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Amazon SageMaker vs Domino Data Science Platform comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 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 Data Science Platforms
3rd
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
37
Ranking in other categories
AI Development Platforms (5th)
Domino Data Science Platform
Ranking in Data Science Platforms
15th
Average Rating
7.6
Reviews Sentiment
6.7
Number of Reviews
2
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 7.3%, down from 9.8% compared to the previous year. The mindshare of Domino Data Science Platform is 2.5%, down from 2.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

Hemant Paralkar - PeerSpot reviewer
Improves team collaboration with advanced feature sharing but needs a better user experience
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. Additionally, dealing with frequent UI updates can be challenging, especially for infrastructure architects like myself. It involves effort to migrate to new UIs, making the updates not seamless. User auditing requires enhancements as tracking operations performed by users can be difficult due to dynamic IP validation and role propagation.
AS
Accelerated machine learning model development with seamless deployment
We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-end development processes. Domino is based on Git, enabling collaboration similar to using Git. Each user operates on their own equivalent of a branch or fork, and once finished, they…

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."
"SageMaker is a comprehensive platform where I can perform all machine learning activities."
"It's user-friendly for business teams as they can understand many aspects through the AWS interface."
"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."
"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."
"The technical support from AWS is excellent."
"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 tool makes our ML model development a bit more efficient because everything is in one environment."
"The scalability of the solution is good; I'd rate it four out of five."
"The workspaces, which are like wrappers of Docker containers, made it easy to start development environments using Domino."
 

Cons

"Amazon SageMaker can make it simpler to manage the data flow from start to finish, such as by integrating data, usingthe machine, and deploying models. This process could be more user-friendly compared to other tools. I would also like to improve integration with Bedrock and the LLM connection for AWS."
"There is room for improvement in the collaboration with serverless architecture, particularly integration with AWS Lambda."
"Improvements are needed in terms of complexity, data security, and access policy integration in Amazon SageMaker."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"The entry point can be a bit difficult. Having all documentation easily accessible on the front page of SageMaker would be a great improvement."
"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."
"AI is a new area and AWS needs to have an internship training program available."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"The predictive analysis feature needs improvement."
"The deployment of large language models (LLMs) could be improved."
 

Pricing and Cost Advice

"Amazon SageMaker is a very expensive product."
"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."
"On average, customers pay about $300,000 USD per month."
"Databricks solution is less costly than Amazon SageMaker."
"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
"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."
"There is no license required for the solution since you can use it on demand."
"I would rate the solution's price a ten out of ten since it is very high."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
19%
Educational Organization
11%
Computer Software Company
11%
Manufacturing Company
9%
Financial Services Firm
36%
Manufacturing Company
11%
Insurance Company
9%
Computer Software Company
6%
 

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...
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?
Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.
What needs improvement with Domino Data Science Platform?
The deployment of large language models (LLMs) could be improved. Currently, Domino provides a simple server that cannot handle big deployments, which is not suitable for LLMs.
What is your primary use case for Domino Data Science Platform?
We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-end development processes. Domino is based on Git, enabling collaboration similar...
What advice do you have for others considering Domino Data Science Platform?
It's important to have a DevOps team well-versed with cloud-native solutions to manage Domino effectively. Relying solely on data scientists might not be sufficient. I'd rate the solution eight out...
 

Also Known As

AWS SageMaker, SageMaker
Domino Data Lab Platform
 

Interactive Demo

 

Overview

 

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
Allstate, GSK, AstraZeneca, Federal Reserve, US Navy, Bristol Myers Squibb, Bayer, BNP Paribas, Moodys, New York Life
Find out what your peers are saying about Amazon SageMaker vs. Domino Data Science Platform and other solutions. Updated: April 2025.
849,686 professionals have used our research since 2012.