Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. SageMaker supports the leading ML frameworks, toolkits, and programming languages.

With SageMaker, you pay only for what you use. You have two choices for payment: an On-Demand Pricing that offers no minimum fees and no upfront commitments, and the SageMaker Savings Plans that offer a flexible, usage-based pricing model in exchange for a commitment to a consistent amount of usage.

Amazon SageMaker Free Tier

Amazon SageMaker is free to try. As part of the AWS Free Tier, you can get started with Amazon SageMaker for free. Your free tier starts from the first month when you create your first SageMaker resource. The details of the free tier for Amazon SageMaker are in the table below.

Amazon SageMaker capability Free Tier usage per month for the first 2 months
Studio notebooks, and notebook instances 250 hours of ml.t3.medium instance on Studio notebooks OR 250 hours of ml.t2 medium instance or ml.t3.medium instance on notebook instances
RStudio on SageMaker 250 hours of ml.t3.medium instance on RSession app AND free ml.t3.medium instance for RStudioServerPro app
Data Wrangler 25 hours of ml.m5.4xlarge instance
Feature Store 10 million write units, 10 million read units, 25 GB storage
Training 50 hours of m4.xlarge or m5.xlarge instances
Real-Time Inference 125 hours of m4.xlarge or m5.xlarge instances
Serverless Inference 150,000 seconds of inference duration
Canvas 750 hours/month for session time, and up to 10 model creation requests/month, each with up to 1 million cells/model creation request

On-Demand Pricing

  • Studio Notebooks
  • Amazon SageMaker Studio Notebooks
    Amazon SageMaker Studio Notebooks are one-click Jupyter notebooks that can be spun up quickly. The underlying compute resources are fully elastic and the notebooks can be easily shared with others, enabling seamless collaboration. You are charged for the instance type you choose, based on the duration of use.

  • RStudio on SageMaker
  • RStudio on SageMaker
    RStudio on SageMaker offers on-demand cloud compute resources to accelerate model development and improve productivity. You are charged for the instance types you choose to run the RStudio Session app and the RStudio Server Pro app.

    RStudioServerPro App

  • Notebook Instances
  • Notebook Instances
    Notebook instances are compute instances running the Jupyter notebook app. You are charged for the instance type you choose, based on the duration of use.

  • Processing
  • Amazon SageMaker Processing
    Amazon SageMaker Processing lets you easily run your pre-processing, post-processing, and model evaluation workloads on fully managed infrastructure. You are charged for the instance type you choose, based on the duration of use.

  • Data Wrangler
  • Amazon SageMaker Data Wrangler
    Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning from weeks to minutes. You pay for the time used to cleanse, explore, and visualize data. SageMaker Data Wrangler is priced per instance type by the second.*

    Amazon SageMaker Data Wrangler Jobs

    An Amazon SageMaker Data Wrangler job is created when a data flow is exported from SageMaker Data Wrangler. With SageMaker Data Wrangler jobs, you can automate your data preparation workflows. SageMaker Data Wrangler jobs help you reapply your data preparation workflows on new datasets to help save you time, and are billed by the second.

  • Feature Store
  • Amazon SageMaker Feature Store
    Amazon SageMaker Feature Store is a central repository to ingest, store and serve features for machine learning. You are charged for writes, reads, and data storage on the SageMaker Feature Store. Writes are charged as write request units per KB, reads are charged as read request units per 4KB, and data storage is charged per GB per month.

  • Training
  • Amazon SageMaker Training
    Amazon SageMaker makes it easy to train machine learning (ML) models by providing everything you need to train, tune, and debug models. You are charged for usage of the instance type you choose. When you use Amazon SageMaker Debugger to debug issues and monitor resources during training, you can use built-in rules to debug your training jobs or write your own custom rules. There is no charge to use built-in rules to debug your training jobs. For custom rules, you are charged for the instance type you choose, based on the duration of use.

  • Real-Time Inference
  • Amazon SageMaker Hosting: Real-Time Inference
    Amazon SageMaker provides real-time inference for your use cases needing real-time predictions. You are charged for usage of the instance type you choose. When you use Amazon SageMaker Model Monitor to maintain highly accurate models providing real-time inference, you can use built-in rules to monitor your models or write your own custom rules. For built-in rules, you get up to 30 hours of monitoring at no charge. Additional charges will be based on duration of usage. You are charged separately when you use your own custom rules.

  • Asynchronous Inference
  • Amazon SageMaker Asynchronous Inference:
    Amazon SageMaker Asynchronous Inference is a near-real time inference option that queues incoming requests and processes them asynchronously. Use this option when you need to process large payloads as the data arrives or run models that have long inference processing times and do not have sub-second latency requirements. You are charged for the type of instance you choose.

  • Batch Transform
  • Amazon SageMaker Batch Transform
    Using Amazon SageMaker Batch Transform, there is no need to break down your data set into multiple chunks or manage real-time endpoints. SageMaker Batch Transform allows you to run predictions on large or small batch datasets. You are charged for the instance type you choose, based on the duration of use.

  • Serverless Inference
  • Amazon SageMaker Serverless Inference
    Amazon SageMaker Serverless Inference enables you to deploy machine learning models for inference without configuring or managing any of the underlying infrastructure. With Serverless Inference, you only pay for the compute capacity used to process inference requests, billed by the millisecond, and the amount of data processed. The compute charge depends on the memory configuration you choose.

  • JumpStart
  • Amazon SageMaker JumpStart
    Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning with one-click access to popular model collections (also known as “model zoos”). Jumpstart also offers end-to-end solutions that solve common ML use cases which can be customized for your needs. There is no additional charge for using JumpStart models or solutions. You will be charged for the underlying Training and Inference instance hours used the same as if you had created them manually.

