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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
15th
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 October 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 4.9%, down from 7.0% compared to the previous year. The mindshare of IBM Watson Machine Learning is 2.1%, down from 2.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Market Share Distribution
ProductMarket Share (%)
Amazon SageMaker4.9%
IBM Watson Machine Learning2.1%
Other93.0%
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, 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
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 evolution from SageMaker Classic to SageMaker Studio, particularly the UI part of Studio, is commendable."
"They are doing a good job of evolving."
"The most valuable features are the ability to store artifacts and gather reports and measures from experiments."
"SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"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 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 technical support of the tool was good."
"It has improved self-service and customer satisfaction."
"It is has a lot of good features and we find the image classification very useful."
"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."
"We can enable and change developer productivity with artificial intelligence-recommended code based on natural language input or exciting source code."
"The most valuable aspect of the solution's the cost and human labor savings."
"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."
 

Cons

"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"The entry point can be a bit difficult. 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."
"There is room for improvement in the collaboration with serverless architecture, particularly integration with AWS Lambda."
"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."
"SageMaker would be improved with the addition of reporting services."
"I had to create custom templates for labeling multi-data sets, such as text and images, which was time-consuming."
"The model repository is a concern as models are stored on a bucket and there's an issue with versioning."
"The supporting language is limited."
"In future releases, I would like to see a more flexible environment."
"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."
"Sometimes training the model is difficult."
"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."
"They should add more GPU processing power to improve performance, especially when dealing with large amounts of data."
"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

"The tool's pricing is reasonable."
"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."
"The support costs are 10% of the Amazon fees and it comes by default."
"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
"The product is expensive."
"On average, customers pay about $300,000 USD per month."
"There is no license required for the solution since you can use it on demand."
"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%
Computer Software Company
11%
Manufacturing Company
9%
University
5%
University
12%
Computer Software Company
12%
Educational Organization
10%
Financial Services Firm
10%
 

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: September 2025.
869,566 professionals have used our research since 2012.