Neo4j
A graph database for connected applications, graph analytics, and AI knowledge layers, with managed and self-hosted options.
About Neo4j
Neo4j is a graph database platform built for connected data. It stores entities and relationships natively, so teams can query those connections with Cypher instead of rebuilding them through layers of relational joins. Drivers cover Python, Java, JavaScript, and C#. The documentation includes local and cloud setups, plus Docker and Kubernetes deployments. Neo4j's tools handle graph import, exploration, visualization, management, and monitoring. Graph Data Science adds algorithms and machine learning workflows. AuraDB is the managed cloud option, with upgrades, patches, and maintenance handled as part of the service. Self-managed editions cover community projects and enterprise deployments. This specialization comes with a real adoption cost: teams need to learn graph modeling and Cypher, plan data migration, and decide when Community Edition no longer meets production needs. Enterprise and Infinigraph prices require a sales conversation, which leaves larger buyers without an instant cost estimate.
Best for
Best use cases
Use Cypher to traverse multi-hop relationships when repeated relational joins make queries difficult to maintain.
Run graph algorithms over connected data to identify patterns, communities, paths, or influential entities.
Deploy AuraDB for managed operations, or use Docker and Kubernetes for infrastructure-controlled environments.
Compare before choosing
Community Edition lowers the entry cost, while enterprise availability, controls, and support require a commercial agreement.
Native graph storage fits relationship-heavy queries better than a general relational model, but it adds graph-specific modeling and Cypher skills.
AuraDB reduces database administration, while self-managed editions preserve infrastructure control and add operational responsibility.
Features
Connected application data
Build applications that traverse relationships directly with a native property graph model and Cypher queries.
Graph analytics and machine learning
Run graph algorithms and machine learning workflows with Neo4j Graph Data Science.
Knowledge and AI context
Create a connected knowledge layer that gives AI systems structured context about entities and relationships.
Pros
- Native graph storage and Cypher make multi-hop relationship queries direct and easier to express.
- Community Edition provides a free entry point for development and community-supported deployments.
- AuraDB handles upgrades, patches, maintenance, and offers a managed cloud operating model.
Cons
- Teams must learn graph modeling and Cypher, which adds onboarding work beyond familiar relational databases.
- Enterprise Edition and Infinigraph pricing requires a sales conversation, reducing upfront cost visibility.
- Self-managed production deployments add infrastructure and operational responsibility for the adopting team.
Pricing Plans
Community Edition
- Native graph processing and storage
- ACID transactions
- Property graph model
- Cypher graph query language
Enterprise Edition
- Includes Community Edition features
- Unlimited horizontal read scaling with replication
- Fine-grained access controls
- High availability and advanced manageability
- Change Data Capture
Infinigraph Edition
- Includes Enterprise Edition features
- Unlimited horizontal scaling with automatic sharding
- Graph Data Science included with license
* Pricing information is for reference only. Please refer to the latest announcement on Neo4j's official website.