Google

Google Cloud Vertex AI

Composite Score

8.8 /10

CX Score

9.1 /10

93 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 Data Quadrant Champion

Latest Award, 2025

0 Top Rated Capabilities

1 Top Rated Features

Latest Award, 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

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

98 Plan to Renew

78 Satisfaction of Cost Relative to Value

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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

2% Neutral

96% Positive

Pros

Performance Enhancing
Reliable
Inspires Innovation
Respectful

Feature Ratings

Average 81

Model Training

Explainability

Data Exploration and Visualization

Model Monitoring and Management

Pre-Packaged AI/ML Services

Feature Engineering

Data Ingestion

Openness and Flexibility

Data Labeling

Algorithm Diversity

Model Tuning

Vendor Capability Ratings

Average 79

Ease of Customization

Quality of Features

Ease of Implementation

Ease of Data Integration

Ease of IT Administration

Business Value Created

Breadth of Features

Availability and Quality of Training

Vendor Support

Product Strategy and Rate of Improvement

Usability and Intuitiveness

Google Cloud Vertex AI Reviews

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

Validated Review

Anonymous Reviewer

Submitted Aug 2023

Review for Google Cloud Vertex AI

Likeliness to Recommend

8 /10

Pros

Inspires Innovation
Caring
Respectful
Friendly Negotiation

Cons

Less Productive
Charges for Enhancements

Sashank M.

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

Validated Review

Verified Reviewer

Submitted Jun 2023

Fantastic product and features live upto

Likeliness to Recommend

9 /10

What differentiates Google Cloud Vertex AI from other similar products?

Unified Platform: Google Cloud Vertex AI provides a unified platform for end-to-end machine learning development and deployment. AutoML Capabilities: Google Cloud Vertex AI emphasizes automated machine learning (AutoML) capabilities, enabling users with limited machine learning expertise to build and deploy models. Customizability and Openness: While Google Cloud Vertex AI provides AutoML capabilities, it also offers flexibility for advanced users and developers.

What is your favorite aspect of this product?

Pre-built Components and Pipelines: Google Cloud Vertex AI provides pre-built components and pipelines, known as Vertex Pipelines, to streamline the development and deployment of machine learning workflows. Integration with Google Cloud Services: Google Cloud Vertex AI seamlessly integrates with other Google Cloud services, such as BigQuery for data storage and querying, Cloud Storage for data storage and retrieval, and Dataflow for data processing. Google AI Ecosystem: Google Cloud Vertex AI benefits from Google's expertise and innovations in machine learning and artificial intelligence.

What do you dislike most about this product?

Learning Curve for Advanced Features: While Google Cloud Vertex AI provides a user-friendly experience and AutoML capabilities, utilizing advanced features and customizing workflows may require a certain level of expertise in machine learning and Google Cloud Platform. Complexity for Beginners: Similar to other machine learning platforms, Google Cloud Vertex AI can be complex for beginners. Cost Management: Utilizing machine learning services, including Google Cloud Vertex AI, can incur costs. Vendor Lock-In: Google Cloud Vertex AI is part of the Google Cloud ecosystem, and using it ties users to Google Cloud Platform.

What recommendations would you give to someone considering this product?

Define Your Goals: Clearly define your machine learning goals and requirements. Identify the specific tasks you want to accomplish, such as image classification, natural language processing, or recommendation systems. Learn Machine Learning Fundamentals: Familiarize yourself with the fundamentals of machine learning, including concepts like supervised learning, unsupervised learning, and evaluation metrics. Explore AutoML Capabilities: Google Cloud Vertex AI offers powerful AutoML capabilities that automate various aspects of the machine learning workflow.

Pros

Helps Innovate
Performance Enhancing
Enables Productivity
Trustworthy

Avineet A.

Role: Information Technology
Industry: Technology
Involvement: IT Leader or Manager

Validated Review

Verified Reviewer

Submitted May 2023

Great new AI capability from Google

Likeliness to Recommend

9 /10

What differentiates Google Cloud Vertex AI from other similar products?

It has much more purpose built models and is easier to implement than others. Has better accuracy and great to see it includes good integration with BigQuery etc.

What is your favorite aspect of this product?

That it has purpose built models and can start without many code lines to write.

What do you dislike most about this product?

That the interface can be improved and be more versatile

What recommendations would you give to someone considering this product?

Implement it with the right set of data and select the recommended models for more accurate output

Pros

Trustworthy
Effective Service
Generous Negotitation
Appreciates Incumbent Status

Google Cloud Vertex AI

What is Google Cloud Vertex AI?

Company Details

Need Assistance?

Awards & Recognition

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

98 Plan to Renew

78 Satisfaction of Cost Relative to Value

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.

Explainability Ability to explain/ visualize the model.

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

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

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

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

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.

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

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

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.

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

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.

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.

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

Review for Google Cloud Vertex AI

Likeliness to Recommend

Pros

Cons

Sashank M.

Fantastic product and features live upto

Likeliness to Recommend

Pros

Avineet A.

Great new AI capability from Google

Likeliness to Recommend

Pros

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Feature Ratings

Model Training

Explainability

Data Exploration and Visualization

Model Monitoring and Management

Pre-Packaged AI/ML Services

Feature Engineering

Data Ingestion

Openness and Flexibility

Data Labeling

Algorithm Diversity

Model Tuning

Vendor Capability Ratings

Ease of Customization

Quality of Features

Ease of Implementation

Ease of Data Integration

Ease of IT Administration

Business Value Created

Breadth of Features

Availability and Quality of Training

Vendor Support

Product Strategy and Rate of Improvement

Usability and Intuitiveness