Instance details

Amazon SageMaker P4d instance product details

Instance Size vCPUs Instance Memory (GiB) GPU GPU memory (GB) Network Bandwidth (Gbps) GPUDirect RDMA GPU Peer to Peer Instance Storage (GB) EBS Bandwidth (Gbps)
ml.p4d.24xlarge 96 1152 8 320 HBM2 400 ENA and EFA Yes 600 GB/s NVSwitch 8x1000 NVMe SSD 19

Amazon SageMaker P3 instance product details

Instance Size vCPUs Instance Memory (GiB) GPUs-V100 GPU memory (GB) Network Bandwidth (Gbps) GPU Peer to Peer EBS Bandwidth (Gbps)
ml.p3.2xlarge 8 61 1 16 Up to 10 N/A 1.5
ml.p3.8xlarge 32 244 4 64 10 NVLink 7
ml.p3.16xlarge 64 488 8 128 25 NVLink 14
ml.p3dn.24xlarge 96 768 8 256 100 NVLink 19

Amazon SageMaker G4 instance product details

Instance Size vCPUs Instance Memory (GiB) GPUs-T4 Network Bandwidth (Gbps) Instance Storage (GB) EBS Bandwidth (Gbps)
ml.g4dn.xlarge 4 16 1 Up to 25 1 x 125 NVMe SSD Up to 3.5
ml.g4dn.2xlarge 8 32 1 Up to 25 1 x 125 NVMe SSD Up to 3.5
ml.g4dn.4xlarge 16 64 1 Up to 25 1 x 125 NVMe SSD 4.75
ml.g4dn.8xlarge 32 128 1 50 1 x 900 NVMe SSD 9.5
ml.g4dn.16xlarge 64 256 1 50 1 x 900 NVMe SSD 9.5
ml.g4dn.12xlarge 48 192 4 50 1 x 900 NVMe SSD 9.5

Amazon SageMaker G5 instance product details

Instance Size vCPUs Instance Memory (GiB) GPUs-A10G GPU Memory (GiB) Network Bandwidth (Gbps) EBS Bandwidth (Gbps) Instance Storage (GB)
ml.g5n.xlarge 4 16 1 24 Up to 10 Up to 3.5 1x250
ml.g5.2xlarge 8 32 1 24 Up to 10 Up to 3.5 1x450
ml.g5.4xlarge 16 64 1 24 Up to 25 8 1x600
ml.g5.8xlarge 32 128 1 24 25 16 1x900
ml.g5.16xlarge 64 256 1 24 25 16 1x1900
ml.g5.12xlarge 48 192 4 96 40 16 1x3800
ml.g5.24xlarge 96 384 4 96 50 19 1x3800
ml.g5.48xlarge 192 768 8 192 100 19 2x3800

Amazon SageMaker Studio

You can now access Amazon SageMaker Studio, the first fully integrated development environment (IDE) at no additional charge. SageMaker Studio gives you complete access and visibility into each step required to build, train, and deploy models. Using SageMaker Studio, you pay only for the underlying compute and storage that you use within Studio.

You can use many services from SageMaker Studio, AWS SDK for Python (Boto3), or AWS CLI, including:

You pay only for the underlying compute and storage resources within SageMaker or other AWS services, based on your usage.

Amazon SageMaker Studio Lab

You can build and train ML models using Amazon SageMaker Studio Lab for free. SageMaker Studio Lab offers developers, academics, and data scientists a no-configuration development environment to learn and experiment with ML at no additional charge.

Amazon SageMaker Canvas

Amazon SageMaker Canvas expands ML access by providing business analysts the ability to generate accurate ML predictions using a visual point-and-click interface—no coding or ML experience required.

Amazon SageMaker Data Labeling

Amazon SageMaker Data Labeling provides two data labeling offerings, Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth. You can learn more about Amazon SageMaker Data Labeling, a fully managed data labeling service that makes it easy to build highly accurate training datasets for ML.

Amazon SageMaker shadow testing

SageMaker helps you run shadow tests to evaluate a new ML model before production release by testing its performance against the currently deployed model. There is no additional charge for SageMaker shadow testing other than usage charges for the ML instances and ML storage provisioned to host the shadow model. The pricing for ML instances and ML storage dimensions is the same as the real-time inference option specified in the preceding pricing table. There is no additional charge for data processed in and out of shadow deployments.

Amazon SageMaker Edge

Learn more about pricing for Amazon SageMaker Edge to optimize, run, and monitor ML models on fleets of edge devices. 

Amazon SageMaker Savings Plans

Amazon SageMaker Savings Plans help to reduce your costs by up to 64%. The plans automatically apply to eligible SageMaker ML instance usage, including SageMaker Studio notebooks, SageMaker notebook instances, SageMaker Processing, SageMaker Data Wrangler, SageMaker Training, SageMaker Real-Time Inference, and SageMaker Batch Transform regardless of instance family, size, or Region. For example, you can change usage from a CPU instance ml.c5.xlarge running in US East (Ohio) to a ml.Inf1 instance in US West (Oregon) for inference workloads at any time and automatically continue to pay the Savings Plans price. 

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Total cost of ownership (TCO) with Amazon SageMaker

Amazon SageMaker offers at least 54% lower total cost of ownership (TCO) over a three-year period compared to other cloud-based self-managed solutions. Learn more with the complete TCO analysis for Amazon SageMaker.

Pricing examples

Learn more about Amazon SageMaker

Visit the SageMaker overview page
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