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Amazon SageMaker Reviews

Vendor: Amazon Web Services (AWS)
3.9 out of 5
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What is Amazon SageMaker?

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.

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Amazon SageMaker is the #2 ranked solution in top Data Science Platforms and #4 ranked solution in top AI Development Platforms. PeerSpot users give Amazon SageMaker an average rating of 7.8 out of 10. Amazon SageMaker is most commonly compared to Databricks: Amazon SageMaker vs Databricks. Amazon SageMaker is popular among the large enterprise segment, accounting for 65% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 17% of all views.
Buyer's Guide
Amazon SageMaker
March 2026
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Amazon SageMaker mindshare

Product category:
Data Science Platforms
As of March 2026, the mindshare of Amazon SageMaker in the Data Science Platforms category stands at 4.0%, down from 7.5% compared to the previous year, according to calculations based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
Amazon SageMaker4.0%
Databricks9.3%
KNIME Business Hub6.8%
Other79.9%
Data Science Platforms

PeerResearch reports based on Amazon SageMaker reviews

TypeTitleDate
CategoryData Science PlatformsMar 15, 2026Download
ProductReviews, tips, and advice from real usersMar 15, 2026Download
ComparisonAmazon SageMaker vs DatabricksMar 15, 2026Download
ComparisonAmazon SageMaker vs KNIME Business HubMar 15, 2026Download
ComparisonAmazon SageMaker vs DataikuMar 15, 2026Download
Suggested products
TitleRatingMindshareRecommending
Databricks4.19.3%96%93 interviewsAdd to research
KNIME Business Hub4.16.8%94%60 interviewsAdd to research
 
 
Key learnings from peers

Valuable Features

Valuable features of Amazon SageMaker include Random Cut Forest, pre-built solutions, increased RAM and GPU support, seamless deployment, integration with AWS services, and API endpoint creation. Users appreciate its model deployment, serverless capabilities, and hyperparameter tuning. Autopilot, rich libraries, and built-in algorithms enhance functionality. SageMaker's user-friendly interface, scalability, and AI model lifecycle management make it appealing, alongside text extraction accuracy. Users value its flexibility, efficient infrastructure, integration options, and automation for data preparation and model deployment.
  • "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 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."

Room for Improvement

Amazon SageMaker faces complexity and usability challenges. Users find the IDE and UI difficult to navigate, with high pricing as a major concern. Performance, scalability, and integration, including with GPUs and other cloud services, require enhancement. Documentation and training resources need improvement, with users seeking more comprehensive and accessible guides. Security and data management features lack intuitiveness. Enhanced no-code capabilities and additional AI functionalities are desired for a more user-friendly experience.
  • "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."
  • "There is room for improvement in the collaboration with serverless architecture, particularly integration with AWS Lambda."
  • "One area for improvement is the pricing, which can be quite high."

ROI

Organizations experience diverse returns on investment from Amazon SageMaker. Some achieve substantial cost reductions and revenue growth, notably in fraud detection and targeted advertising, while others benefit from SageMaker's time-saving capabilities and pre-configured components. Firms on AWS find it particularly valuable. Despite high long-term costs potentially hindering widespread use, some projects report significant savings, including a $3 million application development success. ROI often varies significantly, with some companies seeing returns multiplied by four or five times.

Pricing

Amazon SageMaker pricing is complex, primarily based on machine type, storage, and computation needs. Users pay for compute resources with variable costs according to usage. Pricing is deemed high by some, with costs ranging between $7,000 to $15,000 per month depending on deployment factors. Although there are additional AWS support costs, SageMaker itself is considered expensive despite offering some cost-effective features and free trials for initial usage.
  • "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."
  • "In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."

Popular Use Cases

Organizations primarily utilize Amazon SageMaker for developing, training, and deploying machine learning models. Users implement it for tasks like predictive modeling, outlier detection, and computer vision. They leverage its capabilities for AI projects such as recommendation systems, financial analysis, and real-time customer interactions. Large datasets benefit from SageMaker's flexible compute resources. It is commonly used for managing infrastructure costs, simplifying MLOps processes, and handling tasks involving data preparation, model monitoring, and optimization.

Service and Support

Amazon SageMaker customer service receives mixed feedback. Many users report satisfaction due to good documentation and support, with ratings often between eight and ten. Premium AWS partners benefit from enhanced assistance. Some praise the responsiveness, while others note delays and additional fees for improved service. Users occasionally find support for new users lacking, rating it lower. Generally, Amazon SageMaker's support is well-regarded, but experiences vary based on customer status and expectations.

