Pricing score

8.6

Amazon SageMaker Pricing Profile

Amazon SageMaker is a fully-managed service 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.

Key Takeaways

End-to-End Machine Learning Workflow

SageMaker provides a comprehensive suite of tools for the entire machine learning lifecycle, including data labeling, model building, training, evaluation, and deployment.

Human-in-the-loop

Harness the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of FMs with human-in-the-loop capabilities.

Built-in Algorithms and Pre-built Models

SageMaker comes with a wide selection of built-in machine learning algorithms and pre-trained models. This enables users to quickly apply algorithms such as XGBoost, K-means clustering, DeepAR (for forecasting), and more.

Product Overview

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Amazon SageMaker is a fully managed service from Amazon Web Services (AWS) that enables developers and data scientists to build, train, and deploy machine learning models quickly and easily.

SageMaker simplifies the end-to-end machine learning (ML) lifecycle, from data preprocessing to model deployment, and provides a variety of tools and frameworks that help automate and optimize the machine learning workflow.

INSIGHTS

Our insights about Amazon SageMaker pricing

01

Free trial

02

Custom options

03

Savings plans

Available Pricing Models

How much does Amazon SageMaker cost?

There is no additional charge for using SageMaker Studio. You pay only for the underlying compute and storage charges on the services that you use within SageMaker Studio.

What users say about Amazon SageMaker pricing

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Muhamamd U.

I can hardly make an estimate of the price calculation. Even though there is some tool called AWS pricing calculator, the list of available configurations doesn't show the number of configurations you can select while setting up the tool Studio and Notebook instances.

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Gourav J.

I am exclusively using Amazon SageMaker for both professional and personal usage. The variety of application make Handy while work upon machine learning task. The training and canvas features i've been using for quite some time there application make my ML task faster and productive.

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Verified User in Financial Services

SageMaker makes it very easy to train and deploy models. The managed infrastructure allows us to focus on business logic without needing to deal with things like cluster management, autoscaling, etc.

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Krishna K.

Though we are getting compute for a reasonable costs, the onus of responsibility to run the large model lies witth the users. When they run larger models just to test it is attracting some additional costs. Though Sagemaker is easy to use, the cost management responsibility lies with the users.

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Shyam P.

I like the endpoint creation which can inference our model through lambda function. Along with Sagemaker I used API gateway as well as to use the model in local environment.

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Muhamamd U.

I can hardly make an estimate of the price calculation. Even though there is some tool called AWS pricing calculator, the list of available configurations doesn't show the number of configurations you can select while setting up the tool Studio and Notebook instances.

avatar

Krishna K.

Though we are getting compute for a reasonable costs, the onus of responsibility to run the large model lies witth the users. When they run larger models just to test it is attracting some additional costs. Though Sagemaker is easy to use, the cost management responsibility lies with the users.

avatar

Gourav J.

I am exclusively using Amazon SageMaker for both professional and personal usage. The variety of application make Handy while work upon machine learning task. The training and canvas features i've been using for quite some time there application make my ML task faster and productive.

avatar

Shyam P.

I like the endpoint creation which can inference our model through lambda function. Along with Sagemaker I used API gateway as well as to use the model in local environment.

avatar

Verified User in Financial Services

SageMaker makes it very easy to train and deploy models. The managed infrastructure allows us to focus on business logic without needing to deal with things like cluster management, autoscaling, etc.

Amazon SageMaker Pricing Rating

Scalability: 4.8/5

Amazon SageMaker allows you to scale your ML workloads based on demand, enabling you to run training jobs or deploy models at a fraction of the cost compared to traditional on-premise infrastructure. SageMaker's pay-as-you-go pricing ensures cost efficiency.

Built-in Model Management: 4.7/5

SageMaker provides a centralized repository for managing models and experiments, making it easier to track versions and deploy the best model to production.

Collaboration: 4.9/5

SageMaker integrates with other AWS services such as Amazon S3 for storage, AWS Lambda for serverless functions, and AWS Glue for ETL. It also supports easy sharing and collaboration with other team members through the SageMaker Studio environment.

FAQ on Amazon SageMaker Pricing

What is Amazon SageMaker?

SageMaker is a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.

What is the service availability of SageMaker?

SageMaker is designed for high availability. There are no maintenance windows or scheduled downtimes. SageMaker APIs run in Amazon proven high-availability data centers, with service stack replication configured across three facilities in each Region to provide fault tolerance in the event of a server failure or Availability Zone outage.

What if I have my own notebook, training, or hosting environment?

SageMaker provides a full and complete workflow, but you can continue using your existing tools with SageMaker. You can easily transfer the results of each stage in and out of SageMaker as your business requirements dictate.

How does SageMaker Clarify improve model explainability?

SageMaker Clarify is integrated with SageMaker Experiments to provide a feature importance graph detailing the importance of each input for your model’s overall decision-making process after the model has been trained. These details can help determine if a particular model input has more influence than it should on overall model behavior. SageMaker Clarify also makes explanations for individual predictions available through an API.