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Google

Google Cloud Vertex AI

Composite Score

8.8 /10

CX Score

9.1 /10

109 Reviews Write a Review

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What is Google Cloud Vertex AI?

Cloud Machine Learning Engine is a managed service that lets developers and data scientists build and run superior machine learning models in production. Cloud ML Engine offers training and prediction services, which can be used together or individually. It has been used by enterprises to solve problems ranging from identifying clouds in satellite images, ensuring food safety, and responding four times faster to customer emails. The training and prediction services within ML Engine are now referred to as AI Platform Training and AI Platform Prediction.

Company Details

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Awards & Recognition

Google Cloud Vertex AI won the following awards in the Machine Learning Platforms category

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2025 Emotional Footprint Champion

Latest Award, 2025

2025 Data Quadrant Champion

2025

0 Top Rated Capabilities

1 Top Rated Features

2025

2024 Emotional Footprint Champion

2024

Service Experience

2024

2024 Data Quadrant Champion

2024

1 Top Rated Capabilities

4 Top Rated Features

2024

2023 Emotional Footprint Champion

2023

Strategy and Innovation
Service Experience
Contract Negotiation

2023

2023 Data Quadrant Champion

2023

5 Top Rated Capabilities

11 Top Rated Features

2023

Service Experience

2022

3 Top Rated Capabilities

9 Top Rated Features

2022

Google Cloud Vertex AI Ratings

Real user data aggregated to summarize the product performance and customer experience.
Download the entire Product Scorecard to access more information on Google Cloud Vertex AI.

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

89 Likeliness to Recommend

97 Plan to Renew

1
Since last award

81 Satisfaction of Cost Relative to Value

1
Since last award

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Emotional Footprint Overview

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

+95 Net Emotional Footprint

The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.

How much do users love Google Cloud Vertex AI?

2% Negative

3% Neutral

95% Positive

Pros

Respectful
Reliable
Efficient Service
Inspires Innovation

Feature Ratings

Average 82

Pre-Packaged AI/ML Services

Model Training

Feature Engineering

Explainability

Model Monitoring and Management

Openness and Flexibility

Model Tuning

Data Exploration and Visualization

Data Ingestion

Data Labeling

Algorithm Diversity

Vendor Capability Ratings

Average 80

Ease of Customization

Quality of Features

Ease of Implementation

Ease of Data Integration

Breadth of Features

Business Value Created

Ease of IT Administration

Availability and Quality of Training

Vendor Support

Product Strategy and Rate of Improvement

Usability and Intuitiveness

Google Cloud Vertex AI Reviews

Ritika B.

Role: Information Technology
Industry: Technology
Involvement: IT Development, Integration, and Administration

Validated Review

Verified Reviewer

Submitted Aug 2024

Likeliness to Recommend

10 /10

Pros

Continually Improving Product
Reliable
Performance Enhancing
Efficient Service

Siddharth C.

Role: Information Technology
Industry: Telecommunications
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted May 2024

Enhancing ML with vertex AI

Likeliness to Recommend

9 /10

What differentiates Google Cloud Vertex AI from other similar products?

Since vertex Ai is part of Google cloud environment the data we use for model development is easy to integrate and provides more layers of security. Integrating with data pipelines and other dependencies is made easier. Auto ML features are also more profound compared to other platforms.

What is your favorite aspect of this product?

Use of tensor flow for processing and other google owned libraries and resources makes it lot faster and convinient for model development and deployment. Deployment to cloud is also a matter of minutes.

What do you dislike most about this product?

Notebooks in vertex Ai could be mare more in sync with other googles visualization tools for the Analytical team with less coding base can visualize data that is in transit instead to wait till finish of the model.

What recommendations would you give to someone considering this product?

People who operate on cloud based environments should consider this as a choice without thinking much. It is cost efficient, user friendly, anyone who used Jupiter or Google Collab can easily be familiar with the environment.

Pros

Efficient Service
Reliable
Performance Enhancing
Enables Productivity

Cons

Vendor's Interest First
Under Delivered

Sikha S.

Role: Information Technology
Industry: Other
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Mar 2026

a unified platform for enterprise level learning

Likeliness to Recommend

10 /10

What differentiates Google Cloud Vertex AI from other similar products?

flexibility for both auto ml and custom modeling suitable for various use cases

What is your favorite aspect of this product?

increased team productivity and reduced development time through auto ml features

What do you dislike most about this product?

the ui is not intuitive in some cases especially for new users

What recommendations would you give to someone considering this product?

make effective use of vertex pipelines for complex workflows

Pros

Continually Improving Product
Performance Enhancing
Trustworthy
Efficient Service

Google Cloud Vertex AI

What is Google Cloud Vertex AI?

