Comparison · Build it yourself
Curiosity and Neo4j
Somewhere to put the graph, or the graph already built
Curiosity gives you a finished context layer; Neo4j gives you a 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.
Neo4j
A widely used property graph database, queried with Cypher, popular with developer teams building graph applications, and now marketing a knowledge layer for AI and agents. It is infrastructure you build on rather than a finished product.
How Curiosity compares
Where the boundary falls
Neo4j is a good database and Curiosity is not competing on storage. The question is who builds the connectors, the enrichment, the permission sync and the retrieval on top, and how long that takes.
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
Property graph database
Industrial fit
Built for industrial and enterprise teams
General purpose
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. Neo4j 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
Cypher, Java procedures on self managed servers
Your own interface
Your own front end in C#, deployed from git
Aura dashboards, no end user app builder
Data model
Typed node and edge schemas you define
Labels, relationship types, constraints, schema optional
Configuration as code
Every change committed to git, pushed to your remote
Aura Terraform provider, Labs beta
APIs and SDKs
REST API, C# and Python library, connector SDK
Bolt drivers, Query API, Aura Admin API
AI integration
What AI can build and reach
Whether AI can build on it, who reviews the result, and which models it runs. Neo4j as its own documentation described it in October 2026.
AI that builds
Sudo drafts endpoints, tools, schemas and screens
Aura Agent and dashboards generated from a prompt
Review before it applies
Every change a diff a person approves, in a sandbox
Agent config editable and testable before external release
Model choice
Any major provider, or an OpenAI compatible endpoint
OpenAI, Azure OpenAI, Vertex AI, Bedrock, more via GraphRAG
Models on your servers
Open weights on your servers, or air gapped
Ollama local models via GraphRAG Python package
Assistant permissions
Built in tools search as the user, per record
MCP signs in with the user's Aura credentials
Agent protocols
MCP server and an OpenAI compatible API
Hosted MCP server, agents exposed as MCP and REST
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
Amazon 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.
CompareStardog
Curiosity gives you a finished context layer; Stardog gives you a semantic engine to model.
CompareYour data. Your infrastructure.