Graph databases & knowledge graphs · TigerGraph, Inc.

TigerGraph

Massively parallel native graph database with its own GSQL language, built for deep multi-hop analytics at scale.

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TigerGraph is a property-graph database built around a massively parallel processing (MPP) engine, aimed at queries that traverse 10 or more hops deep — the kind relational joins and many single-node graph databases struggle with. It uses GSQL, a SQL-like language with native support for parallel graph traversal and user-defined functions for custom algorithms. Savanna is TigerGraph's fully managed cloud platform, priced per GB of graph data per month with elastic storage billed separately; a free self-managed Community edition covers single-server deployments up to 300GB, while Enterprise adds high availability and disaster recovery under a custom contract. GraphStudio provides a visual schema and query-building IDE, and a machine-learning workbench layers GNN-style feature engineering on top of the graph.

At a glance

Vendor TigerGraph, Inc.
Pricing model Usage-based
Free tier Yes
Deployment Cloud, Self-hosted
Open source No
Best for Enterprises needing deep multi-hop graph analytics at scale, such as fraud rings or complex supply-chain networks.

Pricing

Savanna cloud is billed per GB of graph data per month plus elastic storage; self-managed Community edition is free, Enterprise is quoted.

Plan Price Notes
TigerGraph Community Free single server, self-managed, up to 300GB storage
Savanna from $45/GB/month 24-7 service, US/Tier 1 region, up to 512 vCPU/4TB RAM per workspace
Savanna Business Critical from $126/GB/month multi-zone deployment, 99.95% uptime SLA, premium support
TigerGraph Enterprise (self-managed) custom quote high availability, disaster recovery, enterprise security

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • GSQL parallel graph query language
  • Massively parallel processing engine for deep-link traversals
  • User-defined functions for custom graph algorithms
  • GraphStudio visual schema and query IDE
  • Machine learning workbench for GNN feature engineering
  • REST++ API for application integration
  • Horizontal scaling via sharding
  • Automated backups on Savanna

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Neo4j Native property-graph database using Cypher, the language behind the new ISO GQL standard, for relationship-heavy analytics. Amazon Neptune Fully managed AWS graph database supporting both property-graph (Gremlin, openCypher) and RDF (SPARQL) query models. ArangoDB Multi-model database combining graph, document and key/value storage under one engine and one query language, AQL. JanusGraph Open-source distributed property-graph database queried with Gremlin, with pluggable storage and indexing backends.

TigerGraph in the index now

Terms to know

Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Data modeling The practice of organizing data into entities, relationships and schemas that make it accurate and efficient to query. Feature engineering Creating, transforming or selecting the input variables a model is trained on, to make the underlying pattern easier for it to learn. Massively parallel processing An architecture that splits a query across many independent compute nodes, each processing its own data slice. Graph analytics Analyzing data modeled as nodes and edges to study connections, structure, and influence rather than isolated records.

Related guides

Graph and network analytics, explained How relationships between things become data — the algorithms that find structure in a network and where graph databases fit. How to choose a graph database Decide property graph vs RDF first, then managed vs self-hosted and how the engine scales — the query language follows from those, not the other way round.