Google Vertex AI vs watsonx.ai comparison

Google and IBM are both solutions in the AI Development Platforms category. Google is ranked #2 with an average rating of 8.4, while IBM is ranked #19 with an average rating of 7.0. Additionally, 100% of Google users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

Google Vertex AI

Read 12 Google Vertex AI reviews

8,787 Views
7,908 Comparison Views

100% willing to recommend

watsonx.ai

Read 1 watsonx.ai review

40 Views
17 Comparison Views

100% willing to recommend

Google Vertex AI

watsonx.ai

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Executive Summary

We performed a comparison between Google Vertex AI and watsonx.ai based on real PeerSpot user reviews.

Find out what your peers are saying about Microsoft, Google, Hugging Face and others in AI Development Platforms.

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July 2025

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Google Vertex AI

Ranking in AI Development Platforms

2nd

Average Rating

8.4

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

AI Infrastructure (1st)

watsonx.ai

Ranking in AI Development Platforms

19th

Average Rating

7.0

Reviews Sentiment

5.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Featured Reviews

Hamada Farag

Technology Consultant at Beta Information Technology

Customization and integration empower diverse AI applications

We are familiar with most Google Cloud services, particularly infrastructure services, storage, compute, AI tools, containerization, GCP containerization, and cloud SQL. We are familiar with approximately eighty percent of Google's services, primarily related to infrastructure, AI, containers, backup, storage, and compute. We are familiar with Gemini AI and Google Vertex AI, and we have completed some exercises and cases with our customers for Google AI. We use automation in machine learning. I work with a team where everyone has specific responsibilities. We have design and development processes in place. Based on my experience, I would rate Google Vertex AI a 9 out of 10.

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AmerKhan

Senior Director - Head of Solution Engineering at Osol tech (Private) Limited

Experience gains better customer interactions and user-friendliness but requires more efficient agent development

The development toolkit itself and the engine that supported the agent development was flexible. The features include support for RAG and support for generative AI. What we're looking to do is provide a human-like interface where natural language comes into play to serve HR data. Our users interact in a narrative fashion and get their queries answered. It has increased our serving of HR requests by 30%. It is user friendly, and our user base was able to work with it quite conveniently.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The integration of AutoML features streamlines our machine-learning workflows."

"It provides the most valuable external analytics."

"We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently."

"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."

"Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."

"The support is perfect and fantastic."

"Vertex comes with inbuilt integration with GCP for data storage."

"The most valuable features of the solution are that it is quite flexible, and some of the services are almost low-code, with no-code services, so it gives agents flexibility to build the use cases according to the operational needs."

More Google Vertex AI pros

"The development toolkit itself and the engine that supported the agent development was flexible."

Cons

"I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process."

"I'm not sure if I have suggestions for improvement."

"Google Vertex AI is good in machine learning and AI, but it lacks optimization."

"I think the technical documentation is not readily available in the tool."

"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."

"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."

"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."

"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."

More Google Vertex AI cons

"The platform and toolkit that we use to develop agents could benefit from improvements from a user-friendliness perspective."

Pricing and Cost Advice

"The Versa AI offers attractive pricing. With this pricing structure, I can leverage various opportunities to bring value to my business. It's a positive aspect worth considering."

"I think almost every tool offers a decent discount. In terms of credits or other stuff, every cloud provider provides a good number of incentives to onboard new clients."

"The price structure is very clear"

"The solution's pricing is moderate."

Information not available

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Top Industries

By visitors reading reviews

Computer Software Company

13%

Financial Services Firm

12%

Manufacturing Company

Educational Organization

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about Google Vertex AI?

We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for trai...

See all answers

What is your experience regarding pricing and costs for Google Vertex AI?

They have different pricing models like pay-as-you-go or subscription model, and total cost of ownership. It is comparatively cheaper than Azure.

See all answers

What needs improvement with Google Vertex AI?

Google Vertex AI is one of the best in the market, followed by Azure AI. It can be rated at eight or nine out of ten. It is not completely mature and needs some features and functions. The interfac...

See all answers

What needs improvement with watsonx.ai?

Improving on the development toolkit would help. The platform and toolkit that we use to develop agents could benefit from improvements from a user-friendliness perspective. Making it more user-fri...

See all answers

What is your primary use case for watsonx.ai?

We are looking to develop HR agents on it, HR-based bots. We integrated it with our HR system, HRIS.

See all answers

What advice do you have for others considering watsonx.ai?

This solution is highly recommended. On a scale of 1-10, I rate watsonx.ai a seven out of ten.

See all answers

Comparisons

Azure OpenAI vs Google Vertex AI

Compared 34% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 15% of the time

Hugging Face vs Google Vertex AI

Compared 11% of the time

Amazon SageMaker vs Google Vertex AI

Compared 7% of the time

TensorFlow vs Google Vertex AI

Compared 4% of the time

More Google Vertex AI Competitors

No data available

Product Reports

Buyer's Guide

Google Vertex AI

July 2025

Download Google Vertex AI product report

Buyer's Guide

AI Development Platforms

July 2025

Download watsonx.ai product report

Overview

Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.

Google

watsonx.ai is an advanced AI platform designed to streamline AI model development, deployment, and management. Tailored for businesses, it enhances operational efficiency and decision-making with its robust analytics and machine learning capabilities.

watsonx.ai integrates seamlessly with existing systems, offering scalable AI solutions tailored to a wide range of industry needs. It focuses on delivering real-time insights and data-driven strategies, making complex data handling easier and more efficient. Designed for knowledgeable audiences, its capabilities in AI model lifecycle management set it apart. Adaptable and user-friendly, it supports businesses in harnessing AI for innovation and growth.

What are the most valuable features of watsonx.ai?

Model Lifecycle Management: Facilitates seamless end-to-end AI model operations from development to deployment.
Real-Time Analytics: Offers immediate insights allowing swift data-driven decisions.
Scalability: Easily adapts to growing business requirements for AI solutions.
Integration: Connects smoothly with enterprise systems enhancing data interoperability.
Customization: Tailors functionalities to specific business domains ensuring relevance and efficiency.

What benefits or ROI should users consider?

Increased Efficiency: Automates complex tasks reducing time and effort.
Cost Reduction: Optimizes AI operations leading to overall cost savings.
Improved Data Accuracy: Enhances precision in data processing and insights generation.
Innovation Facilitation: Supports development of new AI-driven products and solutions.
Scalability: Grows with business needs allowing continuous improvement and expansion.

In industries like healthcare, watsonx.ai improves diagnostic accuracy with its data analysis capabilities. Financial services leverage its predictive models for risk assessment. Retail businesses use its insights for targeted customer strategies. Its industry-wide adaptability allows businesses to stay competitive with advanced AI integration.

IBM

Buyer's Guide

AI Development Platforms

July 2025

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