12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing

Jupyter Notebook is a widely popular tool for data scientists to work on data science projects. This article reviews the top 12 alternatives to Jupyter Notebook that offer additional features and capabilities.

What are some challenges with Jupyter Notebook?

Although Jupyter Notebook is popular tool to work on data projects, it lacks some essential features such as real-time collaboration, code completion, security, commenting, versioning, publishing, and native integrations with other tools. Collaborative and interactive notebook environments with real-time synchronization are essential for open source or business projects with multiple contributors.

 

Noteable – Collaborative Data Notebook for Python, SQL and R

Noteable – The #1 among all Jupyter Notebook Alternatives with the most comprehensive set of features for data science and machine learning use cases for both users and business.

Noteable is a cloud-based data notebook platform for data science, machine learning and data engineering that delivers an interactive and collaborative experience for users. It is 100% free for individual users, offers paid plans for businesses and has received great reviews and recognition from experts across the industry.

Some Cool Features why Noteable the #1 Jupyter Alternative

• Real-time collaboration (like Google Drive)
• Use Python, R, and SQL
• Write SQL queries on CSV or JSON files (no need for data tables)
• Effortlessly use Python code & SQL in the same notebook on any data frames
• Interactive data visualization (like Tableau or Excel)
• Interactive tables with format helpers (convert date formats, strings, numbers, float etc)
• Select hardware resources between 1 to 15 CPUs (free compute, fair use policy applies)
• Secrets Vault to securely store, use, and share API Keys and Credentials
• Unlimited cloud storage
• Versioning to manage multiple versions of a notebook
• Publish unlimited number of interactive notebooks
• Schedule notebooks to run periodically
• integration with Dagster, Airflow and Papermill for orchestration and data pipelines

 

Noteable is collaborative – for both data experts and stakeholders

Noteable users can query, visualize and automate together in real-time using a programming language of their choice or using no-code features. Noteable is designed to bring data practitioners and stakeholders onto the same platform facilitating effective collaboration, cross-functional reviews and feedback.

Noteable increase data science productivity

Many data scientists typically run SQL in a separate tool, export data as csv file and the use a Python IDE or Jupyter Notebook to then explore the data. Noteable has an interesting feature that allows users to use SQL or Python code to directly query a variety of file formats (JSON, CSV, Excel etc.) eliminating this inconvenience and delivering an efficient data science workflow.

Noteable supports all data science use cases – from BI to AI

It is library agnostic, supporting libraries like PyTorch, Tensorflow, Huggingface transformers and so on. This makes it the most comprehensive data platform for a data analyst, data scientist, machine learning engineer, and data engineer.

Also, Noteable offers a really intuitive interface with side by side markdown text editor and preview, intuitive Python editor, secrets vault and more – making it truly enjoyable to build a network diagram with a single click or to build machine learning models or seamlessly collaborate over big data.

This makes it a convenient platform for the varying needs of data analysts, data scientists and machine learning practitioners. Try Noteable today →

Here is a quick overview of Noteable:

Google Colab

Google Colab (or google colaboratory) is a popular Jupyter Notebook alternative, which is a free, cloud-based Jupyter Notebook environment built by Google. Colab provides high-performance computing resources like free GPU and TPU for machine/deep learning models and data science use cases.

However, there are limitations on the free tier and users may need to upgrade to a paid plan for more intensive or longer-term usage. The free tier on Colab can be resource-intensive with slow performance and long wait times when working with large datasets.

Colab offers features such as importing/exporting data from Google Drive, integration with Google Cloud Storage, and support for Git version control, making it a convenient platform for data scientists.

Colab is primarily geared towards Google products like Drive and Cloud Storage, which may not be convenient for users preferring other storage solutions.

It is important for users to carefully consider their needs and preferences when deciding whether Google Colab is the right choice of free service for them.

Kaggle Notebooks (a cloud-based Jupyter Notebook alternative environment for Kaggle)

Kaggle Notebooks is a cloud-based notebook alternative platform for data science enthusiasts and machine learning. Like Google Colab, Kaggle Notebooks offers powerful hardware resources, including GPUs and TPUs, for running machine learning and deep learning models. It also offers a number of other useful features, such as integration with the Kaggle API, support for version control with Git, and the ability to easily share and collaborate on notebooks with team members.

