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Amazon SageMaker vs Domino Data Science Platform comparison

Amazon Web Services (AWS) and Domino Data Lab are both solutions in the Data Science Platforms category. Amazon Web Services (AWS) is ranked #3 with an average rating of 8.0, while Domino Data Lab is ranked #14 with an average rating of 8.0. Amazon Web Services (AWS) holds a 6.2% mindshare in Data Science Platforms, compared to Domino Data Lab’s 2.7% mindshare. Additionally, 91% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Domino Data Lab users who would recommend it.
Amazon SageMaker
Read 37 Amazon SageMaker reviews
  • 7,896 Views
  • 4,738 Comparison Views
91% willing to recommend
Domino Data Science Platform
Read 2 Domino Data Science Platform reviews
  • 1,578 Views
  • 1,546 Comparison Views
100% willing to recommend
 

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

Domino Data Science Platform and Amazon SageMaker are competing solutions in data science and machine learning platforms. Amazon SageMaker is viewed as the more feature-rich product because of its comprehensive capabilities and integration with AWS.

Features: Domino focuses on user-centric features including collaboration capabilities, version control, and team-focused tools. Amazon SageMaker offers automated machine learning processes, extensive integration with other AWS services, and enhanced machine learning lifecycle management.

Ease of Deployment and Customer Service: Amazon SageMaker benefits from a scalable and integrative deployment model, leveraging AWS's global infrastructure and an extensive support ecosystem. Domino emphasizes easy-to-use deployment mechanisms and collaborative workspaces, but its support system is less extensive.

Pricing and ROI: Domino typically requires a higher initial setup cost but offers ROI for organizations focusing on team collaboration. Amazon SageMaker provides flexible pricing aligned with cloud usage, favoring organizations deeply integrated with AWS and offering a scalable pay-as-you-go model.

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To learn more, read our detailed Amazon SageMaker vs. Domino Data Science Platform Report (Updated: June 2025).
Buyer's Guide
Amazon SageMaker vs. Domino Data Science Platform
June 2025
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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
14th
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 July 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 6.2%, down from 9.3% compared to the previous year. The mindshare of Domino Data Science Platform is 2.7%, down from 2.8% 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 ( /products/amazon-sagemaker-reviews ), such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue ( /products/aws-glue-reviews ) integrate well for data transformations. The Databricks ( /products/databricks-reviews ) integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow ( /products/tensorflow-reviews ), PyTorch ( /products/pytorch-reviews ), and MXNet ( /products/mxnet-reviews ), and comes with pre-trained algorithms for forecasting, anomaly detection, and more.
Read full review
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…
Read full review

Quotes from Members

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

Pros

"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."
"We've had no problems with SageMaker's stability."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project."
"The intuitive interface and streamlined user experience make it easy to navigate and set up various tools like Visual Studio Code or Jupyter Notebook."
"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 product aggregates everything we need to build and deploy machine learning models in one place."
More Amazon SageMaker pros
"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

"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."
"The platform could be more accessible to users with basic coding skills, making it more intuitive and easier for beginners to use comfortably."
"Lacking in some machine learning pipelines."
"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 dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options."
"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."
"Amazon might need to emphasize its capabilities in generative models more effectively."
More Amazon SageMaker cons
"The predictive analysis feature needs improvement."
"The deployment of large language models (LLMs) could be improved."
 

Pricing and Cost Advice

"The pricing could be better, especially for querying. The per-query model feels expensive."
"The tool's pricing is reasonable."
"On average, customers pay about $300,000 USD per month."
"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."
"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."
"Databricks solution is less costly than Amazon SageMaker."
"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
More Amazon SageMaker pricing and cost advice
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
19%
Computer Software Company
12%
Manufacturing Company
8%
Educational Organization
6%
Financial Services Firm
38%
Manufacturing Company
10%
Insurance Company
7%
Computer Software Company
7%
 

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...
See all answers
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...
See all answers
What is your experience regarding pricing and costs for Amazon SageMaker?
The pricing is high, around an eight. However, SageMaker offers free trials for the first two months, allowing users to determine which features they need. It is considered value for money given it...
See all answers
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.
See all answers
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...
See all answers
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...
See all answers
 

Comparisons

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Compared 9% of the time
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Compared 4% of the time
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Compared 29% of the time
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Compared 24% of the time
Alteryx vs Domino Data Science Platform Logo
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Compared 7% of the time
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Compared 5% of the time
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Product Reports

Buyer's Guide
Amazon SageMaker
July 2025
Amazon SageMaker reportDownload Amazon SageMaker product report
Buyer's Guide
Data Science Platforms
July 2025
Domino Data Science Platform reportDownload Domino Data Science Platform product report
 

Also Known As

AWS SageMaker, SageMaker
Domino Data Lab Platform
 

Interactive Demo

Demo not available
Amazon Web Services (AWS)
Domino Data Lab
 

Overview

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Amazon Web Services (AWS)

Domino provides a central system of record that keeps track of all data science activity across an organization. Domino helps data scientists seamlessly orchestrate AWS hardware and software toolkits, increase flexibility and innovation, and maintain required IT controls and standards. Organizations can automatically keep track of all data, tools, experiments, results, discussion, and models, as well as dramatically scale data science investments and impact decision-making across divisions. The platform helps organizations work faster, deploy results sooner, scale rapidly, and reduce regulatory and operational risk.

Domino Data Lab
 

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
Buyer's Guide
Amazon SageMaker vs. Domino Data Science Platform
June 2025
Free Report: Amazon SageMaker vs. Domino Data Science Platform
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