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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
7.0
Number of Reviews
38
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 August 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 6.1%, down from 8.8% compared to the previous year. The mindshare of Domino Data Science Platform is 2.7%, down from 2.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science 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.
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

"The most valuable features are the ability to store artifacts and gather reports and measures from experiments."
"We were able to use the product to automate processes."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"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."
"I have seen a return on investment, probably a factor of four or five."
"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 technical support from AWS is excellent."
"The workspaces, which are like wrappers of Docker containers, made it easy to start development environments using Domino."
"The scalability of the solution is good; I'd rate it four out of five."
 

Cons

"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."
"One area for improvement is the pricing, which can be quite high."
"I had to create custom templates for labeling multi-data sets, such as text and images, which was time-consuming."
"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."
"The main challenge with Amazon SageMaker is the integrations."
"For any cloud provider, the cost has to be substantially reduced, especially in the case of Amazon SageMaker, which is extremely expensive for huge workloads."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"The main challenge with Amazon SageMaker is the integrations."
"The deployment of large language models (LLMs) could be improved."
"The predictive analysis feature needs improvement."
 

Pricing and Cost Advice

"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."
"Amazon SageMaker is a very expensive product."
"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."
"SageMaker is worth the money for our use case."
"There is no license required for the solution since you can use it on demand."
"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."
"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
19%
Computer Software Company
11%
Manufacturing Company
9%
University
5%
Financial Services Firm
39%
Manufacturing Company
10%
Insurance Company
8%
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?
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 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: July 2025.
865,164 professionals have used our research since 2012.