Scale AI into production

Neo4j outlines a set of capabilities intended to help organizations scale graph-powered AI applications across large datasets, multiple workloads, and production availability requirements. Virtual Graph, in public preview for Aura customers, enables zero-copy graph querying over Snowflake, Databricks, and BigQuery by translating Cypher into SQL while keeping data under its existing governance, making it suitable for warehouse-latency GraphRAG, exploration, and enrichment workloads. Multiple Databases on AuraDB Business Critical and Virtual Dedicated Cloud allows isolated tenant, environment, project, or workload databases to share an instance’s compute and storage, reducing the operational burden of managing separate instances. AuraDB also offers High Memory configurations of up to 2 TB of RAM and 5 TB of storage for large graph workloads, while Cross-Cluster Database Replication provides active-passive replication across independent clusters for disaster recovery, compliance, migrations, and geographically local reads. Finally, Neo4j Graph Analytics, offered through an early access program for self-managed deployments, separates analytics algorithms from transactional database resources through dedicated, scalable compute, helping prevent intensive graph analysis from affecting production operations.

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