Google Vertex AI vs IBM Watson Studio 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 7.0. Google holds a 8.1% mindshare in AIDP, compared to IBM’s 1.5% 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

6,731 Views
5,991 Comparison Views

100% willing to recommend

IBM Watson Studio

Read 17 IBM Watson Studio reviews

2,182 Views
960 Comparison Views

100% willing to recommend

Google Vertex AI

IBM Watson Studio

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Executive SummaryUpdated on Dec 4, 2024

IBM Watson Studio and Google Vertex AI are tools in the AI sector aimed at data science needs. Google Vertex AI seems to have advantages thanks to its advanced features and integration capabilities, which justify its cost compared to IBM Watson Studio.

Features: IBM Watson Studio provides an integrated environment supporting various data sources and collaborative tools, a necessary feature for enterprise solutions. It includes a comprehensive array of connectors for seamless data importation, and its notebooks simplify data science tasks. Google Vertex AI, meanwhile, offers robust model training capabilities, scalability, and end-to-end AI pipelines. It also excels in automatic machine learning processes and integrates seamlessly with other Google Cloud services.

Room for Improvement: IBM Watson Studio could enhance its user interface to reduce the learning curve for new users and improve its integration speed and efficiency. While its support options are extensive, more streamlined deployment would be beneficial. Google Vertex AI could refine its pricing structure for more cost-effective solutions, expand its in-built library functionalities for broader data set applicability, and enhance user guides for aiding technical onboarding.

Ease of Deployment and Customer Service: IBM Watson Studio provides a flexible cloud deployment model with extensive support options, beneficial for complex needs. Google Vertex AI offers a streamlined deployment process enhanced by Google's infrastructure efficiencies; however, initial technical setup may require expertise. Thus, IBM Watson Studio appeals to businesses needing extensive support, while Google Vertex AI suits those wanting quick integration.

Pricing and ROI: IBM Watson Studio's competitive setup costs and adaptable plans promise high ROI with strong support offerings. In contrast, Google Vertex AI may entail higher initial costs but offers significant ROI through its efficient scaling and advanced AI capabilities. Choosing between them involves balancing immediate costs with long-term value for IBM when budget constraints exist, and opting for Google Vertex AI if high-performance AI solutions are a priority.

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

Buyer's Guide

Google Vertex AI vs. IBM Watson Studio

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 (6th)

IBM Watson Studio

Ranking in AI Development Platforms

17th

Average Rating

8.2

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Data Science Platforms (18th)

Mindshare comparison

As of January 2026, in the AI Development Platforms category, the mindshare of Google Vertex AI is 8.1%, down from 17.0% compared to the previous year. The mindshare of IBM Watson Studio is 1.5%, down from 1.7% 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 Studio	1.5%
Other	90.4%

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

Altan_Atabarut

Director, Channel and Alliances at Akinon

Automated processes improve efficiency while user interface and accessibility need enhancements

IBM Watson Studio, while powerful, lacks user-friendliness. It is not easy to use, particularly for medium or small enterprises or less experienced staff. Another aspect that requires improvement is the complexity involved in computer vision tasks. The integration capabilities have not significantly impacted workflow since there are simpler tools like Alteryx and Nine. The platform is associated with a complicated setup process and demands heavy hardware, making it expensive to scale. IBM should work on optimizing the user interface and enhancing the product's accessibility for medium and small enterprises.

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

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

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

"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."

"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."

"It provides the most valuable external analytics."

"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."

"The support is perfect and fantastic."

"The most useful function of Google Vertex AI for me is the ease of integration, as we can easily create a prompt and integrate it into our current system."

More Google Vertex AI pros

"Watson Studio is the most complete tool for AI projects."

"IBM Watson Studio has positively impacted my organization by increasing work efficiency, as it is an end-to-end ML pipeline in one place."

"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."

"It is a very stable and reliable solution."

"It has a lot of data connectors, which is extremely helpful."

"Watson Studio is the most complete tool for AI projects."

"Stability-wise, it is a great tool."

"Watson Studio is very stable."

More IBM Watson Studio pros

Cons

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

"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."

"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."

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

"Google can improve Google Vertex AI in terms of analysis and accuracy. When passing a very large context, instead of receiving vague responses, it would be better if the system could prompt users not to pass overly large prompts and provide clearer guidance on how to fine-tune Gemini for specific use cases."

"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

"It's sometimes easy to get lost given the number of images the solution opens up when you click on the mouse and the amount of different tabs."

"The solution's interface is very slow at times."

"IBM Watson Studio, while powerful, lacks user-friendliness. It is not easy to use, particularly for medium or small enterprises or less experienced staff."

"So a better user interface could be very helpful"

"Watson Studio would be improved with a clearer path for the deployment of docker images."

"Some of the solutions are really good solutions but they can be a little too costly for many."

"I think maybe the support is an area where it lacks."

"I want IBM's technical support team to provide more specific answers to queries."

More IBM Watson Studio cons

Pricing and Cost Advice

"The solution's pricing is moderate."

"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"

"IBM Watson Studio is an expensive solution."

"IBM Watson Studio is a reasonably priced product"

"Watson Studio's pricing is reasonable for what you get."

"The pricing is generally reasonable and straightforward but can vary significantly depending on the specific workloads in use."

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Top Industries

By visitors reading reviews

Computer Software Company

13%

Financial Services Firm

Manufacturing Company

Educational Organization

Financial Services Firm

12%

Manufacturing Company

11%

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

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	1
Large Enterprise	5

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 Studio?

IBM Watson Studio is considered rather expensive, with a rating of six or seven. The pricing could be optimized relative to the features and capabilities of the product.

See all answers

What needs improvement with IBM Watson Studio?

I think IBM Watson Studio can be improved. I wish learning IBM Watson Studio could be easier and more gradual, as it is a complex task. Also, I think pricing is a bit high.

See all answers

What is your primary use case for IBM Watson Studio?

My main use case for IBM Watson Studio is the end-to-end ML life cycle. A specific example of a project where I used IBM Watson Studio for the end-to-end machine learning life cycle is that I built...

See all answers

Comparisons

Azure OpenAI vs Google Vertex AI

Compared 21% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 14% of the time

Hugging Face vs Google Vertex AI

Compared 9% of the time

Amazon SageMaker vs Google Vertex AI

Compared 7% of the time

Amazon Bedrock vs Google Vertex AI

Compared 6% of the time

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Databricks vs IBM Watson Studio

Compared 13% of the time

Azure OpenAI vs IBM Watson Studio

Compared 9% of the time

Microsoft Azure Machine Learning Studio vs IBM Watson Studio

Compared 9% of the time

KNIME Business Hub vs IBM Watson Studio

Compared 7% of the time

IBM SPSS Statistics vs IBM Watson Studio

Compared 4% of the time

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Product Reports

Buyer's Guide

Google Vertex AI

January 2026

Download Google Vertex AI product report

Buyer's Guide

IBM Watson Studio

January 2026

Download IBM Watson Studio product report

Also Known As

No data available

Watson Studio, IBM Data Science Experience, Data Science Experience, DSx

Overview

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

Google

IBM Watson Studio provides tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. It gives you the flexibility to build models where your data resides and deploy anywhere in a hybrid environment so you can operationalize data science faster.

IBM

Sample Customers

Information Not Available

GroupM, Accenture, Fifth Third Bank

Buyer's Guide

Google Vertex AI vs. IBM Watson Studio

December 2025

Free Report: Google Vertex AI vs. IBM Watson Studio

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