Count
Count helps your data team work closer with the business so everyone is working on the problems that matter. Fast.
Count vs Deepnote:
a side-by-side comparison for 2024
Comparing two data science notebooks.
Deepnote
Deepnote is a new kind of data notebook that’s built for collaboration — Jupyter compatible, works magically in the cloud, and sharing is as easy as sending a link.
Count
Deepnote
Setup
Fully managed (setup in minutes)
Fully managed (setup in minutes)
Can you self-host?
Can you self-host?
No, you must use a managed offering
No, you must use a managed offering
Features
Is it Jupyter compatible?
Is it Jupyter compatible?
Not Jupyter-compatible
Jupyter-compatible
Programming languages
Programming languages
SQL
Jupyter languages (e.g. Python, R)
SQL
What kind of data sources can you connect to?
What kind of data sources can you connect to?
Connect to data warehouses (AWS, GCP, etc.)
Connect to databases (Postgres, MongoDB, etc.)
Connect with Jupyter libraries (e.g. SQLAlchemy, psycopg2)
Connect to data warehouses (AWS, GCP, etc.)
Connect to databases (Postgres, MongoDB, etc.)
Provided file storage
What kind of data visualization can you do?
What kind of data visualization can you do?
UI for building charts
Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)
UI for building charts
Reactivity
Reactivity
Unknown
Full, realtime reactivity
No notebook scheduling
Notebook scheduling is built in
Management
Reproducibility
Reproducibility
Unknown
Environments are reproducible by default
Run notebooks in containers
Version history is built in
Version history is built in
Multiple editors at the same time
Multiple editors at the same time
Comments
Comments
Comment on items within a notebook
Comment on items within a notebook
Notebook organization
Notebook organization
View notebooks in a list
View notebooks in a tree, like a wiki
Licensing
License
License
Proprietary
Proprietary
Price
Price
Free tier
Pay-per-user
Free tier
Pay for compute
Pay-per-user