What frameworks are preinstalled in Vertex AI Workbench instances?

Vertex AI Workbench instances are prepackaged with JupyterLab and a preinstalled suite of deep learning packages, including support for TensorFlow and PyTorch.

Can Vertex AI Workbench instances run on GPUs?

Yes. Vertex AI Workbench instances can be configured as either CPU-only or GPU-enabled instances.

Does Vertex AI Workbench shut down idle instances automatically?

Yes. To help manage costs, Vertex AI Workbench instances shut down after being idle for a specific time period by default, and this time can be changed or the feature turned off.

Vertex AI Workbench Review 2026: GCP Notebook Pricing & Guide

Vertex AI Workbench: Managed JupyterLab Notebooks on Google Cloud

Vertex AI Workbench is a managed, Jupyter notebook-based development environment for the entire data science workflow on Google Cloud, prepackaged with JupyterLab and deep learning frameworks. Note on naming: Google has rebranded this product's documentation as "Agent Platform Workbench" under the new Gemini Enterprise Agent Platform umbrella, but "Vertex AI Workbench" remains the name people actually search for and the one used throughout this review. It is one of the more enterprise-integrated entries in the site's Google Colab AI Tools category.

$0.0379/hr

vCPU (N1/N2/A2)

pay-as-you-go

JupyterLab

Prepackaged

TensorFlow & PyTorch

CPU or GPU

Instance Types

configurable

Renamed

in Docs

to Agent Platform Workbench

Table of Contents: Vertex AI Workbench Pricing, Features & Alternatives

Jump to the pricing, features, pros and cons, comparisons, FAQs, and alternatives.

1Quick Summary 2Vertex AI Workbench Role 3Best Fit 4Specialist Features 5Pricing 6Pros & Cons 7Use Cases 8Getting Started 9Worth It?10Comparison 11FAQ 12Alternatives

Vertex AI Workbench Quick Summary for AI Workflow Buyers

Overall Rating: 4.5/5 | Free Plan: No -- requires a Google Cloud project and billing; pay-as-you-go pricing from the first instance.
Best For: data scientists and ML engineers already on Google Cloud who need a managed, secure JupyterLab environment
Pricing: Pay-as-you-go -- vCPU from $0.0261-$0.0379/hr, memory from $0.0035-$0.0051/GiB-hr, plus GPU fees | Ease of Use: 4.2/5 | Business Value: 4.5/5
Last Tested: October 2026 | Version: Latest

Visit Vertex AI Workbench →

What Role Does Vertex AI Workbench Play in a Modern AI Workflow Stack?

Vertex AI Workbench is a Jupyter notebook-based development environment designed for the entire data science workflow, enabling users to interact with Gemini Enterprise Agent Platform and other Google Cloud services directly from notebooks. It is prepackaged with JupyterLab and includes deep learning packages for TensorFlow and PyTorch, with options for CPU-only or GPU-enabled instances. The platform supports GitHub repository sync and is secured by Google Cloud authentication and authorization. Key integrations include Cloud Storage for browsing data and BigQuery for querying tables, writing queries, and loading data without leaving JupyterLab. The executor feature allows one-time or scheduled notebook runs on custom training, supporting distributed training, hyperparameter optimization, and parameterized runs. Executed results are stored in Cloud Storage for sharing. Security capabilities include VPC deployment, customer-managed encryption keys, and service perimeter support.

Who Is Vertex AI Workbench Best For in 2026?

Data scientists Use Vertex AI Workbench to build and run Jupyter notebooks with preinstalled TensorFlow and PyTorch, access data via Cloud Storage and BigQuery, and execute notebook runs on custom training.
ML engineers Leverage the executor to run notebooks on a schedule or one-time, with distributed training and hyperparameter optimization, even when the instance is powered off.
Researchers Explore and visualize data in JupyterLab, add conda environments for R or Apache Beam, and share executed notebook results stored in Cloud Storage.
Enterprise teams Secure instances with VPC, CMEK, Confidential Computing, and automatic idle power-off, and integrate with Managed Service for Apache Spark for fast data processing.

