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Vertex AI Workbench vs Colab Enterprise: Which Notebook Solution is Right for You?

Updated: 2 days ago

When it comes to data science and machine learning, choosing the right notebook solution can make a big difference. Google Cloud offers two excellent options: Vertex AI Workbench and Colab Enterprise. Both are built on the popular Jupyter notebook platform, but each has its own strengths and ideal use cases. Last year, we showed how you can get away with almost free notebooks on GCP. In this post, we’ll compare these two solutions to help you decide which one suits your needs best.

📔 What They Have in Common: Jupyter Notebooks

Both Vertex AI Workbench and Colab Enterprise use Jupyter notebooks, a favorite among data scientists for their interactive capabilities. Jupyter notebooks allow you to write and execute code, visualize data, and document your findings all in one place. This shared foundation ensures that both tools offer a powerful and familiar interface for your data projects.

Comparison Table


Colab Enterprise

Vertex AI Workbench


Managed, collaborative

Customizable, developer-focused

Infrastructure Management

Serverless, managed by Google

User-controlled, flexible


Excellent, with IAM control

Good, with GitHub integration

Compute Provisioning



Data Integration

Seamless with Google Cloud services

Seamless with Google Cloud services

Code Completion






GPU Support

Conda Environments

Custom Containers

Automated Notebook Runs

Idle Shutdown



Persistent Storage

Access to VM

Original Jupyter UI



Which One Should You Choose?

  • For Collaboration and Simplicity: If your priority is to collaborate easily with others and avoid managing infrastructure, Colab Enterprise is the way to go. It’s designed to make teamwork simple and setup effortless.

  • For Customization and Control: If you need detailed control over your environment and extensive customization options, Vertex AI Workbench is your best bet. It supports complex workflows and allows you to configure instances to meet specific requirements.

Let's deep dive to why I think this way:

Colab Enterprise: Ideal for Collaboration and Ease of Use

Colab Enterprise is designed to make collaboration easy and free you from the hassles of managing infrastructure. Originally, it evolved from Google Colab, which was part of the Google Workspace (previously Gsuite) ecosystem, much like Google's version of Microsoft Office.

Key Features:

  • 🔗 Share and Collaborate: Easily share notebooks with individuals, Google groups, or entire Google Workspace domains. Access control is handled through Google Cloud’s IAM.

  • 🌐 Managed Compute: Colab Enterprise takes care of provisioning and managing compute resources. It starts runtimes when needed and shuts them down when not in use.

  • Google Cloud Integration: Seamlessly work with Google Cloud services like Vertex AI and BigQuery from within your notebook.

  • Inline Code Completion: Write code faster with suggestions that pop up as you type.

When working with Colab, you should consider the experience that Google intended to provide, which is somewhat similar to Google Docs/Slides. It's designed to be serverless and well connected to your G Suite data (Drive, files, etc.). The concept of sharing and creating copies of the notebook is at its core. An ideal experience is when you want to show your colleague some analysis that you have done, allowing them to duplicate it and experiment with it themselves in a new environment.

However, this solution is less efficient when you want to run heavy workloads, as the runtime needs to be extended for long tasks, or when you want the data to persist on the disk of the machine once it's turned off (or released, in this case). When you want to control the environment and optimize it, typically you'd prefer a more professional experience, which brings us to the next product: Workbench.

Vertex AI Workbench: Maximum Control and Customizability

Vertex AI Workbench offers extensive customization options, making it perfect for those who need more control over their environment. It is essentially an enhanced version of Google's Deep Learning VMs, part of the Compute Engine professional services.

Key Features:

  • 👨🏻‍💻 Access to the VM: Unlike Colab Enterprise, you get full access to the virtual machine itself, allowing for in-depth configuration tailored to your specific needs. You can integrate more easily with your Google Cloud Platform (GCP) environment based on IAM.

  • 📦 Persistent Storage: Data isn't lost when the machine restarts, as the VM's disk is retained, ensuring your data remains intact.

  • Diverse Instance Types: Choose from several types of instances, including custom setups that blend managed and user-managed features.

  • 🤏 Preinstalled Packages and GPU Support: All instances come with JupyterLab and a suite of deep learning packages like TensorFlow and PyTorch, with GPU support available.

  • </> GitHub Integration: Sync your notebooks with GitHub for version control and collaboration.

  • 💾 Custom Environments and Containers: Add conda environments or create custom containers to tailor your setup to specific needs.

  • 👾 Data Integration: Access Cloud Storage and BigQuery directly from JupyterLab.

  • 🛠️ Automated Notebook Runs and Idle Shutdowns: Schedule notebook runs and automatically shut down idle instances to manage costs effectively.

  • 🖥️ Original Jupyter UI: Workbench retains more of the original Jupyter UI, providing a cleaner and more familiar interface for users accustomed to Jupyter notebooks.


Both Colab Enterprise and Vertex AI Workbench are powerful tools that integrate well with Google Cloud services. Your choice will depend on what your project needs: ease of collaboration and management, or deep customization and control.

In conclusion, understanding the features and strengths of Colab Enterprise and Vertex AI Workbench can help you select the right tool for your data science and machine learning projects. Each solution offers unique benefits, so choose the one that best fits your workflow and goals.


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