Colab Enterprise vs Agent Platform Workbench (PMLE 2026)

Use Colab Enterprise for shared notebooks and managed runtime administration. Use Agent Platform Workbench for JupyterLab and supported environment customization. Both use VMs. Choose by how the team shares work, controls its environment, and manages access.

Last checked October 18, 2026.

Testero is independent and not affiliated with Google.

Google Cloud’s comparison describes Colab Enterprise as a collaborative, managed notebook environment. Agent Platform Workbench provides Jupyter notebook development environments through VM instances. Both support model prototyping; neither removes the need to configure IAM, data access, and networking.

The June 1, 2026 Professional Machine Learning Engineer guide names both products in objective 2.2, alongside collaboration and security best practices, framework-based development, and Model Garden prototypes.

Start with the collaboration model

Colab Enterprise lets you share a notebook with individual users, Google groups, or Google Workspace domains through IAM. Notebook roles distinguish owners, editors, and viewers. Use it when the notebook is what colleagues need to review: an analysis, a prototype, or an experiment.

Reading a notebook, running its code, and accessing its source data are separate permissions. The sharing guide requires the Notebook Runtime User role to run the notebook. Data access still depends on the credentials and permissions used by that run. A colleague who can open your file does not necessarily have permission to query your BigQuery dataset.

Saved outputs cross that permission boundary. People with notebook access can see them, including data from resources they cannot query directly. Treat cell outputs as shared content. Disabling output saving helps prevent new saved disclosures, but archived revisions can still contain earlier output.

Workbench starts from a different access model. Its JupyterLab interface uses an access mode selected at creation: a single specified user, or access through a service account. The latter can permit multiple individual users with the relevant service-account permission. That is not the same as sharing a Colab notebook to a group or domain, and the Workbench access guide explicitly says group access is unsupported.

Managed runtime does not mean unconfigurable runtime

Colab runtimes are Google-provisioned VMs; runtime templates determine hardware and other settings. The runtime-template guide documents configuration for machine type, accelerators, disks, network, public internet access, and end-user credential behavior. Configure a template once, then let the team use it for notebook runs.

Review the defaults before the first run. Current runtime documentation lists public internet access as enabled and automatic deletion 18 hours after creation. Files uploaded or changed on a deleted runtime are not preserved. Store datasets and artifacts in persistent storage.

IAM can also affect first use. The Colab Enterprise User role does not include permission to create the default runtime template. If a project and region do not yet have that template, someone needs the additional template-creation permission. Check template permissions before debugging the framework.

Use Workbench for environment control

Workbench arrives with JupyterLab and deep learning packages, including TensorFlow and PyTorch support. It supports conda environments, GitHub synchronization, and CPU or GPU configurations. Use it when the team needs a particular kernel environment or supported custom container.

Check the customization limits before choosing it. Google documents that third-party JupyterLab extensions are unsupported. Custom containers should derive from its supplied base container. Underlying VM changes must use the supported Workbench interfaces rather than editing the VM through the Compute Engine API or console.

For a specific library version, use a conda environment or supported container. For a requested extension, check support first; JupyterLab availability alone is not enough.

Scenario 1: a shared demand-forecast prototype

A retailer’s analysts need to review a forecasting prototype using approved BigQuery views. The team prioritizes sharing its analysis and avoiding routine infrastructure work. It does not require a specially managed JupyterLab environment.

Use Colab Enterprise. Share the notebook with the appropriate group, choose editor and viewer roles deliberately, and give only the people who need execution access the required runtime permissions. Check that each runner can access the approved views under the configured credentials.

Before the review, inspect saved outputs for sensitive records. Use aggregated results and disable output saving when outputs must not persist. Check revision-history exposure separately. Configure the runtime template to match the company’s network policy, and persist useful outputs outside the runtime.

Scenario 2: a controlled research environment

A research team needs JupyterLab, separate conda kernels, GitHub synchronization, and a supported container customization. Its security team also requires deployment inside a service perimeter.

Use Workbench for this environment. Start with the supported image or base container, select the access mode before creation, and assign the instance service account only the data permissions it needs. Grant instance-management permissions separately from JupyterLab access.

VPC Service Controls changes the automation plan: the Workbench executor is unsupported inside that perimeter. Plan another scheduling path. Document access revocation too: stopping or resetting an instance may be necessary to remove an existing browser session immediately.

How this maps to the June 2026 guide

In the June 2026 guide, 2.2 covers notebook prototyping, collaboration, and security. 2.1 adds privacy and sensitive-data handling, while 2.3 covers experimentation and artifact lineage. A notebook choice is not, by itself, a complete experiment-tracking design. For objective 2.2, explain the collaboration model, execution identity, environment requirements, and security constraints. “Easy notebooks versus powerful notebooks” misses the decisions that matter.

Common traps

Avoid choosing by UI alone. Notebook sharing does not configure an experiment-tracking system, and a powerful VM does not solve access control. Decide how the team will record runs, preserve artifacts, and review results before choosing the notebook environment.

Ready to check your foundations? Try the free PMLE diagnostic and select its 5-question quick check, then explore PMLE sample questions. Use the Agent Platform cheat sheet for terminology. For broader preparation, read the study plan by domain and June 2026 exam changes.

Sources

• Choose a notebook solution
• PMLE exam guide as of June 1, 2026
• Colab notebook sharing and output security
• Workbench JupyterLab access modes
• Colab runtimes and runtime templates
• Configure a Colab runtime template
• Workbench introduction and supported customization