Graph databases & knowledge graphs · Neo4j, Inc.

Neo4j

Native property-graph database using Cypher, the language behind the new ISO GQL standard, for relationship-heavy analytics.

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Neo4j is a native property-graph database: nodes and relationships are stored directly rather than emulated with foreign-key joins, so multi-hop traversals (fraud rings, recommendation paths, supply-chain lineage) stay fast as data grows. Queries are written in Cypher, the pattern-matching language Neo4j originated and later contributed to the new ISO GQL graph-query standard. Community Edition is free for self-hosted use under a source-available community license; Enterprise Edition adds clustering, fine-grained security and unlimited scaling under a commercial license. AuraDB is the fully managed cloud service, billed per GB of memory per month above a free tier. The ecosystem includes the Graph Data Science library (PageRank, community detection, similarity algorithms) and Bloom for visual exploration, making it the most mature option for teams already comfortable with Cypher.

At a glance

Vendor Neo4j, Inc.
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source No
Best for Teams doing fraud detection, recommendations or knowledge graphs who want the most mature Cypher/graph tooling ecosystem.

Pricing

AuraDB is billed per GB/month above a free tier; self-managed Community edition is free, Enterprise requires a quote.

Plan Price Notes
AuraDB Free $0 learn and explore graphs, capped instance size
AuraDB Professional from $65/GB/month minimum 1GB cluster, hourly metering available
AuraDB Business Critical from $146/GB/month minimum 2GB cluster, 99.95% uptime SLA
AuraDB Virtual Dedicated Cloud custom quote dedicated infrastructure, VPC isolation

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Cypher query language (basis of ISO GQL)
  • Native graph storage and index-free adjacency
  • Graph Data Science library of algorithms
  • Vector and full-text search alongside graph queries
  • Causal clustering for high availability
  • Bloom for no-code graph visualization
  • Bulk import and ETL tooling
  • Role-based access control

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

TigerGraph Massively parallel native graph database with its own GSQL language, built for deep multi-hop analytics at scale. ArangoDB Multi-model database combining graph, document and key/value storage under one engine and one query language, AQL. Amazon Neptune Fully managed AWS graph database supporting both property-graph (Gremlin, openCypher) and RDF (SPARQL) query models. Memgraph In-memory, Cypher-compatible property-graph database built in C++ for low-latency graph analytics and GraphRAG.

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Terms to know

Data modeling The practice of organizing data into entities, relationships and schemas that make it accurate and efficient to query. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Fraud detection Identifying transactions, claims, or behavior that are likely fraudulent, using rules, statistics, or machine learning. Role-based access control (RBAC) Granting data or system permissions based on a user's assigned role rather than configuring access for each person individually. Community detection Identifying groups of nodes in a network that are more densely connected to each other than to the rest of the graph.

Related guides

Graph and network analytics, explained How relationships between things become data — the algorithms that find structure in a network and where graph databases fit. How to choose a graph database Decide property graph vs RDF first, then managed vs self-hosted and how the engine scales — the query language follows from those, not the other way round.