Company Details

Need Assistance?

Awards & Recognition

2025 Emotional Footprint Champion

2025 Data Quadrant Champion

2024 Emotional Footprint Champion

2024 Data Quadrant Champion

2023 Emotional Footprint Champion

2023 Data Quadrant Champion

Google Cloud Vertex AI Ratings

89 Likeliness to Recommend

97 Plan to Renew

1 Since last award

81 Satisfaction of Cost Relative to Value

1 Since last award

Emotional Footprint Overview

+95 Net Emotional Footprint

How much do users love Google Cloud Vertex AI?

Pros

Feature Ratings Average rating of all product features, some of which may not be shown below

Pre-Packaged AI/ML Services Pre-trained models and associated resources, e.g., for forecasting, recommendations, image and video analysis, NLP, voice, etc.

Model Training Ability to train a model with a range of data sets that you provide.

Feature Engineering Ability to extract and build features from raw data.

Explainability Ability to explain/ visualize the model.

Model Monitoring and Management Ability to monitor model use and performance in production, incl. identifying models for refresh and/or replacement.

Openness and Flexibility Integration with open-source and 3P resources (languages, frameworks, libraries, etc.)

Model Tuning Ability to fine-tune models based on monitoring feedback within the platform.

Data Exploration and Visualization Ability to visually manipulate, interact with, and explore data, and perform basic descriptive statistics and pattern detection.

Data Ingestion Ability to access data from various sources on-prem and in the cloud and of various type: structured, unstructured, and semi-structured, real-time, etc.

Data Labeling Ability to annotate/label data to create a training data set.

Algorithm Diversity Provides multiple diverse ready-to-use machine learning algorithms out-of-the-box.

Vendor Capability Ratings Average rating of all vendor capabilities, some of which may not be shown below

Ease of Customization Don't get bogged down in a difficult customization; use this data to make sure you can easily achieve the functionality you need for your particular situation.

Quality of Features Feature quality is just as important as quantity. Use this data to determine if this product will do what you're purchasing it to do, easily, intuitively, reliably, and effectively.

Ease of Implementation Successfully implementing new software is necessary to realize its full value and promote end user adoption. This data indicates whether or not the product is easy to implement.

Ease of Data Integration Use this data to determine whether the product will cause headaches or make data integration easy.

Breadth of Features Users prefer feature rich software that enables them to perform diverse series of tasks. This data expresses user satisfaction with the product's breadth of features.

Business Value Created Software needs to create value for employees, customers, partners, and, ultimately, shareholders. This data expresses user satisfaction - or lack thereof - with the product's business value.

Ease of IT Administration This data indicates whether IT personnel will be able to resolve issues and perform configurations efficiently and effectively.

Availability and Quality of Training Effective and readily available training enables users to get the most out of the software you've chosen. Use this section to make sure your vendor's training programs and materials measure up.

Vendor Support The importance of vendor support will vary for each organization depending on internal capabilities, but there will always be issues that only the vendor can resolve.

Product Strategy and Rate of Improvement Vendors who don't stay on top of emerging needs and trends won't enable you to meet your business goals. Use this data to separate innovators from imposters.

Usability and Intuitiveness End user learning curves cost the organization money. Pay attention to your end users' technical ability to determine how important UX is in your purchase.

Google Cloud Vertex AI Reviews

Ritika B.

Likeliness to Recommend

Pros

Siddharth C.

Enhancing ML with vertex AI

Likeliness to Recommend

Pros

Cons

Sikha S.

a unified platform for enterprise level learning

Likeliness to Recommend

Pros

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Feature Ratings

Pre-Packaged AI/ML Services

Model Training

Feature Engineering

Explainability

Model Monitoring and Management

Openness and Flexibility

Model Tuning

Data Exploration and Visualization

Data Ingestion

Data Labeling

Algorithm Diversity

Vendor Capability Ratings

Ease of Customization

Quality of Features

Ease of Implementation

Ease of Data Integration

Breadth of Features

Business Value Created

Ease of IT Administration

Availability and Quality of Training

Vendor Support

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Usability and Intuitiveness