Neo4j is a Graph Databases company founded in 2007 and based in San Mateo, United States. It has raised $598.6M in total funding, most recently a Growth in 2024 at a $2B valuation.
| Date | Stage | Amount | Valuation | Lead investors |
|---|---|---|---|---|
| Nov 19, 2024 | Growth | $50M | $2B | Noteus Partners |
| Nov 9, 2021 | Series F | $66M | — |
| Inovia Capital |
| Jun 17, 2021 | Series F | $325M | $2B | Eurazeo |
| Nov 1, 2018 | Series E | $80M | — | One Peak Partners, Morgan Stanley Expansion Capital |
| Nov 10, 2016 | Series D | $36M | — | Greenbridge Partners |
| Jan 15, 2015 | Series C | $20M | — | Creandum, Dawn Capital |
| Nov 2, 2012 | Series B | $11M | — | Sunstone Capital |
| Sep 20, 2011 | Series A | $10.6M | — | Fidelity Growth Partners Europe |
Cloudera expands its enterprise AI ecosystem with Nvidia-based preconfigured applications and deeper partnerships with Neo4j and Mistral AI.
Raymond James Financial describes its use of knowledge graphs to ground AI agents, presented at a Neo4j graph summit.
Imago Platform builds a graph-based knowledge system on Neo4j to answer warranty-policy questions accurately for a lighting and electrical distributor.
Spranic is building a spatial memory layer for physical AI that pairs recorded space changes with knowledge graphs.
BotMan AI uses a knowledge graph in its AI migration pipeline to map dependencies, business logic and data models for legacy application rebuilds.
Flahy applies knowledge graphs to clinical decision support, bringing together data that could inform a patient's next prevention or treatment step.
The company's core native property-graph database, which stores data relationships as first-class citizens rather than reconstructing them through joins. Queried with Cypher, the declarative graph language Neo4j created that became the basis of GQL, the ISO-standard graph query language ratified in 2024. Available self-managed in Community and Enterprise editions.
The fully managed graph-database-as-a-service running on AWS, Google Cloud, and Azure. Aura is Neo4j's fastest-growing line — cloud revenue grew fivefold in the three years to late 2024 — and includes integrated vector search so a single platform can serve both knowledge-graph and embedding retrieval for GraphRAG workloads.
An analytics and ML engine with a library of more than 65 graph algorithms (community detection, centrality, pathfinding, node embeddings) that runs alongside the database. Teams use it for fraud-ring detection, recommendations, supply-chain analysis, and feature engineering that feeds downstream machine-learning models.
A no-code graph visualization and exploration tool that lets analysts and business users navigate, search, and edit the graph visually without writing Cypher, turning knowledge graphs into an interactive canvas for investigation and presentation.