TigerGraph Unveils Hybrid Search for AI-Powered Graph Database, Launches Free Community Edition

TigerGraph Unveils Next Generation Hybrid Search to its Graph Database to Power AI at Scale; Also Introduces a Game-Changing Community Edition

Enter your email to get Benzinga's ultimate morning update: The PreMarket Activity Newsletter REDWOOD CITY, Calif., March 04, 2025 (GLOBE NEWSWIRE) -- TigerGraph, the enterprise AI infrastructure and graph database leader, today announced its next generation graph and vector hybrid search delivering the industry's most advanced solution for detecting data anomalies through sophisticated pattern analysis, identifying critical deviations from expected norms, and providing actionable recommendations. The integration of graph and vector search on a single high-performance, scalable platform offers businesses a comprehensive solution for developing significantly more accurate AI systems for fraud and anti-money laundering detection, real-time personalized recommendations, and image and multimedia matches among others. Simultaneously, TigerGraph is releasing a Community Edition of its graph database that offers significant compute power and storage capacity. TigerGraph is revolutionizing vector search with unmatched speed, accuracy, and scalability - essential components for advanced AI and ML systems. By leveraging graphs to represent proprietary local knowledge and real-time data, including their interrelationships, graph-enhanced AI and GraphRAG deliver superior personalization and explainability. This multi-modal approach simplifies the design and operation of complex AI use cases, dramatically reducing infrastructure complexity and code requirements while providing enterprise-grade security, access controls, and reliability. TigerGraph vector search benefits include: 5.2x faster vector searches with 23% higher recall than competitors to rapidly uncover the most similar items while using 22.4x fewer resources and reducing operational costs.6x faster indexing - blazing fast loading and automatic incremental updating of search indexes, ensuring accurate searches even with large datasets and rapid changes.Advanced hybrid search of structured and unstructured data - enhances discoverability and contextually rich understanding for ML and AI systems, significantly improving their analytical capabilities.Rich relationship modeling - delivers support for complex relationships between entities and creates sophisticated knowledge graphs.Integrated query language - express hybrid graph+vector queries in GSQL, harmonically achieve structured and unstructured query composition. Our Python library also supports vector database operations. TigerGraph's Community Edition is the most powerful graph database that's free to use, even in production: 16 CPUs of compute power for significantly higher performance.200 GB graph storage and 100 GB of vector storage to enable AI-driven applications.Extensive AI/ML open-source library, simplifying the development of graph + vector applications, including GraphRAG.GSQL, OpenCypher, and ISO GQL for the widest and most powerful query language support. "We're continuing to lead the way in delivering the industry's fastest, most scalable analytics for AI and machine learning users," said Rajeev Shrivastava, CEO of TigerGraph. "The engineer in me is excited to put these solutions directly into the hands of developers who are building mission critical, AI dependent products that improve their customers' lives." Start using Hybrid Search with Community Edition today. Read TigerGraph's blog and SIGMOD 2025 paper for a technical deep dive. On the heels of the release of TigerGraph Savanna, the most innovative cloud native graph database platform for supercharging AI systems, TigerGraph continues to lead the market as the enterprise-grade graph database for customers that need to quickly discover relationships, analyze complex patterns, and make mission critical decisions. About TigerGraph TigerGraph, the enterprise AI infrastructure and graph database leader, delivers massively parallel storage and computation that scales independently and without size limits, to meet the changing workloads and growing data volumes required for crucial business needs and AI adoption within companies. By providing visibility into the multidimensional data connections and relationships, TigerGraph has become a trusted partner to leading companies including JPMC, Intuit, United Healthcare, and Unilever successfully solving fraud detection, entity resolution, customer 360, supply chain management, and many other problems. Headquartered in Silicon Valley, California and with offices around the world TigerGraph is backed by Tiger Global Management, Softbank, Susquehanna International Group (SIG), Oceanpine Capital, Celesta Capital, Nvidia, Blackopal Ventures, and Qiming Venture Partners. Contact: paige.leidig@tigergraph.com Market News and Data brought to you by Benzinga APIs

TigerGraph adds hybrid search capability to its graph database, releases free edition - SiliconANGLE

TigerGraph adds hybrid search capability to its graph database, releases free edition TigerGraph Inc. is upgrading its graph database with a hybrid search capability designed to power artificial intelligence applications. The Redwood City, California-based startup debuted the feature today alongside a new free edition of the database. A graph is a data structure that holds not only business records but also information about how those records are connected to one another. For example, it can point out if two purchase logs were produced by the same e-commerce website. The ability to track such data relationships is necessary for many analytics projects. TigerGraph provides a popular graph database of the same name. The platform can store graphs with up to trillions of edges, data points that show how different pieces of information are connected to one another. The company counts Microsoft Corp., JPMorgan Chase and other major enterprises among its customers. TigerGraph's new hybrid search capability combines its existing graph-based tools for finding connections between data points with a vector search capability. According to the company, the enhancements will enable AI applications powered by its database to retrieve information for users more reliably. Vectors are data structures that can track relationships between different snippets of information, much like a graph. However, they often store different kinds of relationships. Whereas a graph might highlight that two business documents belong to the same department, a vector stores semantic similarities such as the fact the documents discuss the same topic. According to TigerGraph, combining graph and vector search allows AI models to retrieve information more reliably than would be possible using only one or the other. That helps improve the quality of prompt responses. One of the tasks that TigerGraph promises to ease with the new feature is knowledge base navigation. A company could use hybrid search to build an AI chatbot that helps workers find internal documents. Such a chatbot can use graphs to find all the files created by a user's business unit, then leverage vectors to find the file most relevant to the user's search query. The feature also lends itself to other tasks. According to TigerGraph, the ability to find patterns in interconnected datasets makes it easier to generate shopping recommendations and identify supply chain inefficiencies. The company says that its database can complete some queries five times faster than competing platforms. Developers can access the hybrid search feature via GSQL, the custom query language that TigerGraph ships with its database. The syntax is similar to SQL, which makes it relatively simple to learn. There's also a library that enables software teams to access data using the Python programming language.