Professional reality: Vertex AI Workbench is a Google Cloud-specific tool, so it requires a Google Cloud project and billing, and its features are tightly coupled to Google Cloud services, which may not suit teams using other cloud providers.

Specialist Vertex AI Workbench Features That Matter for Business Growth

Data Access

Access data without leaving JupyterLab

Use the Cloud Storage integration to browse files and the BigQuery integration to query tables, preview results, and load data directly into your notebook.

Streamline data exploration and analysis within your notebook environment.

Notebook Execution

Run notebooks on a schedule or on demand

Use the executor to run a notebook file as a one-time execution or on a recurring schedule. Choose the environment and hardware, and use parameters to customize each run.

Automate and scale notebook runs, even when your instance is powered off.

Sharing

Share insights easily

Executed notebook runs are stored in a Cloud Storage bucket, so you can share results with others by granting access to the bucket.

Collaborate effectively by sharing reproducible notebook outputs.

Security

Secure your instance with VPC, CMEK, and Confidential Computing

Deploy within a VPC network, use customer-managed encryption keys (CMEK) for data at rest, and enable Confidential Computing to encrypt data in use.

Meet compliance and regulatory requirements with robust security controls.

Customization

Add conda environments and custom containers

Add conda environments to use kernels like R or Apache Beam, or create an instance from a custom container based on a Google-provided base image.

Tailor your environment to specific frameworks or versions.

Integration

Integrate with Apache Spark and Compute Engine reservations

Run notebooks on a Managed Service for Apache Spark cluster, and use Compute Engine reservations to ensure VM resources are available for your instances.

Process data faster and guarantee resource availability for critical workloads.

Vertex AI Workbench is a frequent pick in broader AI tool roundups -- see our guides to the best AI tools for data scientists and best AI tools for machine learning engineers for how it fits into a wider GCP stack.

How Much Does Vertex AI Workbench Cost in 2026?

Vertex AI Workbench is billed on a pay-as-you-go basis as standard Google Cloud Compute Engine VM usage -- there is no flat subscription fee or free tier. vCPU pricing is $0.0379/hour for N1, N2, or A2 machine series, or $0.0261/hour for the E2 machine series. Memory is billed separately at $0.0051 per GiB-hour (N1/N2/A2) or $0.0035 per GiB-hour (E2). GPU-enabled instances add GPU management fees on top of the vCPU and memory rates. A default instance typically runs around $0.077/hour before GPU costs. Because billing is usage-based, automatic idle power-off (on by default) is the main lever for controlling cost.

Component	Price	Details
vCPU (N1/N2/A2)	$0.0379/hour	Standard Compute Engine machine series, pay-as-you-go.
vCPU (E2)	$0.0261/hour	Lower-cost machine series option.
Memory (N1/N2/A2)	$0.0051/GiB-hour	Billed separately from vCPU.
Memory (E2)	$0.0035/GiB-hour	Lower-cost machine series option.
GPU	Added on top	GPU management fees apply in addition to vCPU/memory rates.

Visit the official Vertex AI Workbench website to check the latest pricing and plans.

Vertex AI Workbench Pros and Cons for AI Tool Buyers

Where Vertex AI Workbench Is Strong

Jupyter-based environmentVertex AI Workbench instances are Jupyter notebook-based development environments for the entire data science workflow. They are prepackaged with JupyterLab and include a preinstalled suite of deep learning packages, including support for TensorFlow and PyTorch frameworks.
Integrated data accessYou can access data without leaving the JupyterLab interface. The Cloud Storage integration lets you browse data and files, while the BigQuery integration allows you to browse tables, write queries, preview results, and load data into your notebook.
Scheduled notebook executionUse the executor to run a notebook file as a one-time execution or on a schedule. You can choose the specific environment and hardware. Notebook code runs on Vertex AI custom training, enabling distributed training, hyperparameter optimization, and continuous training jobs. Even when your instance is powered off, scheduled runs execute and save results.
Secure by defaultVertex AI Workbench instances are protected by Google Cloud authentication and authorization. You can deploy your instance in a VPC, and supported capabilities include Confidential Computing, customer-managed encryption keys, and service perimeter controls.

