TigerGraph has unveiled next-generation graph and vector hybrid search on a single platform for anomaly detection, fraud and anti-money laundering, personalisation and multimedia matching, according to the GlobeNewswire release of 4 March 2025. The company also released a Community Edition free for production use, with stated limits of 16 CPUs, 200 GB graph storage and 100 GB vector storage plus an AI/ML open-source library.
Vendor benchmarks claimed 5.2× faster vector searches with 23% higher recall than competitors, 22.4× fewer resources and 6× faster indexing — all TigerGraph figures. GraphRAG and graph-enhanced AI are positioned for personalisation and explainability using relationship context expressed in GSQL, with Python library support for vector operations.
Chief executive Rajeev Shrivastava said the firm continues to deliver “the industry’s fastest, most scalable analytics for AI and machine learning users” and wants the tools in developers’ hands for mission-critical products. The About block names customers including JPMorgan Chase, Intuit, UnitedHealthcare and Unilever. The release followed TigerGraph Savanna, its cloud-native graph platform.
TigerGraph said hybrid queries can be expressed in GSQL so teams combine structured relationship traversals with unstructured similarity search in one request path.