Comparison · Data platforms
Curiosity and Dataiku
Projects for data teams, or tools for every team
Curiosity is a context layer teams build on; Dataiku is a platform for data science and AI projects.
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.
Dataiku
A platform for data preparation, machine learning and generative AI projects, used by data teams in industry and pharma, deployable self-managed or in the cloud. Projects are built and governed by data teams.
How Curiosity compares
Where the boundary falls
Dataiku is built for the data team’s work. Curiosity gives engineers and support teams a connected, permissioned graph they can ask and build tools on themselves.
Records and documents, not only tables
Industrial knowledge lives in tickets, documents, email and engineering systems as much as in the warehouse. Curiosity connects them where they are.
Permissions from the source
Access is synced from each source system, down to the record, rather than modeled again in a second set of grants.
Tools for the people who do the work
Studio builds apps and agents for engineers and support teams, on top of the graph, without a data team in the loop for each one.
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
Datasets and projects
Industrial fit
Built for industrial and enterprise teams
Data science teams
Data reach
70+ systems connected, and yours through a connector you write
Many data connectors
Residency
EU hosted, or entirely your own infrastructure
Your choice
Customization
What your team can change
Your own code, interface, data model and configuration. Dataiku 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
Python, R and SQL recipes in projects
Your own interface
Your own front end in C#, deployed from git
Standard, Dash, Streamlit, Shiny and Bokeh webapps
Data model
Typed node and edge schemas you define
Datasets with schemas, semantic models
Configuration as code
Every change committed to git, pushed to your remote
Each project a Git repository, push to remotes
APIs and SDKs
REST API, C# and Python library, connector SDK
REST public API, Python client, OpenAI compatible API
AI integration
What AI can build and reach
Whether AI can build on it, who reviews the result, and which models it runs. Dataiku as its own documentation described it in October 2026.
AI that builds
Sudo drafts endpoints, tools, schemas and screens
Cobuild builds flows, agents and webapps from prompts
Review before it applies
Every change a diff a person approves, in a sandbox
Ask every time mode, deletions always confirmed
Model choice
Any major provider, or an OpenAI compatible endpoint
LLM Mesh: major providers, NVIDIA NIM, custom endpoints
Models on your servers
Open weights on your servers, or air gapped
Local Hugging Face models on your own GPUs
Assistant permissions
Built in tools search as the user, per record
Cobuild acts as the user, document security tokens
Agent protocols
MCP server and an OpenAI compatible API
MCP server and remote MCP tools, OpenAI compatible API
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
Data platforms
Other comparisons in this group
Palantir Foundry and AIP
Curiosity gives you a focused context layer; Foundry and AIP are a full operating system.
CompareDatabricks
Curiosity is a finished context layer; Databricks is a data platform to build agents on.
CompareSnowflake
Curiosity connects records where they live; Snowflake runs agents over the data in its cloud.
CompareYour data. Your infrastructure.