Amazon SageMaker Reviews

Name: Amazon SageMaker
Brand: Amazon Web Services (AWS)
Rating: 3.9 (38 reviews)

3.9 out of 5

38 reviews
91% willing to recommend

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 #3 ranked solution in top Data Science Platforms and #5 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 68% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 19% of all views.

Helped 865,164 peers since 2012

Featured Amazon SageMaker reviews

Saurabh Jaiswal

Python AWS & AI Expert at a tech consulting company

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.

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Hemant Paralkar

Lead Consultant at Saama

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.

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SaravananThiagarajan

Data Scientist at a computer software company with 5,001-10,000 employees

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. In EC2, you have spot instances that cut costs tremendously, but you don't have that in Amazon SageMaker. You pay for the local usage. I would like to see better integration with GPUs. GPUs are very expensive for AWS or any cloud provider. NVIDIA has introduced options with Databricks for GPUs, so it would be interesting to see how Amazon SageMaker can parallelize GPU usage. I haven't used it to scale multiple GPUs automatically for model training. The key points are the cost and how effectively they integrate GPUs into the workload for training machine learning models. We want to see how seamless it is and how it can work. I haven't used multiple GPUs scaled automatically. For model training, the first concern is cost, and the second is how effectively they want to integrate GPUs into the workload for training machine learning models.

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Amazon SageMaker mindshare

Product category:

As of August 2025, the mindshare of Amazon SageMaker in the Data Science Platforms category stands at 6.1%, down from 8.8% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Science Platforms

PeerResearch reports based on Amazon SageMaker reviews

Type	Title	Date
Category	Data Science Platforms	Aug 18, 2025	Download
Product	Reviews, tips, and advice from real users	Aug 18, 2025	Download
Comparison	Amazon SageMaker vs Databricks	Aug 18, 2025	Download
Comparison	Amazon SageMaker vs KNIME Business Hub	Aug 18, 2025	Download
Comparison	Amazon SageMaker vs Microsoft Azure Machine Learning Studio	Aug 18, 2025	Download

Title	Rating	Mindshare	Recommending
Databricks	4.1	15.3%	96%	91 interviews Add to research
KNIME Business Hub	4.1	11.9%	94%	60 interviews Add to research

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 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."
"I have seen a return on investment, probably a factor of four or five."

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.