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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 5.3% 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
  • 3,316 Views
  • 2,266 Comparison Views
91% willing to recommend
IBM Watson Machine Learning
Read 7 IBM Watson Machine Learning reviews
  • 1,113 Views
  • 823 Comparison Views
100% willing to recommend
 

Comparison Buyer's Guide

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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.

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To learn more, read our detailed Amazon SageMaker vs. IBM Watson Machine Learning Report (Updated: June 2025).
Buyer's Guide
Amazon SageMaker vs. IBM Watson Machine Learning
June 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
37
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
7
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of July 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 5.3%, down from 7.6% compared to the previous year. The mindshare of IBM Watson Machine Learning is 1.8%, down from 2.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Saurabh Jaiswal - PeerSpot reviewer
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.
Read full review
Anurag Mayank - PeerSpot reviewer
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

"SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."
"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."
"We've had no problems with SageMaker's stability."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"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."
"The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models."
"The feature I found most valuable is the data catalog, as it assists with the lineage of data through the preparation pipeline."
More Amazon SageMaker pros
"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."
"The solution is very valuable to our organization due to the fact that we can work on it as a workflow."
"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."
"We can enable and change developer productivity with artificial intelligence-recommended code based on natural language input or exciting source code."
 

Cons

"Amazon might need to emphasize its capabilities in generative models more effectively."
"The main challenge with Amazon SageMaker is the integrations."
"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."
"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."
"The solution requires a lot of data to train the model."
"There are other better solutions for large data, such as Databricks."
"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."
"SageMaker would be improved with the addition of reporting services."
More Amazon SageMaker cons
"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."
"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."
"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."
"Sometimes training the model is difficult."
"In future releases, I would like to see a more flexible environment."
"They should add more GPU processing power to improve performance, especially when dealing with large amounts of data."
"The supporting language is limited."
 

Pricing and Cost Advice

"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."
"Databricks solution is less costly than Amazon SageMaker."
"Amazon SageMaker is a very expensive product."
"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 pricing is comparable."
"The tool's pricing is reasonable."
"The product is expensive."
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
19%
Computer Software Company
12%
Manufacturing Company
8%
Educational Organization
7%
Computer Software Company
15%
Financial Services Firm
11%
University
10%
Educational Organization
9%
 

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?
The pricing is high, around an eight. However, SageMaker offers free trials for the first two months, allowing users to determine which features they need. It is considered value for money given it...
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

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Compared 11% of the time
Google Vertex AI vs Amazon SageMaker Logo
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Compared 9% of the time
DataRobot vs Amazon SageMaker Logo
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Compared 8% of the time
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Google Cloud AI Platform vs IBM Watson Machine Learning Logo
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Compared 56% of the time
Azure OpenAI vs IBM Watson Machine Learning Logo
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Compared 11% of the time
TensorFlow vs IBM Watson Machine Learning Logo
TensorFlow vs IBM Watson Machine Learning
Compared 8% of the time
Google Vertex AI vs IBM Watson Machine Learning Logo
Google Vertex AI vs IBM Watson Machine Learning
Compared 8% of the time
IBM Watson Studio vs IBM Watson Machine Learning Logo
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Compared 5% of the time
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Product Reports

Buyer's Guide
Amazon SageMaker
June 2025
Amazon SageMaker reportDownload Amazon SageMaker product report
Buyer's Guide
AI Development Platforms
June 2025
IBM Watson Machine Learning reportDownload 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
June 2025
Free Report: Amazon SageMaker vs. IBM Watson Machine Learning
Find out what your peers are saying about Amazon SageMaker vs. IBM Watson Machine Learning and other solutions. Updated: June 2025.
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