Vector Database vs Graph Database vs Knowledge Graphs for AI (2026)

A vector database finds items that are similar in meaning, a graph database stores and traverses explicit connections between things, and a knowledge graph is a model of entities and relationships that is usually stored in a graph database. The first two are database types; the third is a way of organising knowledge, so the comparison is not like for like. Many AI systems use a vector index to find starting points and a graph to follow relationships from there.

We make HyperLake, a sovereign AI operating platform. It is not a vector or graph database itself, and the section on where it fits says exactly how it relates to the tools below.

What is the difference between a vector database, a graph database and a knowledge graph?

The reason for the table is the middle word. A graph database is a storage engine. A knowledge graph is what you put in it and how you structure it. Choosing "graph database or knowledge graph" is usually really choosing whether to model your domain explicitly at all.

How does a vector database work?

A vector database stores embeddings: lists of numbers produced by a model so that similar content lands close together. A query is embedded the same way, and the database returns the nearest stored vectors. For a deeper explanation of indexing and storage, see Vector Databases and Semantic Search Explained.

Here is a small example in SQL using the open-source pgvector extension for PostgreSQL, whose README shows ordering by a distance operator. The three-number vector is for illustration only; real embeddings usually have hundreds or thousands of dimensions.

-- Hypothetical table of support articles with an embedding column
SELECT id, title
FROM articles
ORDER BY embedding <=> '[0.12, -0.40, 0.88]'  -- cosine distance to the query embedding
LIMIT 5;

This returns the five articles whose meaning is closest to the query, even if they share no keywords with it, provided the embeddings capture that similarity. It cannot tell you that article 12 was written by someone who reports to the person who owns product X. That information is not in the vectors.

How does a graph database work?

A graph database stores data as nodes, relationships and properties rather than in tables or documents, according to Neo4j's documentation. In that property graph model, each relationship has a start node, an end node, a type and a direction. Queries describe a pattern to find.

The next example is a hypothetical query in Cypher, Neo4j's query language. It asks which products depend, directly or through up to three links, on a part supplied from a given country.

// Hypothetical data: Product, Part, Supplier, Country nodes
MATCH (p:Product)-[:DEPENDS_ON*1..3]->(part:Part)
      -[:SUPPLIED_BY]->(:Supplier)-[:LOCATED_IN]->(:Country {name: 'Germany'})
RETURN DISTINCT p.name

A vector query would struggle with this because the answer depends on chains of exact relationships, not on how similar two descriptions sound. In a graph, the path itself is the answer, and you can show it to a reviewer.

What is a knowledge graph, and how is it different?

A knowledge graph represents entities (people, products, contracts) and the typed relationships between them, often with an ontology that defines what types and relationships are allowed. A graph database is the usual place to store one, though a knowledge graph can also be exposed through other systems.

The extra layer matters in AI because it gives a model curated facts to reason over. Microsoft's GraphRAG project is one example. It extracts entities, relationships and key claims from a text corpus to build a graph, groups the graph into communities and summarises them, then uses those structures to augment prompts. Its documentation also notes that its basic search mode relies on standard vector search, so the two approaches sit side by side in the same tool.

The cost is effort. Someone has to define the entities, keep them current and resolve duplicates. If your questions are mostly "find the passage that talks about this", a knowledge graph is more work than the question needs.

When should you use a vector database, a graph, or both?

Use this as a starting rule, then test it on your own questions.

• Mostly unstructured text and fuzzy questions: start with a vector index. It is the simplest route to retrieval for RAG. See Vector Storage: How Semantic Search Works.
• Questions about connections, ownership, dependency or lineage: use a graph. Multi-hop questions are what graphs are built for.
• Both kinds of question: use vectors to find candidate entities or passages, then use the graph to expand to related facts. Several tools now offer both in one engine, which is covered below.
• Governance and consistent meaning across teams: a knowledge graph with an agreed ontology helps, but only if someone owns it.

Before adding a graph, check whether better chunking, metadata filters or reranking fix your retrieval problems. A graph is a modelling commitment, not a tuning option.

Which tools are worth knowing?

This list is a starting point, not a ranking. Descriptions come from each project's own public pages and are accurate as of October 2026.

Where others are the better choice: if you only need nearest-neighbour retrieval over a modest corpus and already run PostgreSQL, pgvector avoids a new system. If your core problem is path-finding and relationships, a graph database such as Neo4j is a more natural primary store than a vector database. If you want no database operations at all, a managed service removes that work.

Where does HyperLake fit?

HyperLake does not replace a vector or graph database, and it is not one. It is a platform for assembling, deploying and governing the data, models and applications behind AI in a customer's own environment or a client's. According to HyperLake's product description, the engines it works with follow the workload, and these include Qdrant or Milvus for vectors and graph engines for relationships.

What the platform adds is around the engine. Its stated approach to private AI is to ground models in governed enterprise context, including structured data, documents, vector search and knowledge graphs or ontologies, with policies on data and tool access. People and agents reach data through authenticated, governed services, with policy decisions made by OPA. If you are choosing between vector and graph retrieval, that choice stays a workload decision. HyperLake is relevant if you also need to decide where the chosen engines run and who can query them.

Frequently asked questions

Is a knowledge graph the same as a graph database?

No. A graph database is software that stores nodes and relationships. A knowledge graph is a structured model of entities and their relationships, which is commonly stored in a graph database.

Can a vector database replace a graph database?

Not for relationship questions. A vector database ranks by similarity, so it cannot reliably follow exact chains such as supplier to part to product. It can help find the starting entity for a graph query.

Do I need a graph for RAG?

Not always. Many RAG systems work with a vector index plus good chunking and metadata. Add a graph when questions depend on relationships across documents, and expect to maintain it.

Can one database do both vector and graph search?

Some can. Neo4j documents vector indexes alongside its graph model, and PostgreSQL with pgvector keeps vectors beside relational data. Test whether the combined system meets your latency, scale and query needs.

What is GraphRAG?

GraphRAG is a retrieval approach that builds a knowledge graph from a text corpus and uses it, with community summaries, to augment an LLM's prompts. Microsoft publishes an implementation with documentation.

Related reading

• Vector Databases and Semantic Search Explained
• Vector Storage: How Semantic Search Works
• RAG vs. Fine-Tuning: How to Choose