Google Vertex AI vs IBM Watson Studio comparison

Google and IBM are both solutions in the AI Development Platforms category. Google is ranked #2 with an average rating of 8.3, while IBM is ranked #13 with an average rating of 8.5. Google holds a 10.3% 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

7,456 Views
6,710 Comparison Views

100% willing to recommend

IBM Watson Studio

Read 15 IBM Watson Studio reviews

2,091 Views
941 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: September 2025).

Buyer's Guide

Google Vertex AI vs. IBM Watson Studio

September 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

8.2

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

AI-Agent Builders (4th)

IBM Watson Studio

Ranking in AI Development Platforms

13th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Data Science Platforms (12th)

Mindshare comparison

As of October 2025, in the AI Development Platforms category, the mindshare of Google Vertex AI is 10.3%, down from 19.5% compared to the previous year. The mindshare of IBM Watson Studio is 1.5%, down 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	10.3%
IBM Watson Studio	1.5%
Other	88.2%

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

Abilio Duarte

Senior Solutions Architect at Self-employed

A highly robust and well-documented platform that simplifies the complex world of AI

The main challenge lies in visibility and ease of use. Providing training sessions can be immensely helpful in helping users navigate and understand the tool's potential. This approach would empower users to explore and make the most of the tools and technologies at their disposal. Another area where IBM could enhance its offering is by providing more visibility to end users regarding the vast potential that Watson offers.

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

"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."

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

"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."

"It provides the most valuable external analytics."

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

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

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

"The support is perfect and fantastic."

More Google Vertex AI pros

"The system's ability to take a look at data, segment it and then use that data very differently."

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

"The solution is very easy to use."

"The scalability of IBM Watson Studio is great."

"The main benefit is the ease of use. We see a lot of engineers in our site and customers that really like the way the tools are able to work with the people."

"It is a stable, reliable product."

"IBM Watson Studio consistently automates across channels."

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

More IBM Watson Studio pros

Cons

"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."

"The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time."

"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."

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

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

"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."

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

More Google Vertex AI cons

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

"The main challenge lies in visibility and ease of use."

"The initial setup was complex."

"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."

"So a better user interface could be very helpful"

"Initially, it was quite complex. For us, it was not only a matter of getting it installed, that was just a start. It was also trying to come up with a standard way of implementing it across the entire organization, which had been a challenge."

"We would like to see it more web-based with more functionality."

"We would like to see it less as one big, massive product, but more based on smaller services that we can then roll out to consumers."

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 a reasonably priced product"

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

"IBM Watson Studio is an expensive solution."

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

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Top Industries

By visitors reading reviews

Computer Software Company

14%

Financial Services Firm

10%

Manufacturing Company

Educational Organization

Financial Services Firm

12%

Manufacturing Company

11%

Computer Software Company

10%

University

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	11
Midsize Enterprise	1
Large Enterprise	4

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?

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 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?

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 i...

See all answers

What is your primary use case for IBM Watson Studio?

In my role as an integrator, my primary use cases for IBM Watson Studio ( /products/ibm-watson-studio-reviews ) involve advanced analytics and predictive analytics for telecommunications, banks, an...

See all answers

Comparisons

Azure OpenAI vs Google Vertex AI

Compared 28% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 13% of the time

Hugging Face vs Google Vertex AI

Compared 12% of the time

Amazon SageMaker vs Google Vertex AI

Compared 7% of the time

IBM Watson Machine Learning vs Google Vertex AI

Compared 2% of the time

More Google Vertex AI Competitors

Databricks vs IBM Watson Studio

Compared 21% of the time

Microsoft Azure Machine Learning Studio vs IBM Watson Studio

Compared 12% of the time

Azure OpenAI vs IBM Watson Studio

Compared 12% of the time

KNIME Business Hub vs IBM Watson Studio

Compared 7% of the time

Amazon SageMaker vs IBM Watson Studio

Compared 6% of the time

More IBM Watson Studio Competitors

Product Reports

Buyer's Guide

Google Vertex AI

October 2025

Download Google Vertex AI product report

Buyer's Guide

IBM Watson Studio

October 2025

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

September 2025

Free Report: Google Vertex AI vs. IBM Watson Studio

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