Tigergraph
Graph databases
AI knowledge graph tools
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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- Transportation and logistics
What is Tigergraph
TigerGraph is a native graph database designed to store and query highly connected data using graph models. It is used by data engineering and analytics teams for use cases such as entity resolution, fraud detection, customer 360, network analysis, and building knowledge graphs for AI/ML workloads. The product provides a distributed architecture for parallel graph processing and supports a declarative graph query language (GSQL) along with APIs and connectors for data ingestion and integration. It is available as self-managed software and as a managed cloud service.
Native graph and parallel queries
TigerGraph is built around a native graph storage and execution engine rather than layering graph features on top of a general-purpose database. It supports parallel execution for multi-hop traversals and graph analytics, which is important for relationship-heavy workloads. This design can reduce the need for extensive denormalization or complex join patterns common in non-graph systems. It is well-suited to workloads where query patterns depend on traversing many relationships.
GSQL and graph analytics
The platform includes GSQL for expressing pattern matching, traversals, and graph algorithms in a single environment. It supports common graph analytics approaches (for example, centrality and community-style computations) that are frequently used in risk, recommendation, and knowledge graph scenarios. This can simplify development compared with stitching together separate query and analytics stacks. Teams can operationalize graph queries via APIs for application and analytics consumption.
Deployment options and integrations
TigerGraph offers both self-managed deployment and a managed cloud offering, which supports different operational models. It provides connectors and APIs to ingest data from external systems and to integrate with downstream applications and analytics tools. This helps organizations adopt graph capabilities without replacing all surrounding data infrastructure. The product also supports role-based access controls and operational tooling expected in enterprise deployments.
Learning curve for GSQL
GSQL is powerful but differs from SQL and from other graph query standards, which can increase onboarding time. Teams may need training to model data effectively as a property graph and to write efficient traversal queries. Migration from other graph technologies can require query rewrites and rethinking data modeling. This can slow initial time-to-value for organizations without prior graph expertise.
Operational complexity at scale
Running a distributed graph database can require careful capacity planning, partitioning considerations, and performance tuning. High-ingest or high-concurrency workloads may require iterative tuning of schema, indexes, and query patterns. Organizations that choose self-managed deployments must also handle upgrades, monitoring, and backup/restore processes. Managed service options reduce this burden but can constrain certain infrastructure choices.
Knowledge graph tooling gaps
While TigerGraph is used to build knowledge graphs, it is primarily a graph database rather than a full knowledge graph governance suite. Capabilities such as ontology management, semantic modeling workflows, and enterprise metadata governance may require additional tools and processes. Organizations with strong RDF/OWL or semantic web requirements may need to validate fit carefully. This can add integration work for end-to-end knowledge graph programs.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Savanna Free Trial | $0 per GB/month* (24×7 service, US/Tier 1) | Free trial workspace: up to 32 vCPUs, 256GB RAM per workspace; 1 R/W + 2 read-only workspaces; manual backup; includes Graph & Vector search, AI/ML library, GSQL/Cypher/GQL. |
| Savanna | $45 per GB/month* (24×7 service, US/Tier 1) | Everything in Free Trial plus up to 512 vCPUs and 4 TB RAM per workspace; automatic backups; automatic/rolling upgrades; private cloud deployment options; standard support. |
| Savanna Business Critical | $126 per GB/month* (24×7 service, US/Tier 1) | Business-critical tier with higher SLA/HA options and additional enterprise features; contact sales for full details. |
| TigerGraph Community | Free (download) | Single-server Developer/Community edition; up to 300 GB Graph+Vector storage; includes GSQL, GraphStudio, AI/ML and algorithm libraries. |
| TigerGraph Enterprise | Contact Sales | On-premises Enterprise Edition (subscription) — professional support, no storage limits; pricing by quote. |
Additional usage-based components (official site):
- Storage: $0.025 per GB/month (fully elastic, pay for actual use).
- Compute (Savanna) — example hourly prices by instance size (US/Tier 1 shown on official page):
- TG-00 (2 CPU, 16 GB) — $1.00 per hour
- TG-0 (4 CPU, 32 GB) — $2.00 per hour
- TG-1 (8 CPU, 64 GB) — $4.00 per hour
- TG-2 (16 CPU, 128 GB) — $8.00 per hour
- TG-4 (32 CPU, 256 GB) — $16.00 per hour
- (Full instance table and region price factors listed on TigerGraph Savanna pricing page.)
Notes: pricing is usage-based (storage monthly, compute hourly, plus optional add-ons). Add-ons (e.g., Insights) can be billed as percentage of compute usage; GraphStudio and GraphQL listed as free add-ons. Region price factors apply (e.g., London/Dublin and Frankfurt/Singapore/Sydney/Tokyo multiplier 1.125; Sao Paulo 1.375).
Seller details
TigerGraph, Inc.
Redwood City, California, USA
2012
Private
https://www.tigergraph.com/
https://x.com/tigergraph
https://www.linkedin.com/company/tigergraph/
