A graph database stores data as nodes and relationships. Nodes are things such as people, products or accounts, and relationships (also called edges) describe how they connect. Queries follow those connections directly, so questions about how things relate stay fast where the equivalent relational joins would get slow and hard to write.
How does a graph database store data
Each node carries properties, for example a customer with a name and a country. Each relationship has a type and a direction, such as "bought" or "reports to", and can carry properties too. The database stores relationships as first-class data, so following one does not require searching a table for matching keys.
How is it different from a relational database
A relational database stores rows in tables and connects them through keys and joins. A graph database stores the connections themselves.
| Relational database | Graph database | |
|---|---|---|
| Core structure | Tables and rows | Nodes and relationships |
| Strong at | Structured records, reporting, transactions | Connected data, path and pattern queries |
| Linking data | Joins on keys | Direct relationships |
| Query style | SQL | Graph languages such as Cypher or Gremlin |
| Schema | Fixed, changes need migrations | Flexible |
| Typical scale pain | Many-hop joins | Wide aggregations across all records |
When does a graph database make sense
Use one when the questions you ask are about relationships and paths: who is connected to whom, what is three steps away, which accounts share a device. Common cases are fraud detection, recommendations, network and access modelling, supply chains and knowledge graphs. If a query needs five or six joins and gets slower with each, the data is probably graph shaped.
When should you stay with a relational database
For most products, stay relational. Orders, invoices, users and inventory fit tables well, and SQL tooling, reporting and engineering experience are far more widespread. Adding a graph database adds a system to run, secure and keep in sync. Many relational databases now handle recursive queries well enough for modest relationship needs, so test that first.
How do graphs relate to AI retrieval
A vector database finds text that is similar in meaning, which is the base of RAG. A graph adds explicit relationships between the entities in your documents. Combining them lets a system answer questions that span several documents, such as how two projects are linked through a shared supplier. This pattern is often called GraphRAG. It costs extra effort to build and maintain the graph, so it pays off when relationship questions are common in your use case.
What are common graph use cases
Fraud teams link accounts that share a phone number, device or address and look for rings that a row-by-row check misses. Recommendation systems follow paths such as people who bought this also viewed that. Identity and access systems model who can reach what through groups and roles. Knowledge graphs connect entities extracted from documents so that questions can be answered across them.
What this means for your engineering team
The choice of database shapes hiring and maintenance for years. A team that picks a graph database needs someone who has modelled relationships in production, written the query language and planned for backups and scaling. Ask candidates to explain why they would not use a graph database for a given problem, since restraint is a better sign of experience than enthusiasm.
In our vetting, candidates work in a real codebase for two sixty-minute sessions, one with AI tools off and one with their own agents on. If you need data modelling skills on your team, see data engineers, from €5,600 a month for a full-time engineer.
Short answers
Is a graph database faster than a relational database?
For queries that follow many relationships, usually yes. For aggregations over all records or simple lookups, a relational database is often as fast or faster.
What is Cypher?
Cypher is a query language for graph databases that describes patterns of nodes and relationships in a readable form.
Can I use a graph database with an LLM?
Yes. Teams use graphs to give models structured relationships alongside vector search, a pattern known as GraphRAG.
Do I need a graph database for my startup?
Probably not at first. A relational database covers most products, and you can add a graph later if relationship queries become central.
