and answers questions by traversing them. Use a vector database when answers live in relevant passages. Use a knowledge graph when answers depend on relationships between entities. Combine them when you need both semantic recall and precise connections.
Neither wins universally. They solve different retrieval problems, and the right choice follows from the questions your users actually ask.
How each one works
A vector database turns content into embeddings, numerical vectors where similar meanings sit close together, and uses approximate nearest-neighbour indexes to find the closest vectors to a query. It is excellent at 'find me passages about late delivery compensation' even when the wording differs. It does not know that a passage is about customer C-102, unless you add that as metadata. See vector databases for AI.
A knowledge graph stores nodes (customers, products, suppliers, contracts) and edges (buys, supplies, covers, reports to), each with properties. Queries follow edges: from a customer to their orders, to the products in them, to the suppliers of those products. It is excellent at precise, multi-step questions and at explaining how an answer was reached. It needs a defined ontology and clean identities to work.
Side-by-side comparison
| Dimension | Vector database | Knowledge graph |
|---|---|---|
| Data model | Vectors plus metadata | Nodes, typed edges, properties |
| Retrieval | Nearest-neighbour similarity | Traversal, pattern matching, lookups |
| Relationships | Implicit, via metadata or co-occurrence | Explicit and typed |
| Similarity | Native strength | Not native; can add embeddings to nodes |
| Multi-hop reasoning | Weak; each hop needs a new search | Native; follow edges |
| Structured data | Possible, but loses structure | Natural fit |
| Unstructured data | Natural fit | Requires extraction into entities and edges |
| Update pattern | Re-embed changed items; model change means re-embedding everything | Update nodes and edges; schema changes need migration |
| Explainability | 'These passages were similar' | 'This path connects A to B' |
| Operational complexity | Lower to start | Higher: ontology, entity resolution, pipelines |
| Typical use cases | Document Q&A, semantic search, recommendations by similarity | Supply chains, fraud rings, product compatibility, org and account structures |
VECTOR: "refund rules for damaged goods"
query ──embed──▶ ● nearest neighbours
├─ passage 12 (0.89)
├─ passage 47 (0.86)
└─ passage 03 (0.81) → similar text
GRAPH: "which customers are affected if supplier S7 stops?"
(Supplier S7) ─supplies─▶ (Part P3) ─used in─▶ (Product X)
└─used in─▶ (Product Y)
(Product X) ◀─ordered─ (Customer A), (Customer B)
(Product Y) ◀─ordered─ (Customer C) → connected entitiesWhen a vector database alone is enough
Most document-based assistants do not need a graph. If users ask questions answered by one or a few passages (policies, manuals, help articles, contracts read one at a time), vector search with good chunking, metadata filters and reranking is usually sufficient. Add keyword search for exact terms such as SKUs and error codes; see hybrid search for RAG. Many teams can also store vectors in an existing database, such as Postgres with pgvector, before adopting a dedicated system.
- Answers live inside individual documents or passages
- Relationships can be handled with metadata filters (customer ID, region, product)
- Wording varies a lot, so semantic matching matters
- The content changes often and must be indexed quickly
- The team needs something working in weeks, not months
When a knowledge graph makes sense
A graph earns its cost when the questions are about structure. 'Which of our customers buy products that contain this recalled component?' 'Who approves contracts for this subsidiary?' 'Which accessories are compatible with this model and in stock in this region?' Each requires following several relationships across systems. Vector search can retrieve documents mentioning these things but cannot reliably assemble the chain.
- Questions require two or more hops across entities
- Precision matters more than recall (compliance, safety, finance)
- Users need to see how an answer was derived
- Data comes from several systems that share entities
- You already have, or will invest in, entity resolution and an ontology
When to combine them
Hybrid designs are common and practical. Three patterns cover most cases:
| Pattern | How it works | Good for |
|---|---|---|
| Vector first, graph expand | Semantic search finds relevant passages or entities; the graph adds related entities and facts | Questions phrased loosely that need connected context |
| Graph first, vector rank | The graph narrows candidates by relationships; vector search ranks text within them | Scoped questions ('in this customer's contracts, what says...') |
| GraphRAG-style summaries | An LLM extracts entities and relationships from documents, builds a graph and community summaries for global questions | Themes across large document collections |
Worth noting
Microsoft's GraphRAG is one well-documented approach to the third pattern, building a graph and summaries from text with an LLM. It adds indexing cost, so evaluate it on your own questions first. See our guide to GraphRAG.
Update patterns and operating costs
Vector stores update by re-embedding changed content, which is simple per item but means a full re-embed when you change embedding models. Freshness depends on how quickly changes reach the index. Graphs update by changing nodes and edges, which requires pipelines that resolve identities and keep relationships consistent; a wrong merge can connect unrelated entities. Graphs built by LLM extraction (as in GraphRAG) also carry extraction errors and indexing cost. Plan for these operating costs before choosing, and see data freshness for AI for how to keep either one current.
A decision framework
| If most questions are... | Start with |
|---|---|
| 'What does this document or policy say about X?' | Vector database (plus keyword search) |
| 'Find similar items, tickets or products' | Vector database |
| 'How is A connected to B?' or 'Who or what is affected by X?' | Knowledge graph |
| 'How much or how many?' over business data | Semantic layer, not either of these |
| Loosely phrased questions that need connected facts | Both: vector first, graph expand |
| Broad themes across thousands of documents | Evaluate GraphRAG-style summaries |
Common mistakes
Building a knowledge graph because it sounds more sophisticated, without questions that need it, is the most expensive mistake on this list. The opposite mistake is forcing relational questions through vector search and blaming the model when it misses links. Other mistakes: skipping entity resolution, so the graph has three nodes for one customer; using numeric questions as a reason for either store, when a semantic layer is the right tool; and not evaluating retrieval on real questions before and after a change.
Choosing retrieval architecture for an AI application?
ZSpace Labs builds RAG, graph and hybrid retrieval systems and evaluates them against your real questions. See AI automation.
Conclusion
Vector databases and knowledge graphs answer different questions. Vector search is the default for document-grounded AI because it is quick to build and handles varied wording. Knowledge graphs are worth their cost when relationships are the answer. Many mature systems combine them. Write down your users' real questions, classify them by type, and let that decide the architecture.
Common questions.
A vector database stores embeddings and finds items that are similar in meaning. A knowledge graph stores entities and typed relationships and answers questions by following those relationships. One is built for similarity, the other for connections.