Comparison · Build it yourself
Curiosity and TigerGraph
Graph analytics at scale, or the graph already built
Curiosity gives you a finished context layer; TigerGraph gives you a scalable graph database.
Your data · your environment
Who each tool is for
Different problems, honestly stated
Curiosity
For industrial and regulated teams whose knowledge sits in engineering systems and structured records, where the answer depends on how those records connect, and where the deployment has to run on their own infrastructure.
TigerGraph
A distributed graph database queried with GSQL and openCypher, built for analytics over large graphs, with vector search for AI use cases. It is infrastructure for teams building graph applications.
How Curiosity compares
Where the boundary falls
TigerGraph is designed for deep queries over very large graphs. Curiosity is about getting the graph built at all: connected, enriched, kept current and permissioned.
A finished layer, not a database
A graph store gives you somewhere to put the data. Curiosity ships the connectors, the enrichment, the retrieval and the permissions on top of it.
Weeks, not a modeling program
Building a context layer on raw graph infrastructure is a project measured in quarters and staffed with ontology expertise.
Search and AI included
Hybrid retrieval and model routing come with the system rather than being the next two things to integrate.
Side by side
Feature comparison
The same five questions, answered for both.
Deployment
Cloud or self-hosted, ready to use
Self-managed or cloud
Data model
Managed context layer and knowledge graph
Distributed property graph
Industrial fit
Built for industrial and enterprise teams
Graph analytics
Data reach
70+ systems connected, and yours through a connector you write
Whatever you load
Residency
EU hosted, or entirely your own infrastructure
Your choice
Customization
What your team can change
Your own code, interface, data model and configuration. TigerGraph as its own documentation described it in October 2026. Where it says Not available, the vendor documents no such capability.
Your own code
C# endpoints, AI tools and tasks, compiled in Studio
GSQL queries and C++ functions in the database
Your own interface
Your own front end in C#, deployed from git
Insights no code dashboards and apps from widgets
Data model
Typed node and edge schemas you define
Vertex and edge types in a GSQL schema
Configuration as code
Every change committed to git, pushed to your remote
GSQL scripts as files, UDFs read from GitHub
APIs and SDKs
REST API, C# and Python library, connector SDK
REST API, pyTigerGraph Python library
AI integration
What AI can build and reach
Whether AI can build on it, who reviews the result, and which models it runs. TigerGraph as its own documentation described it in October 2026.
AI that builds
Sudo drafts endpoints, tools, schemas and screens
MCP server lets coding agents write schemas and GSQL
Review before it applies
Every change a diff a person approves, in a sandbox
Not available
Model choice
Any major provider, or an OpenAI compatible endpoint
OpenAI, Azure, Vertex, Bedrock, Groq, Ollama and more
Models on your servers
Open weights on your servers, or air gapped
Self hosted GraphRAG with local models via Ollama
Assistant permissions
Built in tools search as the user, per record
GraphRAG runs as the signed in database user
Agent protocols
MCP server and an OpenAI compatible API
MCP server, GraphRAG agents call MCP servers
At Airbus, we developed the next generation of customer support AI assistance tools with Curiosity, enhancing our technical support’s ability to access and navigate vast data from multiple sources, including millions of documents.
Services Innovation Team
Airline Services at Airbus
Build it yourself
Other comparisons in this group
Neo4j
Curiosity gives you a finished context layer; Neo4j gives you a graph database.
CompareAmazon Neptune
Curiosity gives you a finished context layer; Neptune gives you a managed graph store.
CompareGraphwise GraphDB
Curiosity gives you a finished context layer; GraphDB gives you a semantic database.
CompareYour data. Your infrastructure.