One of the main differences between Kaggle Notebooks and other alternatives is that it is specifically geared towards learning Python, data science, and machine learning, with a focus on competition. Kaggle Notebooks provides a good solution for users to easily participate in these competitions and collaborate with other users and developers, making it a top choice for data science projects.

Microsoft Azure Notebooks

Microsoft Azure Notebooks is a cloud-based platform for data science projects and machine learning that is similar to Google Colab and Kaggle Notebooks. It provides access to powerful hardware resources, including GPUs and TPUs, for running machine learning and deep learning models, as well as a number of other useful features, such as integration with Microsoft Azure Storage, support for version control with Git, and the ability to easily share and collaborate on notebooks with other team members.

One of the main advantages of Microsoft Azure Notebooks is that it offers a wide variety of programming languages and libraries, including Python, R, F#, and Julia, making it a versatile platform for data scientists, software developers, and analysts who like to use the programming language of their choice.

Using Azure Notebooks requires you to have an Azure account and the setup is somewhat intense.

CoCalc

CoCalc (formerly known as SageMathCloud) is a cloud-based platform for collaborative computing that offers many of the same features as Jupyter Notebooks, as well as a number of additional capabilities. It supports a wide variety of programming languages, including Python, R, and Julia, and provides access to powerful hardware resources, including GPUs. In addition to its core features, CoCalc also offers support for LaTeX and computer algebra systems, making it a good choice for users who need these capabilities.

JetBrains Datalore

JetBrains Datalore is a cloud-based data science platform that offers many of the same features as Jupyter Notebooks, as well as a number of additional capabilities. It supports a wide variety of programming languages, including Python, R, and SQL, and provides access to powerful hardware resources, including GPUs. One of the main advantages of JetBrains Datalore is its integration with the JetBrains ecosystem of tools, which includes IDEs such as PyCharm and IntelliJ.

It is worth mentioning that it is primarily geared toward users of the JetBrains ecosystem of tools, such as PyCharm and IntelliJ. This can be inconvenient for users who do not use these tools and may prefer to use other IDEs or code editors.

Deepnote

Deepnote is a cloud-based, data science notebook platform that is similar to Jupyter Notebooks, but with a focus on collaboration and real-time editing. It allows users to write and execute code in a variety of programming languages, as well as include text, equations, and visualizations in a single document. Deepnote also has a built-in code editor and supports a wide range of libraries and frameworks, including TensorFlow and PyTorch.

Hex

Hex is a cloud-based platform for data science that offers many of the same features as Jupyter Notebooks, as well as a number of additional capabilities. It supports a wide variety of programming languages, including Python, R, and Julia, and provides access to powerful hardware resources, including GPUs. Hex also has a built-in code editor and supports a wide range of libraries.

Nextjournal

Nextjournal is a cloud-based platform for scientific computing and data science that offers many of the same features as Jupyter Notebooks, as well as a number of additional capabilities. It supports Python, R, and Julia, and provides powerful hardware resources, including GPUs.

One of the main advantages of Nextjournal is its focus on reproducibility, which allows users to easily share and reproduce their work.

Paperspace Gradient

Paperspace Gradient is a cloud-based platform for data science and machine learning that offers many of the same features as Jupyter Notebooks, as well as a number of additional capabilities. It provides powerful hardware resources, including GPUs, and supports Python, R, and Julia.

Paperspace Gradient also has a built-in code editor and supports a wide range of libraries and frameworks, including TensorFlow and PyTorch.

Apache Zeppelin

Apache Zeppelin is an open-source platform for data science and analytics that is similar to Jupyter Notebooks. It allows users to write and execute code in a variety of programming languages, as well as include text, equations, and visualizations in a single document. Apache Zeppelin also has a built-in code editor and supports a wide range of libraries and frameworks, including TensorFlow and PyTorch.

Count.co

Count.co is a cloud-based platform for data science and machine learning that offers many of the same features as Jupyter Notebooks, as well as a number of additional capabilities. It supports a wide variety of programming languages, including Python, R, and Julia, and provides access to powerful hardware resources, including GPUs.

