Comparison of popular cloud notebook platforms such as Google Colab, Azure Notebooks, and AWS SageMaker

Hey, fellow data scientists and machine learning enthusiasts! Are you tired of limitations that come with local processing power? Do you want to scale your computations based on demand? Fortunately, there is an effective solution that you can take advantage of to enhance your production: Cloud Notebook Platforms. Cloud notebook platforms can offer you cost-effective, managed hardware and software environments that allow you to run Python packages, Jupyter notebooks, and other machine learning tools much more easily and seamlessly. It frees you up to concentrate on the actual meat of your project rather than lagging behind with infrastructure management.

In this article, we will review and compare the most popular Cloud Notebook Platforms such as Google Colab, Azure Notebooks, and AWS SageMaker. We will delve into their pros and cons while rating their performance, ease of use, pricing, and features.

Google Colaboratory (Colab)

Google Colab is a free, cloud-based platform that provides Jupyter notebooks with free access to a Tesla K80 GPU (12GB VRAM), which is ideal for machine learning. Through integration with Google Drive, Colab notebooks can be shared easily and worked on collaboratively. Colab offers all features of Jupyter, but it provides powerful support for Google's TensorFlow machine learning framework. You can immediately load your data to Google Drive and start coding without any installation or configuration work. Colab runs inside your browser and doesn't require any setup or installation, therefore it's easily accessible to anyone.

Ease of Use

Colab is undoubtedly the easiest platform of the three, primarily because Google takes care of almost everything. Colab has a robust, intuitive interface that makes editing, running, and commenting on your notebook a breeze. Moreover, Colab provides access to detailed BigQuery public datasets, which opens up the possibility for larger and more complex analyses.

Google Colab is ideal for those getting started with machine learning and data science because of the ease in sharing and collaboration capabilities. Additionally, the platform offers a tutorial section that provides guides and code snippets for building different machine learning models.


Google Colab is currently free for use; however, you are limited to 12-hour sessions to ensure that the infrastructure is available to as many users as possible. However, you can still use the platform without any problems. Unfortunately, you can't run simulations overnight or continuously, for example, based on power management policy.


Colab comes with several intriguing features, including free TPU usage and the ability to use custom libraries. Colab offers a powerful environment for building machine learning models and allows you to experiment with TensorFlow, Keras, Pytorch, and Sklearn, amongst other popular frameworks.

If you have any existing projects in Jupyter notebooks, you can easily upload them into Colab from your Google Drive, and you will be ready to walk and type.

Azure Notebooks

Azure Notebooks is a cloud-based, Jupyter notebook platform provided by Microsoft Azure. The platform provides access to various computing resources, including CPUs and GPUs, making it an excellent choice for computational-intensive workloads.

Ease of Use

Azure Notebooks offers excellent ease of use for beginners, with a minimal learning curve. The platform provides two editors; an integrated text editor and a Jupyter Notebook editor. Users are given the option to build environments from scratch or choose from pre-built environments from the platform's extensive gallery.

Furthermore, Azure Notebooks offers built-in support for GitHub, enabling faster deployment and collaboration. Lastly, the platform offers detailed documentation and tutorials aimed at helping beginners get a quick and efficient start for their new projects.


Azure Notebooks offer strong value through their scalable pay-as-you-go pricing model. Users get a credit of fifteen hours per month to use at their own pace. Furthermore, depending on the utilization, CPU utilization or other computing resources can add costs per hour, yet the pricing is quite transparent and enforced as you create A project or a custom environment.


Azure Notebooks offers a versatile environment that is appropriate for machine learning, data science, and research work. The platform is optimized for both Python and R languages and offers seamless integration with popular machine learning libraries like Keras, TensorFlow, and Pytorch.

Moreover, Azure Notebooks support Azure machine learning - a robust and comprehensive AI framework that helps to streamline complex tasks in automatic labeling, model training, inference, and deployment.

AWS SageMaker

Amazon SageMaker is a fully managed service that enables developers and data scientists to build, train, and deploy machine learning algorithms at scale. It provides a complete range of tools for building, training, and deploying machine learning models.

Ease of Use

AWS SageMaker is a more advanced and comprehensive platform than Colab and Azure Notebooks. Therefore, it is not recommended for beginners. However, it has a well-worth learning curve. SageMaker offers a high level of customization and flexibility, which can help you keep up with the complexity and requires from using advanced architecture solutions.

Amazon SageMaker provides a comprehensive interface that is easy to use, provides quick access to popular resource pools and allows quick access to projects' data management. SageMaker also supplies popular development languages such as R, Python, and TensorFlow.


Amazon SageMaker offers a “pay-as-you-go” model, which means the cost depends on what you use. There are no upfront fees or complicated contracts to set up, which gives greater flexibility in pricing. Users can use the Amazon SageMaker free tier to start a simple project experiment.

However, SageMaker is not cheap. If you have many processing requirements, like using large volumes of data, then expect to pay more.


AWS SageMaker provides a rich set of advanced features such as "Training Amazon SageMaker machine learning models using continuous logging" and "Amazon SageMaker automatic model tuning." Additionally, with SageMaker, developers can select from a wide range of machine learning algorithms or customize and transfer their own for training and scoring. Moreover, SageMaker supports pre-built frameworks, including TensorFlow, MXNet, PyTorch, and R.

Comparison Table

In conclusion, each of the three cloud notebook platforms has its unique strengths and advantages that set it apart from the others. Google Colab is excellent for beginners due to its ease of use, Azure Notebooks is compatible for intermediate users because of its flexibility and straightforward access, while AWS SageMaker is ideal for advanced and experienced data scientists who require customization and scalability.

To help you make a decision that suits your individual requirements, we have prepared a table to break down the features and pricing of the three notebooks platforms for easier comparison.

| Features/Pricing | Colaboratory | Azure Notebooks | AWS SageMaker | |:-:|:-:|:-:|:-:| | Environment | Free | Free | Pay-as-you-go | | Personal Storage | Unlimited | Unlimited | Unlimited | | GPU/TPU hardware | Included | Optional | Available | | Model Training | Included | Limited | Customizable | | Custom Libraries | Included | Customizable | Customizable | | Collaboration | Simple | Complex | Simple | | Project Management | Basic | Intermediate | Intermediate | | Scalability | Not recommended | Slightly-Recommended | Required | | Machine Learning Features | Strong | Decent | Strong | | Ease of Use | 5 | 4 | 3 | | Pricing | 5 | 4 | 2 | | Features | 4 | 4 | 5 | | Score | 14 | 12 | 10 |

In summary, for beginners and people who prefer a user-friendly and cost-effective alternative, Google Colab is a perfect choice. If you require an intermediate level platform for customization and collaboration, Azure Notebooks provides an outstanding solution. Lastly, AWS SageMaker is an advanced and comprehensive machine learning platform that is ideal for large-scale projects while maintaining flexibility and customization.

So, there you have it data scientists and machine learning enthusiasts! Our review and comparison of the best Cloud Notebook Platforms that will help you build and deploy your machine learning models in a collaborative, scalable, and financially efficient way. Are there any platforms that you believe we should add? Please leave your comments below!

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