TigerGraph Unveils Next Generation Hybrid Search to its Graph Database to Power AI at Scale; Also Introduces a Game-Changing Community Edition
Mar 04, 2025 - financialpost.com
TigerGraph has announced the release of its next-generation hybrid search feature for its graph database, designed to enhance AI capabilities at scale. This new solution integrates graph and vector search on a single platform, offering advanced anomaly detection, fraud detection, and personalized recommendations. The company also introduced a Community Edition of its graph database, providing significant compute power and storage capacity for free, even in production environments. The hybrid search offers faster vector searches, advanced indexing, and enhanced data discoverability, making it a powerful tool for AI and machine learning applications.
TigerGraph's Community Edition supports 16 CPUs, 200 GB of graph storage, and 100 GB of vector storage, along with an extensive AI/ML open-source library. The platform supports multiple query languages, including GSQL, OpenCypher, and ISO GQL. TigerGraph aims to simplify the development of AI-driven applications by providing a scalable and efficient infrastructure. The company is backed by major investors and serves leading companies in various industries, helping them solve complex problems like fraud detection and supply chain management.
Key takeaways:
TigerGraph introduces a next-generation hybrid search combining graph and vector search for advanced AI applications.
The new solution offers faster vector searches with higher recall and reduced resource usage, enhancing AI and ML systems.
TigerGraph's Community Edition provides significant compute power and storage for free, supporting AI-driven applications.
The platform supports multiple query languages and offers extensive AI/ML open-source libraries for developers.
