Graph data storage and querying
Store connected data in a graph and query relationships, paths, and patterns with Neo4j’s graph database capabilities.
Neo4j Graph Intelligence Platform
Freemium
Neo4j Graph Intelligence Platform connects data as a graph so teams can query relationships, analyze connected data, and build knowledge layers for AI systems. It supports fully managed cloud services and self-managed deployments.
What is Neo4j Graph Intelligence Platform?
Neo4j Graph Intelligence Platform is a graph data platform for storing, querying, and analyzing connected data. It represents entities and their relationships in a graph, allowing teams to work with context, paths, and dependencies that may be difficult to expose in isolated records.
The platform combines graph database, graph analytics, Graph Data Science, and AI knowledge-layer capabilities. Teams can run complex graph queries, apply algorithms and machine-learning workflows, and use graph-based context in GraphRAG or agent applications. Neo4j is available through fully managed cloud services such as AuraDB and through self-managed deployment options.
What can Neo4j Graph Intelligence Platform do?
Graph algorithms and machine learning
Graph Data Science provides algorithms for areas including centrality, node embedding, clustering, similarity, and classification, along with graph-native ML workflows.
Data science development tools
Data science teams can use a native Python client, automated ML pipelines, reusable models, and graph visualization tools to explore connected data and develop analyses.
Data ecosystem connectivity
Graph Data Science supports connectors and extensions for moving data between graph workflows, data platforms, ML libraries, and pipelines, including Apache Spark and Apache Arrow integrations.
Managed and self-managed deployment
Use AuraDB as a fully managed cloud graph database, or deploy Neo4j through self-managed options for on-premises, private-cloud, or public-cloud infrastructure.
AI knowledge-layer workflows
Combine graph data, vector search, and graph data science to provide connected context for GraphRAG and context-aware AI agent workflows.
Use Cases
“Fraud and anomaly detection”
Analyze relationships and behavior across accounts, transactions, applications, or other connected records to investigate fraud and unusual patterns.
“Recommendations and customer experiences”
Use similarities between users, products, behaviors, and other entities to support recommendation engines and more context-aware customer interactions.
“GraphRAG and agent context”
Build a knowledge layer that gives AI systems access to relationships, history, and decisions, helping applications retrieve connected context for more traceable responses.
“Digital twins and network analysis”
Model complex physical or technical systems as connected graphs to examine dependencies, trace impacts, and support operational analysis.
“Data science and operational analysis”
Apply graph algorithms and graph-native machine learning to connected datasets when teams need to identify important entities, unusual behavior, likely links, or relevant paths.
Frequently Asked Questions
What deployment options does Neo4j provide?
Neo4j provides AuraDB as a fully managed cloud graph database service and also offers self-managed graph database options for on-premises, private-cloud, or public-cloud infrastructure.
Who is Neo4j Graph Data Science for?
It is intended for data science teams that need to analyze relationships, develop graph-based predictions, and move graph analytics or machine-learning projects into production.
What can Graph Data Science produce?
Supported workflows include graph queries, pathfinding, graph algorithm results, visualizations, and graph-native machine-learning outputs for tasks such as clustering, similarity, classification, and node embedding.
Can Neo4j be used with AI applications?
Yes. Neo4j presents its graph as a knowledge layer for AI systems and supports GraphRAG and context-aware agent workflows that use connected data, graph capabilities, and vector search.
How can teams start using Neo4j?
Teams can start with AuraDB, including an AuraDB Free option described on the pricing page, or evaluate self-managed offerings and other platform capabilities according to their deployment needs.
Quick Facts
- Category
- Graph database and data platform
- Deployment
- Fully managed cloud and self-managed
- Primary users
- Developers, data scientists, and AI teams
- Core workflows
- Graph querying, analytics, machine learning, and knowledge graphs
- AI capabilities
- Knowledge layers, GraphRAG, and context-aware agent workflows
- Free entry point
- AuraDB Free
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