TigerGraph
Enterprise-scale graph database for real-time analytics and AI
About TigerGraph
TigerGraph is a native, distributed graph database and analytics platform designed for enterprise-scale workloads. It delivers sub-100ms query responses on 6+ degree relationship traversals, supports massively parallel processing (MPP), and integrates graph + vector capabilities for AI-powered applications including fraud detection, knowledge graphs, and customer 360 analytics.
Pros & Cons
Pros
- Exceptional query performance — sub-100ms on deep-link traversals (6+ hops)
- Scales to trillions of relationships with massively parallel processing architecture
- Combined graph + vector database enables modern AI/ML and GraphRAG applications
- Free Community Edition with generous 300 GB storage limit
- SQL-like GSQL query language lowers the barrier for SQL-experienced developers
- Extensive pre-built connectors for popular data platforms
Cons
- Steep learning curve for GSQL despite its SQL-like syntax
- Enterprise pricing can be expensive and requires contacting sales
- More complex initial setup compared to competing graph databases
- Limited community ecosystem compared to Neo4j
- Cloud pricing model can be difficult to predict for variable workloads
Key Features
Native Graph Database Engine
Distributed, massively parallel processing (MPP) graph database optimized for deep-link analytics across trillions of relationships
GSQL Query Language
SQL-like graph query language combining procedural programming, parallel processing, and user-defined functions
Graph + Vector Database
Combined graph and vector storage enabling GraphRAG and AI-powered applications with full relationship context
Real-Time Stream Processing
Handles 100M+ updates per machine per hour with real-time ingestion from Kafka, Spark, Snowflake, and more
TigerGraph Savanna (Cloud)
Fully managed cloud-native deployment with independent compute and storage scaling and pay-per-use billing
Pre-Built Starter Kits
Ready-to-use graph solutions for fraud detection, supply chain, cybersecurity, and other common use cases
Enterprise Security
ACID compliance, role-based access control, data encryption at rest and in transit, and high availability
Data Connectors
Pre-built integrations with Kafka, Spark, Snowflake, Databricks, S3, Tableau, and GraphQL support
Pricing
- Single-server deployment
- Up to 300 GB graph + vector storage
- Full GSQL query language
- Docker or Linux deployment
- Community support
- Fully managed cloud service
- Pay-per-use compute billing
- Storage at $0.025/GB/month
- Independent compute and storage scaling
- High availability and disaster recovery
- Enterprise RBAC and security
- Rolling upgrades
- 100TB+ scalability
- Dedicated support
Best For
Fraud Detection & Anti-Money Laundering
Map suspicious transaction patterns across accounts, devices, and locations in real-time to detect fraud
Supply Chain Analytics
Map supplier dependencies 5+ levels deep, identify single points of failure, and build digital twins
Knowledge Graphs & GraphRAG
Connect enterprise data to reveal relationships between entities, powering AI with full context for RAG
Customer 360 & Recommendations
Unify customer data across touchpoints to build comprehensive profiles and power recommendation engines
Network & IT Infrastructure Analysis
Analyze network topology, detect cybersecurity threats, and optimize infrastructure
Similar Tools
Chroma
The open-source AI-native vector database for search and retrieval
CrateDB
Distributed SQL database for real-time analytics at any scale
Embedchain
Create an AI app on your own data in a minute
GraphGPT
Convert unstructured text into knowledge graphs using GPT
InfluxDB
Purpose-built time series database for metrics, events, and real-time analytics
JanusGraph
Scalable, distributed open-source graph database