TigerGraph DB

TigerGraph is a distributed native graph database designed for large-scale analytics and machine learning on highly connected data. It uses its own query language, GSQL, which supports both real-time transactional queries and batch analytical workloads within the same database. Its architecture is built for horizontal scaling across clusters, targeting datasets and query volumes that exceed what single-node graph databases handle efficiently. Core capabilities include distributed graph storage and computation, in-database graph algorithm execution, hybrid graph and vector search, and native support for graph machine learning and embedding workflows. It supports multi-hop pattern matching across billions of nodes and edges with low latency, and includes a loading framework for ingesting data from relational databases, data lakes, and streaming sources. It is available as a self-managed deployment or as a managed cloud service, with a Community Edition released in 2025 offering significant compute and storage capacity at no cost. An Enterprise tier adds clustering, high availability, role-based access control, and SLA support. TigerGraph is used primarily by large enterprises running fraud detection, anti-money laundering, customer 360 and recommendation, supply chain analysis, cybersecurity, and knowledge graph workloads. It targets organisations where graph query scale and analytical depth are the primary requirements rather than developer simplicity.