Amazon SageMaker vs IBM Watson Studio comparison

Amazon Web Services (AWS) and IBM are both solutions in the Data Science Platforms category. Amazon Web Services (AWS) is ranked #3 with an average rating of 8.0, while IBM is ranked #12 with an average rating of 8.4. Amazon Web Services (AWS) holds a 7.3% mindshare in Data Science Platforms, compared to IBM’s 1.9% mindshare. Additionally, 91% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

Amazon SageMaker

Read 36 Amazon SageMaker reviews

6,112 Views
4,892 Comparison Views

91% willing to recommend

IBM Watson Studio

Read 14 IBM Watson Studio reviews

1,997 Views
1,233 Comparison Views

100% willing to recommend

Amazon SageMaker

IBM Watson Studio

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 4, 2024

IBM Watson Studio and Amazon SageMaker are both designed for machine learning model development and deployment. IBM Watson Studio appears to have an advantage in pricing and customer support, while Amazon SageMaker excels in its comprehensive feature set.

Features: IBM Watson Studio offers a collaborative environment with intuitive tools, AutoAI technology for model selection, and easy data integration. Amazon SageMaker provides comprehensive services for model building, advanced algorithms, seamless AWS integration, and features like the SageMaker Studio and AutoPilot for simplified model management.

Room for Improvement: IBM Watson Studio could improve on integration with non-IBM platforms, offer more advanced features for large datasets, and enhance scalability for extensive applications. Amazon SageMaker might enhance its user interface for newcomers, refine its pricing transparency, and improve customer service personalization.

Ease of Deployment and Customer Service: IBM Watson Studio offers a straightforward deployment process and superior customer service, with cloud-based and hybrid solutions that easily meet diverse deployment needs. Amazon SageMaker leverages AWS’s vast cloud infrastructure for flexible deployments but lacks the personal touch in customer service that some users prefer.

Pricing and ROI: IBM Watson Studio is recognized for competitive pricing and good ROI with low setup costs, attractive for budget-constrained organizations. Amazon SageMaker justifies its higher cost with features and integration that offer greater ROI potential for larger enterprises in need of scalable machine learning solutions.

To learn more, read our detailed Amazon SageMaker vs. IBM Watson Studio Report (Updated: March 2025).

Buyer's Guide

Amazon SageMaker vs. IBM Watson Studio

March 2025

Download the complete report

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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

Amazon SageMaker

Ranking in Data Science Platforms

3rd

Ranking in AI Development Platforms

5th

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

IBM Watson Studio

Ranking in Data Science Platforms

12th

Ranking in AI Development Platforms

10th

Average Rating

8.4

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 7.3%, down from 9.8% compared to the previous year. The mindshare of IBM Watson Studio is 1.9%, down from 2.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Hemant Paralkar

Lead Consultant at Saama

Improves team collaboration with advanced feature sharing but needs a better user experience

Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background. Additionally, dealing with frequent UI updates can be challenging, especially for infrastructure architects like myself. It involves effort to migrate to new UIs, making the updates not seamless. User auditing requires enhancements as tracking operations performed by users can be difficult due to dynamic IP validation and role propagation.

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

"Allows you to create API endpoints."

"The most tool's valuable feature, in my experience, is hyperparameter tuning. It allows us to test different parameters for the same model in parallel, which helps us quickly identify the configuration that yields the highest accuracy. This parallel computing capability saves us a lot of time."

"SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."

"The few projects we have done have been promising."

"The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."

"SageMaker is a comprehensive platform where I can perform all machine learning activities."

"The technical support of the tool was good."

"I recommend SageMaker for ML projects if you need to build models from scratch."

More Amazon SageMaker pros

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

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

"Watson Studio is very stable."

"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."

"IBM Watson Studio consistently automates across channels."

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

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

"It is a stable, reliable product."

More IBM Watson Studio pros

Cons

"I would recommend having more walkthrough videos and articles beyond AWS Skill Builder."

"One area where Amazon SageMaker could improve is its pricing. The high costs can drive companies to explore other cloud options. Additionally, while generally good, the updates sometimes come with bugs, and the documentation could be much better. More examples and clearer guidance would be helpful."

"AI is a new area and AWS needs to have an internship training program available."

"The model repository is a concern as models are stored on a bucket and there's an issue with versioning."

"One area for improvement is the pricing, which can be quite high."

"Having all documentation easily accessible on the front page of SageMaker would be a great improvement."

"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."

"The entry point can be a bit difficult. Having all documentation easily accessible on the front page of SageMaker would be a great improvement."

More Amazon SageMaker cons

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

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

"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."

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

"One area that could be improved is the backup and restoration of the database and the overall database configuration."

"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."

"One area that could be improved is the backup and restoration of the database and the overall database configuration."

More IBM Watson Studio cons

Pricing and Cost Advice

"The pricing could be better, especially for querying. The per-query model feels expensive."

"Amazon SageMaker is a very expensive product."

"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."

"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."

"SageMaker is worth the money for our use case."

"I rate the pricing a five on a scale of one to ten, where one is the lowest price, and ten is the highest price. The solution is priced reasonably. There is no additional cost to be paid in excess of the standard licensing fees."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a six out of ten."

"The solution is relatively cheaper."

More Amazon SageMaker pricing and cost advice

"IBM Watson Studio is a reasonably priced product"

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

"IBM Watson Studio is an expensive solution."

"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

Financial Services Firm

19%

Educational Organization

13%

Computer Software Company

11%

Manufacturing Company

Financial Services Firm

15%

Computer Software Company

12%

Manufacturing Company

10%

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

See all answers

What do you like most about Amazon SageMaker?

We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for t...

See all answers

What is your experience regarding pricing and costs for Amazon SageMaker?

Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.

See all answers

What do you like most about IBM Watson Studio?

Stability-wise, it is a great tool.

See all answers

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

The pricing of Watson Studio is justified by the benefits and power it offers.

See all answers

What needs improvement with IBM Watson Studio?

One area that could be improved is the backup and restoration of the database and the overall database configuration. There were also challenges with programming the network extension in the last p...

See all answers

Comparisons

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Compared 17% of the time

Azure OpenAI vs Amazon SageMaker

Compared 12% of the time

Dataiku vs Amazon SageMaker

Compared 10% of the time

Google Vertex AI vs Amazon SageMaker

Compared 9% of the time

Domino Data Science Platform vs Amazon SageMaker

Compared 7% of the time

More Amazon SageMaker Competitors

Databricks vs IBM Watson Studio

Compared 26% of the time

Microsoft Azure Machine Learning Studio vs IBM Watson Studio

Compared 15% of the time

Azure OpenAI vs IBM Watson Studio

Compared 14% of the time

Google Vertex AI vs IBM Watson Studio

Compared 14% of the time

Alteryx vs IBM Watson Studio

Compared 3% of the time

More IBM Watson Studio Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

March 2025

Download Amazon SageMaker product report

Buyer's Guide

IBM Watson Studio

March 2025

Download IBM Watson Studio product report

Also Known As

AWS SageMaker, SageMaker

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

Overview

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Amazon Web Services (AWS)

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

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit

GroupM, Accenture, Fifth Third Bank

Buyer's Guide

Amazon SageMaker vs. IBM Watson Studio

March 2025

Free Report: Amazon SageMaker vs. IBM Watson Studio

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