Neo4j has announced the availability of a new distributed graph architecture designed to run both operational and analytical workloads at over 100TB scale in a self-managed environment.
The new architecture, called Infinigraph, enables organisations to manage transactional and analytical graph workloads within a single system. Infinigraph is aimed at companies needing to combine real-time operations with large-scale data analysis, without fragmenting graph data or duplicating infrastructure, and without compromising on performance. The system maintains full ACID compliance, ensuring consistent, reliable, and recoverable transactions even with billions of relationships and thousands of concurrent queries.
Unified workloads
Neo4j states that Infinigraph removes the separation between transactional databases and analytical systems, a division that often leads to data silos, slower decision-making processes, and increased costs due to the need for complex system integrations.
The demand for larger data scale driven by Generative AI deployments is cited as a key motivator for this development. According to Neo4j, enterprises can now store and utilise tens of millions of embedded vectors directly within their graphs to power solutions such as context-aware assistants and semantic search. Additional use cases highlighted include powering global fraud intelligence, building product graphs for hundreds of millions of SKUs, and conducting compliance analysis across multi-decade datasets, all with real-time access.
Referencing an industry perspective, the 2024 Gartner Magic Quadrant for Cloud Data Management Systems notes: "Gartner continues to see a convergence of operational and analytical systems. This may be through collaboration rather than full integration. This is being achieved by three viable approaches: (1) One database and one copy of data; (2) one database but with two engines, one row-based, one column-based, integrated and synchronised; (3) two or more databases that are designed to synchronise and work together."
Neo4j claims that Infinigraph directly addresses the integration challenge by allowing teams to run both operational and analytical workloads without ETL pipelines, synchronisation delays, or redundant infrastructure. Organisations involved in fraud detection, compliance, and real-time customer recommendation generation can now operate from a consistent source of data without duplication or fragmentation.
Distributed graph scale
Infinigraph uses a sharding approach, which spreads property data across cluster members while retaining the logical integrity of the graph. This means queries remain consistent, and there is no need for application rewrites or manual intervention to achieve scale. The company lists benefits such as high availability, automatic failure recovery, embedding billions of vectors directly in the graph, and high performance at both transactional and analytical scales. Pricing is separated for compute and storage, providing customers with more control over costs and flexible deployment options.
Customers can deploy Neo4j in several ways, choosing between replicated graphs for availability and scalability, federated graphs for querying disconnected datasets, or sharded graphs with Infinigraph for scalable deployments. These architectures can be combined as needed based on organisational use cases.
Infinigraph is currently available as part of Neo4j's Enterprise Edition and is expected to be introduced to AuraDB, its cloud-native platform, in the future.
Customer and analyst perspectives
"At Intuit, we rely on Neo4j to power critical projects across our business, one being our Security Attribution Platform powered by our Open Source project Nodestream. As our data footprint and complexity grows, we need to scale without compromising performance. We're excited about the possibilities that Infinigraph can open up for us."
Chad Cloes, Staff Software Engineer at Intuit, highlighted the company's reliance on Neo4j for core projects and sees Infinigraph as important for scaling while maintaining performance standards.
"Our systems rely on connected insights across identity, ownership, linkage, and compliance data. Running real-time queries while also analysing broader patterns is critical to delivering value. That requires a graph to scale both."
Moheesh Raj, Director of Engineering at Dun & Bradstreet, described the dual demand for real-time and broad-pattern analysis as central to their requirements.
"As GenAI use cases expand exponentially, graph infrastructure has become critical. Neo4j's latest move with Infinigraph is an exciting next step in helping organisations scale their graph foundations horizontally to meet enterprise demands."
Devin Pratt, Research Director at IDC, commented on the importance of scalable graph infrastructure for the growth of GenAI applications across enterprises.
Neo4j's view
"Infinigraph sets a new standard for enterprise graph databases: one system that runs real-time operations and deep analytics together, at full fidelity and massive scale. We're giving builders the power to create intelligent systems that transform data into knowledge, scale without limits, and solve their biggest data challenges - without added complexity or cost."
Sudhir Hasbe, President, Technology at Neo4j, outlined how Infinigraph is intended to provide both scale and efficiency for teams building data-driven applications.
Neo4j is currently used by 84% of the Fortune 100, including companies such as Adobe, BT Group, Novo Nordisk, Uber, and UBS. The company has continued to be recognised in reports from Gartner and Forrester and reported revenues exceeding USD $200 million in late 2024.