Deployment

The initial setup of Amazon SageMaker is generally straightforward, with many finding it simple and quick, typically taking between 15 minutes to an hour. It benefits from being cloud-native, although some experience challenges with specific requirements or lack of AWS knowledge. Ratings for ease range from six to nine out of ten. Integrating with other AWS services and customizing security configurations can be complex, requiring technical expertise and familiarity with the platform.

Scalability

Amazon SageMaker is generally viewed as highly scalable, allowing dynamic resource allocation and supporting large datasets. Many users praise its adaptability to varying project requirements and different business sizes, but note that scaling requires knowledge and planning, particularly with AWS quotas and GPU availability. It effectively handles medium to large-scale deployments, although small-scale users may encounter challenges. Organizations appreciate the flexibility of scaling compute resources according to demands.

Stability

Amazon SageMaker is highly stable, rated between seven to nine out of ten by users. Its serverless infrastructure ensures reliability, with minimal downtime reported. Most instabilities are due to configuration errors. Users seldom face performance issues, though occasional UI changes may pose minor challenges. Stability is consistent over extended use, and any issues are usually easy to address. Positive user feedback emphasizes SageMaker's stability and scalability across various implementations.
These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.
Download our Amazon SageMaker Buyer's Guide for additional reliable information.

Review data by company size

By reviewers
Company SizeCount
Small Business11
Midsize Enterprise11
Large Enterprise13
By reviewers
By visitors reading reviews
Company SizeCount
Small Business213
Midsize Enterprise115
Large Enterprise608
By visitors reading reviews

Top industries

By visitors reading reviews
Financial Services Firm
17%
Manufacturing Company
9%
Computer Software Company
9%
University
6%
Insurance Company
5%
Comms Service Provider
5%
Educational Organization
5%
Energy/Utilities Company
4%
Retailer
4%
Healthcare Company
4%
Outsourcing Company
4%
Government
3%
Real Estate/Law Firm
3%
Media Company
3%
Construction Company
2%
Transportation Company
2%
Non Profit
2%
Pharma/Biotech Company
2%
Marketing Services Firm
2%
Performing Arts
2%
Recreational Facilities/Services Company
2%
Wholesaler/Distributor
1%
Legal Firm
1%
Hospitality Company
1%
Consumer Goods Company
1%
Logistics Company
1%
Engineering Company
1%
Aerospace/Defense Firm
1%

Compare Amazon SageMaker with alternative products

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Learn more about Amazon SageMaker

Amazon SageMaker was previously known as AWS SageMaker, SageMaker.

Amazon SageMaker customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit

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Product Categories

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Amazon SageMaker Reviews Summary
Author infoRatingReview Summary
Python AWS & AI Expert at a tech consulting company4.0I use Amazon SageMaker to develop an assistant like Siri using BlazingText. It offers valuable integration options and tools, though integration with AWS Lambda could improve. It is fully managed on AWS, simplifying development with pre-trained models and flexible frameworks.
Data Lake and MLOps Lead at a energy/utilities company with 10,001+ employees3.5I've used Amazon SageMaker for years in various data science projects and found it stable and scalable, though scaling operations remains challenging. While effective for ML tasks, broader data infrastructure integration needs improvement. Overall, it's a solid tool.
Lead Consultant at Saama3.5My primary use of Amazon SageMaker involves provisioning for data scientists. I value its Feature Store sharing and Studio UI, though improvements are needed in no-code options and seamless UI updates. Competing solutions include DataIKU and Databricks.
Senior Solutions Architect at a tech vendor with 10,001+ employees4.0No summary available
President & CEO at Y124.0No summary available
Data Scientist at a computer software company with 5,001-10,000 employees4.0I find Amazon SageMaker valuable for its rich ML libraries and seamless AWS integration, particularly its serverless nature and pay-as-you-go model. However, cost and GPU integration still need improvement, especially for large workloads.
Senior Actuary at Accelerant Holdings4.0I use Amazon SageMaker primarily to handle large datasets that exceed my local laptop's capacity, benefiting from its flexible resource selection and intuitive interface. Despite some integration and startup delays, it significantly reduced costs and improved project outcomes.
Executive Specialists at Linedata Services SA4.5We use Amazon SageMaker to quickly develop AI models for extracting financial and contractual data from unstructured documents. It enhances our predictive analytics and automates processes, despite needing a lot of data for training, unlike our previous Azure solution.