Comparison · Build it yourself
Curiosity and Graphwise GraphDB
A reasoning engine, or a working layer
Curiosity gives you a finished context layer; GraphDB gives you a semantic 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.
Graphwise GraphDB
An enterprise RDF graph database from Graphwise, the company Ontotext and Semantic Web Company formed in 2024, supporting OWL 2 profiles and SPARQL. It is widely used for applications built on an ontology in life sciences, publishing and cultural heritage, and is a database and reasoning engine rather than a finished product.
How Curiosity compares
Where the boundary falls
GraphDB is a strong choice where a formal ontology already exists and the organization can maintain it. Curiosity assumes neither and gets to a usable graph sooner.
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
RDF database with OWL 2 profiles
Industrial fit
Built for industrial and enterprise teams
Domains built on an ontology
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. Graphwise GraphDB 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
JavaScript SPARQL functions, Java plugins in the server
Your own interface
Your own front end in C#, deployed from git
Not available
Data model
Typed node and edge schemas you define
RDF ontologies with SHACL validation
Configuration as code
Every change committed to git, pushed to your remote
Repository config files, Docker and Helm
APIs and SDKs
REST API, C# and Python library, connector SDK
RDF4J REST API, SPARQL endpoint, GraphQL endpoints
AI integration
What AI can build and reach
Whether AI can build on it, who reviews the result, and which models it runs. Graphwise GraphDB as its own documentation described it in October 2026.
AI that builds
Sudo drafts endpoints, tools, schemas and screens
Talk to Your Graph agents write SPARQL from questions
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, Gemini, or any Chat Completions endpoint
Models on your servers
Open weights on your servers, or air gapped
Local models via LM Studio or vLLM
Assistant permissions
Built in tools search as the user, per record
Repository access rules apply to the caller
Agent protocols
MCP server and an OpenAI compatible API
MCP server and TTYG tools via OpenAPI
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.
CompareStardog
Curiosity gives you a finished context layer; Stardog gives you a semantic engine to model.
CompareYour data. Your infrastructure.