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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.
reviewer2319402 - PeerSpot reviewer
Director of Business Development at a educational organization with 1,001-5,000 employees
Good fit for medium-sized companies, and offers good AutoML feature
In future releases, I would like to see a more flexible environment. It's a good product for customization and developing products. But when we need the most control over the delivery, Watson isn't the best. We can't fix everything because we're working with a machine that's creating a product. And the ability to go in-depth and tweak our model easily would be really nice.

Quotes from Members

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

Pros

"The tangible benefits we have observed from using Amazon SageMaker include improved time to insight and generally the common stack that is easier to support over time."
"The most valuable features in Amazon SageMaker are its AutoML, feature store, and automated hyperparameter tuning capabilities."
"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."
"The various integration options available in Amazon SageMaker, such as Firehose for connecting to data pipelines, are simple to use."
"The support is very good with well-trained engineers whose training curriculum is rigorous."
"The deployment is very good, where you only need to press a few buttons."
"Allows you to create API endpoints."
"I recommend SageMaker for ML projects if you need to build models from scratch."
"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."
"Scalability-wise, I rate the solution ten out of ten."
"The most valuable aspect of the solution's the cost and human labor savings."
"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."
"It is has a lot of good features and we find the image classification very useful."
"It has improved self-service and customer satisfaction."
 

Cons

"When starting a new session, the waiting time can be quite long, ranging from two to five minutes."
"Having all documentation easily accessible on the front page of SageMaker would be a great improvement."
"The platform could be more accessible to users with basic coding skills, making it more intuitive and easier for beginners to use comfortably."
"The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options."
"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."
"They could add features such as managing environments, experiment management across those environments, and the integration with training datasets as you go through those experiments."
"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."
"There is room for improvement in the collaboration with serverless architecture, particularly integration with AWS Lambda."
"Sometimes training the model is difficult."
"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."
"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."
"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."
"The supporting language is limited."
 

Pricing and Cost Advice

"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."
"Amazon SageMaker is a very expensive product."
"The product is expensive."
"The support costs are 10% of the Amazon fees and it comes by default."
"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."
"The pricing is comparable."
"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."
"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
"The pricing model is good."
"I've only been using the free tier, but it's quite competitive on a service basis."
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Computer Software Company
11%
Manufacturing Company
9%
University
6%
University
13%
Computer Software Company
12%
Financial Services Firm
12%
Educational Organization
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business12
Midsize Enterprise11
Large Enterprise16
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 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.
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...
 

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.
879,853 professionals have used our research since 2012.