- Graph databases at a glance
- Eleven graph databases worth a look
- Where to start for your project
- How graph data works
- Native graph databases versus multi-model platforms
- Questions to ask before you choose a graph database
- Signs your data wants to be a graph
- My plain advice on choosing a graph database
- Frequently asked questions
The best graph databases store data as things and the relationships between them, so questions about connections are quick to answer. Some are open source databases you run yourself, while others are managed cloud services or platforms built for AI and analytics. Eleven options follow in no particular order.
Some questions are awkward to answer with rows and columns, and you'll know them when you see them. Which customers share a delivery address with a known fraudster? Which products are bought by people who also bought this one? Which documents mention the same supplier? When the relationships matter as much as the records, a graph database is often the better fit, and it's usually quicker to query.
The eleven graph databases below are listed alphabetically and in no particular order, and every description comes from the vendor's own website, read this week. If you're working with AI data more broadly, my explainer on what synthetic data is covers a related topic.
Graph databases at a glance
I checked the free plan column on each tool's own website, as of Oct 2026. Yes means the site names a free plan, a free version, a free allowance or an open source version. Trial only means a time-limited trial. Not stated means the pages I read named neither, even where there's a button inviting you to start for free, so check the vendor's page before you sign up.
| Tool | Type | Good for | Free plan |
|---|---|---|---|
| Amazon Neptune | Managed graph database on AWS | Graphs inside an AWS setup | Not stated |
| Arango | Graph, vector, document and search platform | Several data models in one place | Not stated |
| Dgraph | Open source distributed graph database | Real-time use cases at large scale | Yes |
| FalkorDB | Open source graph database for AI agents | GraphRAG and AI agent memory | Yes |
| JanusGraph | Distributed open source graph database | Very large self-hosted graphs | Yes |
| Memgraph | In-memory graph engine | Fast queries held in memory | Yes |
| NebulaGraph | Scalable distributed graph database | Fraud detection across huge graphs | Trial only |
| Neo4j | Graph intelligence platform | A long-established name in graph | Not stated |
| PuppyGraph | Graph query engine over relational data | Querying existing tables as a graph | Yes |
| TigerGraph | Graph database for real-time AI and analytics | Deep analytics on connected data | Trial only |
| Ultipa | AI-native graph database and analytics | Graph analytics with ready-made solutions | Trial only |
Eleven graph databases worth a look
Amazon Neptune
Best for: Teams already building on AWS who want a managed graph database rather than running and patching one themselves.
Arango
Best for: Teams who want graph, vector, document and search data in one platform instead of gluing separate databases together.
Dgraph
Best for: Engineering teams who want an open source, distributed graph database designed to power real-time use cases at terabyte scale.
FalkorDB
Best for: AI developers building GraphRAG or agent memory who want a fast, open source graph database they can run themselves.
JanusGraph
Best for: Engineering teams who want a distributed, open source graph database they can scale massively on their own infrastructure.
Memgraph
Best for: Developers who want an in-memory graph engine with a free Community Edition and the Cypher query language they may already know.
NebulaGraph
Best for: Fraud and risk teams who need to spot suspicious patterns and hidden rings of collusion quickly across very large graphs.
Neo4j
Best for: First-time graph builders who want the most established platform, with free online courses through GraphAcademy.
PuppyGraph
Best for: Data teams who want to query relational data they already have as a graph, without copying it into a new database first.
TigerGraph
Best for: Enterprises that want a graph platform for real-time analytics and AI, with its own GSQL query language plus openCypher support.
Ultipa
Best for: Organisations that want an AI-native graph database bundled with analytics and industry solutions rather than a bare database.
Where to start for your project
- Running on AWS already: Amazon Neptune.
- A first graph project with plenty of learning material: Neo4j.
- Open source and self-hosted at scale: JanusGraph or Dgraph.
- AI agents and GraphRAG: FalkorDB or Memgraph.
- Querying tables you already have as a graph: PuppyGraph.
- Fraud and risk analytics: NebulaGraph or TigerGraph.
How graph data works
The W3C's RDF 1.1 Primer explains that every RDF statement has a subject, a predicate and an object, and because there are three parts, these statements are called triples. A customer bought a product. A supplier ships to a warehouse. That's the shape of graph data.
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Not every graph database uses RDF, but the idea's the same: things, and the relationships between them. If you can sketch your data as dots and lines on a whiteboard, it's probably a graph problem.
Native graph databases versus multi-model platforms
A dedicated graph database focuses on relationships, and it's usually the fastest option for deep connection queries, such as following a chain of links several steps away.
Multi-model platforms keep graph, document, vector and search data together. They suit teams who'd rather run one system than several, even if each part isn't as specialised.
Questions to ask before you choose a graph database
- Which query language does it use, and does my team know it?
- Can I run it myself, or is it cloud only?
- How large will my graph get?
- Does it need to work alongside an existing relational database?
- Will it support AI features such as vector search?
- How easy is it to back up and restore?
Data engineers who have taken graph databases into production may find their idea among the technology topics I accept.
Signs your data wants to be a graph
- Your SQL joins keep getting longer. Try a graph query.
- You care about chains of connections. Model them as edges.
- Fraud hides in shared details. Link the records.
- Your AI tool lacks context. Feed it connected facts.
Do you make a graph database? Tool makers can ask to be considered for a featured place on this list.
My plain advice on choosing a graph database
Start with the question you'd like answered, not the database. Write down three real queries your business needs, then test each shortlisted option against them with a sample of your own data rather than a demo dataset.
If your data already lives in tables, try querying it as a graph before you move anything. You'll learn whether a graph's the right shape for your problem without a migration. For keeping that data tidy, my guide on ways to manage marketing data is a practical companion.
Frequently asked questions
What is a graph database?
A graph database stores data as nodes, which are things, and edges, which are the relationships between them, so connections are quick to query.
Are graph databases free?
Several are open source or offer free editions, and others are managed services with trials. The free plan column above shows what each vendor's site says.
When should I use a graph database instead of SQL?
When the relationships between records matter most, such as fraud rings, recommendations or networks of people and organisations.
What query languages do graph databases use?
Common ones include Cypher, openCypher, Gremlin and GQL, and some vendors have their own language as well.
Can a graph database help with AI?
Many are used for GraphRAG and agent memory, where connected facts give an AI model better context.
Can I query my existing tables as a graph?
Some tools let you query relational data as a graph without copying it into a new database first.
