Comparison · Build it yourself
Curiosity and Elasticsearch
A toolkit, or a system
Curiosity ships as one system; Elasticsearch is a search engine to build on.
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.
Elasticsearch
A flexible search engine with vector search and strong control over indexing, self-managed or in Elastic Cloud. It suits engineering teams that want to design and operate their own search layer. Elastic retired its packaged Workplace Search in version 9, so the search experience on Elastic is now something you build.
How Curiosity compares
Where the boundary falls
Elasticsearch is an excellent engine, and if your team wants to own the search layer it is a reasonable choice. Curiosity is the finished layer, including the connectors, permissions and graph you would otherwise build around it.
The parts you would build
Connectors, incremental sync, a typed graph, hybrid retrieval and an interface come with the system rather than being the next quarter of work.
Permissions at query time
Access is synced from the source systems and enforced on every query, down to the record. In a framework that is code your team writes and maintains.
Your code still fits
Custom endpoints, AI tools and agents are written against the graph in C# or Python, so you keep the control a framework gives you.
Side by side
Feature comparison
The same five questions, answered for both.
Deployment
Cloud or self-hosted, ready to use
Self-managed or Elastic Cloud
Data model
Managed context layer and knowledge graph
Index and vector search
Industrial fit
Built for industrial and enterprise teams
Engineering teams
Data reach
70+ systems connected, and yours through a connector you write
Whatever you build
Residency
EU hosted, or entirely your own infrastructure
Your choice
Customization
What your team can change
Your own code, interface, data model and configuration. Elasticsearch 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
Painless scripts, ingest pipelines and workflows in cluster
Your own interface
Your own front end in C#, deployed from git
Kibana dashboards, Search UI JavaScript library
Data model
Typed node and edge schemas you define
Index mappings and templates you define
Configuration as code
Every change committed to git, pushed to your remote
Terraform provider, Dashboards API for config as code
APIs and SDKs
REST API, C# and Python library, connector SDK
REST API, official clients in major languages
AI integration
What AI can build and reach
Whether AI can build on it, who reviews the result, and which models it runs. Elasticsearch as its own documentation described it in October 2026.
AI that builds
Sudo drafts endpoints, tools, schemas and screens
Agent Builder drafts dashboards, skills, queries, workflows from chat
Review before it applies
Every change a diff a person approves, in a sandbox
Generated dashboards stay unsaved until you save them
Model choice
Any major provider, or an OpenAI compatible endpoint
Elastic managed LLMs, OpenAI, Azure, Bedrock, Vertex
Models on your servers
Open weights on your servers, or air gapped
Self managed LLMs, vLLM documented for air gapped
Assistant permissions
Built in tools search as the user, per record
Tools run with the user's or API key's privileges
Agent protocols
MCP server and an OpenAI compatible API
MCP server and client, A2A server
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.