Neo4j provides a native graph database platform that stores data as nodes, relationships, and properties, enabling fast, multi‑hop traversals and real‑time analytics on highly connected data. It offers both self‑managed deployments and the fully managed AuraDB cloud service, along with the Graph Data Science library of 65+ algorithms, Cypher query language, and integration tools for building fraud detection, recommendation, and knowledge‑graph applications.
Funding
$50M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
NPFounders
Product
Problem
Organizations that need to analyze highly connected data often struggle with relational databases, which require costly joins and cannot scale efficiently for deep relationship queries. This limits real‑time insight, hampers fraud detection, recommendation, and knowledge‑graph use cases, and forces complex infrastructure management.
Solution
Neo4j delivers a native graph database platform that stores data as nodes, relationships, and properties, enabling queries that traverse connections in constant time. The platform offers self‑managed deployments and fully managed cloud services (AuraDB) with automatic scaling, high availability, and built‑in security. Integrated graph analytics and the Graph Data Science library provide over 65 pre‑tuned algorithms and machine‑learning pipelines for anomaly detection, recommendation, and network analysis. Neo4j’s Cypher query language and native drivers simplify application development, while tools such as Bloom, Fleet Manager, and Aura Agent extend visualization, operations, and AI integration. Together, these components let enterprises build intelligent applications that leverage the full value of their connected data without the overhead of traditional database architectures.
Target Audience
Primary customers are software developers, data scientists, and enterprise IT teams building fraud detection, recommendation engines, knowledge graphs, and other intelligent applications that require real‑time graph analytics.
Features
- Native property‑graph model with index‑free adjacency for fast, multi‑hop traversals
- Fully managed AuraDB service (Free, Professional, Business Critical) with 99.95% SLA, automatic upgrades, and multi‑cloud deployment
- Graph Data Science library offering 65+ algorithms, graph‑native ML pipelines, and Python client integration
- Cypher declarative query language and native drivers for Java, Python, JavaScript, .NET, and others
- Bloom visual analytics and Aura Agent for AI‑driven knowledge‑graph agents
- Fleet Manager control plane for centralized provisioning, monitoring, and scaling of multiple instances
- Extensive connectors (Kafka, CDC, data‑warehouse, Snowflake, Microsoft Fabric) for seamless data ecosystem integration