Comparison · Build it yourself
Curiosity and Stardog
Reasoning power, or time to production
Curiosity gives you a finished context layer; Stardog gives you a semantic engine to model.
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.
Stardog
A semantic knowledge-graph platform built on an RDF triple store with OWL 2 reasoning and data virtualization, supporting SPARQL and fine grained access control, suited to regulated use cases heavy on compliance. It is a platform teams model and build on.
How Curiosity compares
Where the boundary falls
Stardog’s reasoning is more expressive than what Curiosity does, and for some problems that is the right trade. It also assumes a team that can model an ontology and maintain it.
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 triple store with OWL 2 reasoning
Industrial fit
Built for industrial and enterprise teams
Regulated, led by an ontology
Data reach
70+ systems connected, and yours through a connector you write
Virtualised and loaded sources
Residency
EU hosted, or entirely your own infrastructure
Your choice
Customization
What your team can change
Your own code, interface, data model and configuration. Stardog 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
Java extensions: query functions, aggregates, HTTP server
Your own interface
Your own front end in C#, deployed from git
Not available
Data model
Typed node and edge schemas you define
OWL ontologies built in Designer, SHACL constraints
Configuration as code
Every change committed to git, pushed to your remote
Not available
APIs and SDKs
REST API, C# and Python library, connector SDK
HTTP API, GraphQL, Java, Python, JavaScript, .NET clients
AI integration
What AI can build and reach
Whether AI can build on it, who reviews the result, and which models it runs. Stardog as its own documentation described it in October 2026.
AI that builds
Sudo drafts endpoints, tools, schemas and screens
Voicebox writes SPARQL and drafts models in Designer
Review before it applies
Every change a diff a person approves, in a sandbox
Generated SPARQL lands in Studio for editing
Model choice
Any major provider, or an OpenAI compatible endpoint
Anthropic, OpenAI, Azure, Bedrock, Vertex, or OpenAI proxy
Models on your servers
Open weights on your servers, or air gapped
Not available
Assistant permissions
Built in tools search as the user, per record
Not available
Agent protocols
MCP server and an OpenAI compatible API
Stardog Cloud MCP Server exposes Voicebox
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.