Where Vertex AI Workbench Needs Care

Sharing insightsExecuted notebook runs are stored in a Cloud Storage bucket, so you can share insights by granting access to the results. The documentation does not specify any other sharing mechanisms or integrations beyond this.
GitHub syncVertex AI Workbench instances support the ability to sync with a GitHub repository. The documentation does not provide details on how this sync works or any limitations.
Hardware configurationYou can configure either CPU-only or GPU-enabled instances. The documentation does not list specific machine types or GPU models available.
PricingBilling is usage-based (vCPU + memory + optional GPU, all per-hour), not a flat plan -- actual monthly cost depends heavily on instance size, GPU choice, and how consistently automatic idle power-off is used.

When Does Vertex AI Workbench Deliver the Most Business Value?

Access Data from JupyterLab

Use the Cloud Storage integration to browse files and the BigQuery integration to query tables, preview results, and load data directly into your notebook without leaving the JupyterLab interface.

Schedule and Execute Notebook Runs

Run notebook files as one-time executions or on a recurring schedule using the executor. Choose the environment and hardware, and even run while your instance is powered off, with results saved to Cloud Storage.

Train Models with Custom Hardware

Configure CPU-only or GPU-enabled instances and run your notebook code on Vertex AI custom training to support distributed training, hyperparameter optimization, and continuous training jobs.

Secure Your Instance with Advanced Controls

Deploy within a VPC, use customer-managed encryption keys (CMEK), enable Confidential Computing for data-in-use encryption, and set automatic power-off for idle instances to manage costs.

How Do You Get Started With Vertex AI Workbench?

Create a Vertex AI Workbench instance from a Google Cloud project, choosing CPU-only or GPU-enabled hardware and your preferred machine series (N1, N2, A2, or E2).

Compare it with closely related AI tools in the same category before committing.

Set review rules for accuracy, privacy, brand voice, compliance, and final approval.

Connect useful outputs to the wider stack instead of leaving them inside the AI tool.

Is Vertex AI Workbench Worth It for AI Tool Buyers?

Vertex AI Workbench is worth it for teams already on Google Cloud who need a secure, managed JupyterLab environment with deep BigQuery/Cloud Storage integration, scheduled notebook execution, and enterprise controls like VPC, CMEK, and Confidential Computing. The pay-as-you-go pricing (from $0.0261/hr vCPU) means it scales with usage rather than a flat fee, so automatic idle power-off matters for cost control. Teams outside the Google Cloud ecosystem, or those wanting a free zero-setup option, are better served by Google Colab or Kaggle Notebooks.

Vertex AI Workbench vs Competitors: Which Tool Fits Best?

Decision Area	Vertex AI Workbench	When Another Option Wins
Data access	Integrated Cloud Storage and BigQuery browsing directly within JupyterLab, without leaving the interface.	Google Colab offers simpler, more consumer-friendly data upload and Google Drive integration for quick, casual use.
Scheduled notebook runs	Built-in executor runs notebooks on a schedule or one-time, even when the instance is powered off, using custom training.	Deepnote provides collaborative scheduling with a more team-focused UI, which may be easier for non-GCP users.
Security and compliance	Supports VPC, CMEK, Confidential Computing, and Google Cloud IAM for enterprise-grade security.	Kaggle Notebooks offers a simpler, zero-config environment that is fine for public datasets and learning, without security setup.
Custom environments	Add conda environments (e.g., R, Apache Beam) or use custom containers based on Google-provided base images.	Lightning AI Studios provides more flexible environment management with prebuilt templates for PyTorch and other frameworks.
Integration with Google Cloud services	Native integration with Vertex AI custom training, Managed Service for Apache Spark, and Cloud Storage.	Amazon SageMaker Studio Lab is better if you are already invested in AWS ecosystem and need SageMaker-specific features.

Vertex AI Workbench vs Google Colab

Google Colab is a free, cloud-based Jupyter notebook environment that requires no setup and offers GPU access. It is great for quick experiments and learning, but lacks the enterprise-grade security, VPC, and scheduled execution features of Vertex AI Workbench.

