NEO4J GRAPH ANALYTICS
(AVAILABLE NOW IN PREVIEW)

Run graph algorithms
on your own compute

Run graph algorithms on independent compute, surface hidden connections that power smarter decisions and AI apps, and never take the hit on query performance.

Learn more

Overview
Capabilities
Use cases
Customers
Partners
Resources

Improve decisions with  connected data

Explore billions of data points in seconds and uncover hidden connections.

Reduce cost with compute that  matches the job

Scale analytics compute up or down without resizing your database cluster.

Experience reliability
at scale

Run heavy analytics jobs without slowing down your database.

Capabilities

Drive deeper analytic insights using graph analytics on your own compute

Run 65+ prebuilt algorithms

Explore billions of data points in seconds, surface what’s unusual and what’s next, and run 65+ pre-built graph algorithms without rewriting a single query.

Natively integrate with your Neo4j Graph Database

Avoid complex, costly ETLs. Connect directly to your Neo4j database via Apache Arrow Flight and use the same Cypher as Graph Data Science plugin to create subgraphs for analysis.

Scale compute and storage separately

Scale analytics compute independently from your database, add capacity on demand, and pay only for the compute your job actually uses.

Queue analytics jobs

Run analytics jobs at scale with automatic queuing that prevents failures when resources are constrained.

Graph analytics use cases

Build intelligent
fraud detection apps

Look past individual data points to the connections between them. Run graph algorithms on independent compute to uncover fraud rings and patterns that other tools miss, without slowing down your production database.

Enrich GraphRAG with
graph-native signals

Write the algorithm output straight back into the graph that your retrieval layer already reads from. Your GraphRAG system gets richer, graph-native context the moment an algorithm finishes running, no separate pipeline.

Run DS/ML experiments
without disrupting production

Spin up independent compute for exploratory research, run multiple experiments concurrently, and free up your database machine for other intermittent workloads, like vector search.