Count.co also has a built-in code editor and supports a wide range of libraries and frameworks, including TensorFlow and PyTorch. One of the main advantages of Count.co is its focus on collaboration, which allows users to easily share and collaborate on notebooks with other users.

Features Comparison of Jupyter notebooks alternatives

This table summarizes the more in-depth knowledge of each of the alternatives to Jupyter mentioned previously in this article. Each of them has its own set of features and capabilities, and the best choice will depend on the specific needs and preferences of the user.

Here is a comparison of some of the key features of the Jupyter Notebook alternatives discussed in this article:

	Programming Languages	Cloud Storage	Version Control	Collaboration	Interactive Visualization	Additional Capabilities	Security
Noteable	Python, R, SQL	Google, Azure, AWS, Native	Git, Linear Versioning	Real-time collaboration, Commenting, @Mention users, Annotate Data points, Publish Private or Public	Yes	iFrame Interactive Embedding, SQL on csv and data frames,	Yes – Cloud Agnostic. API Key Valut, SOC2 Compliant
Google Colab	Python, R	Google Drive	Git	Not real-time	No		Yes but via Google Cloud
Kaggle Notebooks	Python, R	Kaggle API	Git	No	No		No
Microsoft Azure Notebooks	Python, R, F#, Julia	Microsoft Azure Storage	Git	No	No		Yes but via Azure Cloud
CoCalc	Python, R	Native	Git		Yes	LaTeX, computer algebra systems
JetBrains Datalore	Python, R, SQL	Native	Git		Yes	Integration with JetBrains ecosystem
Deepnote	Python, R	Native		Yes	Yes	Real-time collaboration
Hex	Python, R	Native		Yes	Yes	Real-time collaboration
Nextjournal	Python, R	Native	Git	Yes	Yes	Reproducibility
Paperspace Gradient	Python, R	Native			Yes
Apache Zeppelin	Python, R	Native	Git		Yes
Count.co	Python, R	Native		Yes	Yes	Collaboration

As this table shows, each of the alternatives to Jupyter Notebooks discussed in this article has its own set of features and capabilities, and the best choice will depend on the specific needs and preferences of the user.

For example, Google Colab and Kaggle Notebooks are well-suited for machine learning and deep learning tasks, with access to powerful hardware resources. In addition to the above, there are some other similar tools like visual studio code (VSCode) that can also be used alongside the Jupyter notebook environment.

Noteable, on the other hand, has the most comprehensive set of features to support almost all use cases, is cloud agnostic, can connect to any database, can connect to any API, and offers a serious efficiency boost to any data science workflow.

Best Jupyter Notebook Alternative for Data Analysis

• • Noteable

• • Deepnote (also very good)

Best Jupyter Notebook Alternative for Data Science

• • Noteable

• • Deepnote and Hex

Best Jupyter Notebook Alternative for Machine Learning

• • Hex

• • Noteable & Deepnote

Best Jupyter Notebook Alternative for SQL

• • Noteable (most powerful SQL querying features)

Best Jupyter Notebook Alternative for Python

• • Noteable / Deepnote (both have an array of supporting features, integrations, and versatility)

Best Jupyter Notebook Alternative for Businesses

• • Noteable (particularly because of collaboration, security, interactive visualizations, effortless SQL, Python and R)

Overall, What is the best Jupyter Notebook Alternative?

Noteable is the best Jupyter Notebook alternative for data science, machine learning, and data engineering. It has the most comprehensive set of features that any data science team or data team will ever need. In addition, Noteable has the security features and integrations that are must-have requirements for enterprise data science notebooks.

Moreover, with interactive visualization and collaboration features and the ability to use a programming language of choice, Noteable bridges the gap between data practitioners and stakeholders bringing them together to work on the same platform.

This saves time, and frustration and ensures that data teams don’t have to hop between multiple tools like SQL editor, Python IDE, BI tool, and Slideshow tools to deliver a project end to end.

If you are looking for a powerful Jupyter alternative built to support the needs of modern data teams, we recommend Noteable.

Read what Noteable users are saying