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Google

Google Cloud Vertex AI

Composite Score

8.8 /10

CX Score

9.1 /10

105 Reviews Write a Review

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.

88 Likeliness to Recommend

1
Since last award

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.

+94 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
Inspires Innovation
Efficient Service

Feature Ratings

Average 81

Model Training

Pre-Packaged AI/ML Services

Explainability

Feature Engineering

Openness and Flexibility

Model Monitoring and Management

Data Exploration and Visualization

Model Tuning

Data Labeling

Data Ingestion

Algorithm Diversity

Vendor Capability Ratings

Average 79

Quality of Features

Ease of Customization

Ease of Implementation

Business Value Created

Ease of IT Administration

Ease of Data Integration

Breadth of Features

Vendor Support

Availability and Quality of Training

Product Strategy and Rate of Improvement

Usability and Intuitiveness

Google Cloud Vertex AI Reviews

Swastik P.

Role: Student Academic
Industry: Engineering
Involvement: Initial Implementation

Validated Review

Verified Reviewer

Submitted Mar 2024

Great features for machine learning. Easy to use!

Likeliness to Recommend

8 /10

What differentiates Google Cloud Vertex AI from other similar products?

It's user friendly-ness and how easy it is to maneuver across it.

What is your favorite aspect of this product?

It generates n number of ML models so we can choose any which we like

What do you dislike most about this product?

It's price.

What recommendations would you give to someone considering this product?

It's very easy to go about, I'll recommend that person to visit each and every feature first.

Pros

Transparent
Caring
Respectful
Client Friendly Policies

Vishal R.

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

Validated Review

Verified Reviewer

Submitted Jan 2026

Best launchpad for AI models

Likeliness to Recommend

8 /10

What differentiates Google Cloud Vertex AI from other similar products?

Google vertex AI stands out because it is clean and get the job done in one place. The best thing of this is deeply integration with Kubernetes, BigQuery and Gemini which provides full flexibility.

What is your favorite aspect of this product?

The workflow feels very clean and smooth in vertex AI. The Model Garden is the best feature where you can find multiple pre-trained models for your project.

What do you dislike most about this product?

I have not encountered any difficulties till now.

What recommendations would you give to someone considering this product?

Before you start, i suggest to have basic knowledge of machine learning and automation tools.

Pros

Reliable
Performance Enhancing
Trustworthy
Unique Features

Areeb A.

Role: Consultant
Industry: Consulting
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Nov 2025

Google's AI Solution!

Likeliness to Recommend

9 /10

What differentiates Google Cloud Vertex AI from other similar products?

Its integration with Gemini is really helpful.

What is your favorite aspect of this product?

Good quality models can be build in with minimum coding effort.

What do you dislike most about this product?

The subscription cost is a bit too high. There are instances when I faced technical glitches as well.

What recommendations would you give to someone considering this product?

The application glitches a bit so I recommend storing data in cloud for efficient usage,

Pros

Reliable
Continually Improving Product
Performance Enhancing
Enables Productivity

How We Deliver

Events

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

88 Likeliness to Recommend

1 Since last award

97 Plan to Renew

1 Since last award

81 Satisfaction of Cost Relative to Value

1 Since last award

Emotional Footprint Overview

+94 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

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

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

Explainability Ability to explain/ visualize the model.

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

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

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

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

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

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

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.

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

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 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.

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.

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.

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.

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.

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.

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

Swastik P.

Great features for machine learning. Easy to use!

Likeliness to Recommend

Pros

Vishal R.

Best launchpad for AI models

Likeliness to Recommend

Pros

Areeb A.

Google's AI Solution!

Likeliness to Recommend

Pros

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1
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1
Since last award

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Since last award

Feature Ratings

Model Training

Pre-Packaged AI/ML Services

Explainability

Feature Engineering

Openness and Flexibility

Model Monitoring and Management

Data Exploration and Visualization

Model Tuning

Data Labeling

Data Ingestion

Algorithm Diversity

Vendor Capability Ratings

Quality of Features

Ease of Customization

Ease of Implementation

Business Value Created

Ease of IT Administration

Ease of Data Integration

Breadth of Features

Vendor Support

Availability and Quality of Training

Product Strategy and Rate of Improvement

Usability and Intuitiveness