Amazon SageMaker vs IBM Watson Machine Learning comparison

Amazon Web Services (AWS) and IBM are both solutions in the AI Development Platforms category. Amazon Web Services (AWS) is ranked #5 with an average rating of 8.0, while IBM is ranked #14 with an average rating of 8.0. Amazon Web Services (AWS) holds a 5.5% mindshare in AIDP, compared to IBM’s 1.8% 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 37 Amazon SageMaker reviews

3,898 Views
3,122 Comparison Views

91% willing to recommend

IBM Watson Machine Learning

Read 7 IBM Watson Machine Learning reviews

1,194 Views
934 Comparison Views

100% willing to recommend

Amazon SageMaker

IBM Watson Machine Learning

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Executive SummaryUpdated on Dec 4, 2024

Amazon SageMaker and IBM Watson Machine Learning compete in the machine learning platform space. SageMaker holds an upper hand in pricing and customer support, while IBM Watson stands out with its feature set despite higher costs.

Features: Amazon SageMaker offers an integrated development environment, seamless model training and deployment, and automatic model tuning. IBM Watson Machine Learning provides robust AI capabilities, comprehensive enterprise solutions, and supports a wide range of machine learning frameworks.

Room for Improvement: Amazon SageMaker could enhance certain advanced AI capabilities and broaden its framework support. It may also refine enterprise solution integrations. IBM Watson Machine Learning might improve its pricing strategy and ease of deployment, and streamline customer support processes.

Ease of Deployment and Customer Service: Amazon SageMaker is known for flexible deployment and excellent support, providing an efficient setup experience. IBM Watson Machine Learning offers a structured deployment process fit for enterprise-scale operations, albeit requiring more initial configuration.

Pricing and ROI: Amazon SageMaker is cost-effective, offering solid ROI for small to medium enterprises. IBM Watson Machine Learning, though more expensive, justifies its cost with extensive capabilities and tailored services, providing significant ROI for large organizations needing advanced features.

To learn more, read our detailed Amazon SageMaker vs. IBM Watson Machine Learning Report (Updated: April 2025).

Buyer's Guide

Amazon SageMaker vs. IBM Watson Machine Learning

April 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

Amazon SageMaker

Ranking in AI Development Platforms

5th

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Data Science Platforms (3rd)

IBM Watson Machine Learning

Ranking in AI Development Platforms

14th

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 5.5%, down from 8.5% compared to the previous year. The mindshare of IBM Watson Machine Learning is 1.8%, down from 2.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Saurabh Jaiswal

Python AWS & AI Expert at a tech consulting company

Create innovative assistants with seamless data integration for large-scale projects

The various integration options available in Amazon SageMaker ( /products/amazon-sagemaker-reviews ), such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue ( /products/aws-glue-reviews ) integrate well for data transformations. The Databricks ( /products/databricks-reviews ) integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow ( /products/tensorflow-reviews ), PyTorch ( /products/pytorch-reviews ), and MXNet ( /products/mxnet-reviews ), and comes with pre-trained algorithms for forecasting, anomaly detection, and more.

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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

"The deployment is very good, where you only need to press a few buttons."

"The most valuable feature of Amazon SageMaker is SageMaker Studio."

"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."

"The support is very good with well-trained engineers whose training curriculum is rigorous."

"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."

"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."

"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 these models, making accessing them convenient as needed."

"The tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc."

More Amazon SageMaker pros

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

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

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

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

"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."

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

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

Cons

"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."

"The main challenge with Amazon SageMaker is the integrations."

"There are other better solutions for large data, such as Databricks."

"The user interface (UI) and user experience (UX) of SageMaker and AWS, in general, need improvement as they are not intuitive and require substantial time to learn how to use specific services."

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

"I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox."

"Lacking in some machine learning pipelines."

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

More Amazon SageMaker cons

"Sometimes training the model is difficult."

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

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

"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."

"The supporting language is limited."

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

"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."

Pricing and Cost Advice

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

"Databricks solution is less costly than Amazon SageMaker."

"Amazon SageMaker is a very expensive product."

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

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

"I would rate the solution's price a ten out of ten since it is very high."

"You don't pay for Sagemaker. You only pay for the compute instances in your storage."

"The solution is relatively cheaper."

More Amazon SageMaker pricing and cost advice

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

"The pricing model is good."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

11%

Computer Software Company

11%

Manufacturing Company

Computer Software Company

16%

Financial Services Firm

11%

University

11%

Educational Organization

10%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

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 Machine Learning?

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.

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

Comparisons

Databricks vs Amazon SageMaker

Compared 15% of the time

Azure OpenAI vs Amazon SageMaker

Compared 12% of the time

Dataiku vs Amazon SageMaker

Compared 11% of the time

Google Vertex AI vs Amazon SageMaker

Compared 8% of the time

Domino Data Science Platform vs Amazon SageMaker

Compared 7% of the time

More Amazon SageMaker Competitors

Google Cloud AI Platform vs IBM Watson Machine Learning

Compared 66% of the time

Azure OpenAI vs IBM Watson Machine Learning

Compared 12% of the time

TensorFlow vs IBM Watson Machine Learning

Compared 10% of the time

Google Vertex AI vs IBM Watson Machine Learning

Compared 8% of the time

IBM Watson Studio vs IBM Watson Machine Learning

Compared 5% of the time

More IBM Watson Machine Learning Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

May 2025

Download Amazon SageMaker product report

Buyer's Guide

AI Development Platforms

May 2025

Download IBM Watson Machine Learning product report

Also Known As

AWS SageMaker, SageMaker

No data available

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 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

Sample Customers

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

Information Not Available

Buyer's Guide

Amazon SageMaker vs. IBM Watson Machine Learning

April 2025

Free Report: Amazon SageMaker vs. IBM Watson Machine Learning

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