Try our new research platform with insights from 80,000+ expert users

Amazon SageMaker vs IBM Watson Machine Learning comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 4, 2024

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
4th
Average Rating
7.8
Reviews Sentiment
7.0
Number of Reviews
38
Ranking in other categories
Data Science Platforms (2nd)
IBM Watson Machine Learning
Ranking in AI Development Platforms
16th
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 January 2026, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 4.0%, down from 6.3% compared to the previous year. The mindshare of IBM Watson Machine Learning is 2.0%, up from 2.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Market Share Distribution
ProductMarket Share (%)
Amazon SageMaker4.0%
IBM Watson Machine Learning2.0%
Other94.0%
AI Development Platforms
 

Featured Reviews

Saurabh Jaiswal - PeerSpot reviewer
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, such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue integrate well for data transformations. The Databricks integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow, PyTorch, and MXNet, and comes with pre-trained algorithms for forecasting, anomaly detection, and more.
Anurag Mayank - PeerSpot reviewer
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.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The most valuable feature of Amazon SageMaker is SageMaker Studio."
"Amazon SageMaker is highly valuable for managing ML workloads. It connects to AWS cloud resources, making it easy to deploy algorithms and collaborate using tools like GitLab. It offers a wide range of Python libraries and other necessary tools for modelling and algorithms."
"SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project."
"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."
"I have seen a return on investment, probably a factor of four or five."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"We've had no problems with SageMaker's stability."
"SageMaker is a comprehensive platform where I can perform all machine learning activities."
"The most valuable aspect of the solution's the cost and human labor savings."
"Scalability-wise, I rate the solution ten out of ten."
"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 has improved self-service and customer satisfaction."
"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."
"The solution is very valuable to our organization due to the fact that we can work on it as a workflow."
 

Cons

"I would recommend having more walkthrough videos and articles beyond AWS Skill Builder."
"Amazon might need to emphasize its capabilities in generative models more effectively."
"The solution is complex to use."
"Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker."
"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."
"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."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"The documentation must be made clearer and more user-friendly."
"Sometimes training the model is difficult."
"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."
"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."
"They should add more GPU processing power to improve performance, especially when dealing with large amounts of data."
"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."
"In future releases, I would like to see a more flexible environment."
"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."
"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."
"There is no license required for the solution since you can use it on demand."
"The support costs are 10% of the Amazon fees and it comes by default."
"The pricing could be better, especially for querying. The per-query model feels expensive."
"SageMaker is worth the money for our use case."
"The pricing is comparable."
"Databricks solution is less costly than Amazon SageMaker."
"I've only been using the free tier, but it's quite competitive on a service basis."
"The pricing model is good."
report
Use our free recommendation engine to learn which AI Development Platforms solutions are best for your needs.
881,082 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
18%
Computer Software Company
10%
Manufacturing Company
9%
University
6%
University
14%
Financial Services Firm
12%
Computer Software Company
10%
Educational Organization
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business12
Midsize Enterprise11
Large Enterprise17
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...
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...
What is your experience regarding pricing and costs for Amazon SageMaker?
If you manage it effectively, their pricing is reasonable. It's similar to anything in the cloud; if you don't manage it properly, it can be expensive, but if you do, it's fine.
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...
What is your primary use case for IBM Watson Machine Learning?
We use different artificial intelligence models to build questions and get answers for clients.
 

Also Known As

AWS SageMaker, SageMaker
No data available
 

Overview

 

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
Information Not Available
Find out what your peers are saying about Amazon SageMaker vs. IBM Watson Machine Learning and other solutions. Updated: December 2025.
881,082 professionals have used our research since 2012.