Google Vertex AI vs IBM Watson Machine Learning comparison

Google and IBM are both solutions in the AI Development Platforms category. Google is ranked #3 with an average rating of 8.1, while IBM is ranked #17 with an average rating of 8.0. Google holds a 8.1% mindshare in AIDP, compared to IBM’s 2.0% mindshare. 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 14 Google Vertex AI reviews

6,813 Views
5,995 Comparison Views

100% willing to recommend

IBM Watson Machine Learning

Read 7 IBM Watson Machine Learning reviews

1,150 Views
1,150 Comparison Views

100% willing to recommend

Google Vertex AI

IBM Watson Machine Learning

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 4, 2024

IBM Watson Machine Learning and Google Vertex AI are leading products in the machine learning domain. Google Vertex AI excels due to superior integration features, whereas IBM Watson Machine Learning is preferred for better pricing and customer support.

Features: IBM Watson Machine Learning provides comprehensive tools for model development, training, and deployment. It offers robust natural language processing capabilities and seamless integration with various data platforms. Google Vertex AI delivers a full-featured platform with strong integration across Google services. It supports extensive cloud resources and offers advanced AI capabilities to manage complex workflows efficiently.

Room for Improvement: IBM Watson Machine Learning could enhance integration within diverse digital ecosystems, further improving ease of use. More flexibility in customization options for machine learning models could be beneficial. Increased clarity in documentation could support user engagement. Google Vertex AI may require a more straightforward initial setup process. Enhanced support for users unfamiliar with Google's ecosystem could be beneficial. Refinement in cost transparency can aid better budgeting for users.

Ease of Deployment and Customer Service: IBM Watson Machine Learning allows seamless deployment with responsive support, making onboarding effortless. Its customer support is highly effective, facilitating continuous assistance. Google Vertex AI offers a streamlined deployment process through its cloud integration, although initial setup could be challenging for some users. However, its support system is robust once deployment hurdles are managed.

Pricing and ROI: IBM Watson Machine Learning provides a competitive pricing structure, appealing to budget-conscious users seeking cost-effective solutions with a promising ROI. Google Vertex AI's pricing is higher initially, but its extensive capabilities and potential for enhanced services can yield substantial ROI for enterprises investing in advanced AI strategies.

To learn more, read our detailed Google Vertex AI vs. IBM Watson Machine Learning Report (Updated: December 2025).

Buyer's Guide

Google Vertex AI vs. IBM Watson Machine Learning

December 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

3rd

Average Rating

8.2

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

AI-Agent Builders (5th)

IBM Watson Machine Learning

Ranking in AI Development Platforms

17th

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2026, in the AI Development Platforms category, the mindshare of Google Vertex AI is 8.1%, down from 16.1% compared to the previous year. The mindshare of IBM Watson Machine Learning is 2.0%, up from 1.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms Market Share Distribution
Product	Market Share (%)
Google Vertex AI	8.1%
IBM Watson Machine Learning	2.0%
Other	89.9%

AI Development Platforms

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.

Read full review

Anurag Mayank

Manager at Maruti Suzuki India Limited

A highly efficient solution that delivers the desired results to its users

I had not considered how the solution could be improved because I was focused on how it was helping me to solve my issues. If I consider how we want to use it in our organization, certain areas of improvement can be addressed. For instance, we want to use it with Generative AI, not like ChatGPT, but in a way intended for industrial use. It would be beneficial to incorporate more AI into the solution.

Read full review

Quotes from Members

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

Pros

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

"Google Vertex AI is an out-of-the-box and very easy-to-use solution."

"The most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."

"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."

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

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

"With just one single platform, Google Vertex AI platform, we can achieve everything; we need not switch over to multiple tools, multiple platforms, as everything can be accomplished through this one single platform for integration with existing workflows, systems, tools, and databases."

More Google Vertex AI pros

"The solution is very valuable to our organization due to the fact that we can work on it as a workflow."

"It is has a lot of good features and we find the image classification very useful."

"I was particularly interested in trying the AutoML feature to see how it handles data and proposes new models. The variety of models it provides is impressive."

"Scalability-wise, I rate the solution ten out of ten."

"The most valuable aspect of the solution's the cost and human labor savings."

"It has improved self-service and customer satisfaction."

"We can enable and change developer productivity with artificial intelligence-recommended code based on natural language input or exciting source code."

Cons

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

"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."

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

"We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models."

"The tool's documentation is not good. It is hard."

"I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow."

"It takes a considerable amount of time to process, and I understand the technology behind why it takes this long, but this is something that could be reduced."

More Google Vertex AI cons

"The supporting language is limited."

"They should add more GPU processing power to improve performance, especially when dealing with large amounts of data."

"In future releases, I would like to see a more flexible environment."

"Honestly, I haven't seen any comparative report that has run the same data through two different artificial intelligence or machine learning capabilities to get something out of it. I would love to see that."

"Sometimes training the model is difficult."

"If I consider how we want to use it in our organization, certain areas of improvement can be addressed. For instance, we want to use it with Generative AI, not like ChatGPT, but in a way intended for industrial use."

"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."

Pricing and Cost Advice

"The price structure is very clear"

"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 solution's pricing is moderate."

"The pricing model is good."

"I've only been using the free tier, but it's quite competitive on a service basis."

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Top Industries

By visitors reading reviews

Computer Software Company

12%

Financial Services Firm

Manufacturing Company

Educational Organization

University

14%

Financial Services Firm

12%

Educational Organization

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	7

No data available

Questions from the Community

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

I purchased Google Vertex AI directly from Google, as we are a partner of Google. I would rate the pricing for Google Vertex AI as low; the price is affordable.

See all answers

What needs improvement with Google Vertex AI?

We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models. We have to do some fine-tuning, hyperparameter optimization, and othe...

See all answers

What is your primary use case for Google Vertex AI?

We are developing AI models and agents using Google Vertex AI platform, and we are deploying them using Google Vertex AI platform on Google Cloud Platform, GCP. With just one single platform, Googl...

See all answers

What is your experience regarding pricing and costs for IBM Watson Machine Learning?

The solution is expensive.

See all answers

What needs improvement with IBM Watson Machine Learning?

Sometimes training the model is difficult. We need to have at least a few different components to evaluate and understand the behavior of different users to have a very, very high accuracy in the m...

See all answers

What is your primary use case for IBM Watson Machine Learning?

We use different artificial intelligence models to build questions and get answers for clients.

See all answers

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Compared 32% of the time

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Compared 15% of the time

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Compared 11% of the time

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Compared 10% of the time

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January 2026

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AI Development Platforms

February 2026

Download IBM Watson Machine Learning product report

Overview

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

Google

IBM Watson Machine Learning helps data scientists and developers accelerate AI and machine-learning deployment. With its open, extensible model operation, Watson Machine Learning helps businesses simplify and harness AI at scale across any cloud.

IBM

Buyer's Guide

Google Vertex AI vs. IBM Watson Machine Learning

December 2025

Free Report: Google Vertex AI vs. IBM Watson Machine Learning

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