Choose Vertex AI Workbench if: You need a managed, secure, enterprise-ready notebook environment with deep Google Cloud integrations, custom containers, and scheduled runs. Choose Google Colab if: You want a free, zero-config notebook for quick prototyping, education, or lightweight data exploration.

Vertex AI Workbench vs Deepnote

Deepnote is a collaborative data science notebook that emphasizes real-time collaboration and easy sharing. It supports multiple languages and has a clean UI, but does not offer the same level of Google Cloud integration or custom training execution.

Choose Vertex AI Workbench if: You need tight integration with BigQuery, Cloud Storage, and Vertex AI custom training, plus enterprise security features. Choose Deepnote if: Your team prioritizes real-time collaboration and a polished, simple interface over deep cloud service integration.

Vertex AI Workbench FAQ for AI Tool Buyers

What is Vertex AI Workbench?

Vertex AI Workbench is a Jupyter notebook-based development environment for the entire data science workflow. It is prepackaged with JupyterLab and includes a preinstalled suite of deep learning packages, including support for TensorFlow and PyTorch. You can configure CPU-only or GPU-enabled instances and sync with a GitHub repository.

How can I access data from Vertex AI Workbench?

You can access data without leaving the JupyterLab interface. Use the Cloud Storage integration to browse data and files, and the BigQuery integration to browse tables, write queries, preview results, and load data into your notebook.

Can I schedule notebook runs in Vertex AI Workbench?

Yes, you can use the executor to run a notebook file as a one-time execution or on a schedule. You can choose the environment and hardware for the run. Notebooks run on Vertex AI custom training, and you can set recurring schedules. Even if your instance is powered off, the notebook will run and save results.

What security features does Vertex AI Workbench support?

Vertex AI Workbench supports VPC networks, customer-managed encryption keys (CMEK), and Confidential Computing (Confidential VM service). Instances are protected by Google Cloud authentication and authorization. By default, instances automatically power off after being idle for a specific time period to help manage costs.

Can I customize the environment in Vertex AI Workbench?

Yes, you can add conda environments (e.g., for R, Apache Beam, or earlier versions of TensorFlow, PyTorch, or Python) that appear as kernels in JupyterLab. You can also create an instance based on a custom container, starting from a Google-provided base image. Additionally, you can integrate with Managed Service for Apache Spark and use Compute Engine reservations for VM resources.

Key Takeaways

Vertex AI Workbench is billed pay-as-you-go: vCPU from $0.0261-$0.0379/hr plus memory and GPU fees -- there is no free tier.
Google's documentation now labels this product "Agent Platform Workbench" under the Gemini Enterprise Agent Platform rebrand, though "Vertex AI Workbench" remains the name people search for.
Deep BigQuery, Cloud Storage, and scheduled-execution integration make it strongest for teams already invested in Google Cloud.

Vertex AI Workbench also shows up in our best AI tools for developers roundup. Browse the full Google Colab AI Tools category for more cloud notebook options.

Vertex AI Workbench Alternatives Worth Comparing

Google Colab - a free, zero-config notebook platform, simpler but without Vertex AI's enterprise security controls.
Deepnote - a collaborative notebook platform with real-time co-editing, less tied to a single cloud provider.
Amazon SageMaker Studio Lab - AWS's equivalent managed notebook environment for teams on that platform instead of GCP.
Lightning AI Studios - on-demand GPU notebooks with simpler per-hour pricing and less GCP-specific integration.
Kaggle Notebooks - a free notebook platform tied to Kaggle's dataset and competition ecosystem.

Bottom Line: Vertex AI Workbench is the right pick for data science teams already on Google Cloud who need deep BigQuery/Cloud Storage integration, scheduled notebook execution, and enterprise security controls, with pay-as-you-go pricing from $0.0261/hr. Teams wanting a free, zero-setup option should look at Google Colab or Kaggle Notebooks instead.

Last Tested: June 2026 | Reviewed by theaitoolsbox.com editorial team

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Vertex AI Workbench Review 2026: Pricing & Managed Notebooks

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