Count
WebsiteCount helps your data team work closer with the business so everyone is working on the problems that matter. Fast.
Count vs Amazon Sagemaker:
a side-by-side comparison for 2024
Comparing two data science notebooks.
Amazon Sagemaker
WebsiteAmazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML.
Count
Amazon Sagemaker
Setup
Fully managed (setup in minutes)
Fully managed (setup in hours)
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)
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)
AWS
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)
Reactivity
Reactivity
Unknown
No reactivity, you decide the execution order
No notebook scheduling
Notebook scheduling with additional tools
Management
Reproducibility
Reproducibility
Unknown
There is no support for reproducibility
Version history is built in
File-based (use Git)
Multiple editors at the same time
No support for collaborative editors
Comments
Comments
Comment on items within a notebook
File-based (use GitHub)
Notebook organization
Notebook organization
View notebooks in a list
View notebooks in a list
Licensing
License
License
Proprietary
Proprietary
Price
Price
Free tier
Pay-per-user
Free tier
Pay for compute