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|
RStudio on SageMaker
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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|
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|
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:
- SageMaker Pipelines to automate and manage ML workflows
- SageMaker Autopilot to automatically create ML models with full visibility
- SageMaker Experiments to organize and track your training jobs and versions
- SageMaker Debugger to debug anomalies during training
- SageMaker Model Monitor to maintain high-quality models
- SageMaker Clarify to better explain your ML models and detect bias
- SageMaker JumpStart to easily deploy ML solutions for many use cases. You may incur charges from other AWS Services used in the solution for the underlying API calls made by Amazon SageMaker on your behalf.
- SageMaker Inference Recommender to get recommendations for the right endpoint configuration
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.
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 example #1: Studio notebooks
A data scientist goes through the following sequence of actions while using Amazon SageMaker Studio notebooks.
- Opens notebook 1 in a TensorFlow kernel on an ml.c5.xlarge instance, then works on this notebook for 1 hour.
- Opens notebook 2 on an ml.c5.xlarge instance. It will automatically open in the same ml.c5.xlarge instance that is running notebook 1.
- Works on notebook 1 and notebook 2 simultaneously for 1 hour.
- The data scientist will be billed for a total of two (2) hours of ml.c5.xlarge usage. For the overlapped hour where she worked on notebook 1 and notebook 2 simultaneously, each kernel application will be metered for 0.5 hour and she will be billed for 1 hour.
Kernel application Notebook instance Hours Cost per hour Total TensorFlow ml.c5.xlarge 1 $0.204 $0.204 TensorFlow ml.c5.xlarge 0.5 $0.204 $0.102 Data Science ml.c5.xlarge 0.5 $0.204 $0.102 $0.408
Pricing example #2: RStudio on SageMaker
A data scientist goes through the following sequence of actions while using RStudio on SageMaker:
- Launches RSession 1 on an ml.c5.xlarge instance, then works on this notebook for 1 hour.
- Launches RSession 2 on an ml.c5.xlarge instance. It will automatically open in the same ml.c5.xlarge instance that is running RSession 1.
- Works on RSesssion 1 and RSession 2 simultaneously for 1 hour.
- The data scientist will be billed for a total of two (2) hours of ml.c5.xlarge usage. For the overlapped hour where she worked on RSession 1 and RSession 2 simultaneously, each RSession application will be metered for 0.5 hour and she will be billed for 1 hour.
Meanwhile, the RServer is running 24/7 no matter whether there are running RSessions or not. If the admin chooses “Small” (ml.t3.medium), then it is free of charge. If the admin chooses “Medium” (ml.c5.4xlarge) or “Large” (ml.c5.9xlarge), then it is charged hourly as far as RStudio is enabled for the SageMaker Domain.
RSession app RSession instance Hours Cost per hour Total Base R ml.c5.xlarge 1 $0.204 $0.204 Base R ml.c5.xlarge 0.5 $0.204 $0.102 Base R ml.c5.xlarge 0.5 $0.204 $0.102 $0.408
Pricing example #3: Processing
Amazon SageMaker Processing only charges you for the instances used while your jobs are running. When you provide the input data for processing in Amazon S3, Amazon SageMaker downloads the data from Amazon S3 to local file storage at the start of a processing job.
The data analyst runs a processing job to preprocess and validate data on two ml.m5.4xlarge instances for a job duration of 10 minutes. She uploads a dataset of 100 GB in S3 as input for the processing job, and the output data (which is roughly the same size) is stored back in S3.
Hours Processing instances Cost per hour Total 1 * 2 * 0.167 = 0.334 ml.m5.4xlarge $0.922 $0.308 General purpose (SSD) storage (GB) Cost per hour Total 100 GB * 2 = 200 $0.14 $0.0032
The subtotal for Amazon SageMaker Processing job = $0.308.
The subtotal for 200 GB of general purpose SSD storage = $0.0032.
The total price for this example would be $0.3112.
Pricing example #4: Data Wrangler
As a data scientist, you spend three days using Amazon SageMaker Data Wrangler to cleanse, explore, and visualize your data for 6 hours per day. To execute your data preparation pipeline, you then initiate a SageMaker Data Wrangler job that is scheduled to run weekly.
The table below summarizes your total usage for the month and the associated charges for using Amazon SageMaker Data Wrangler.
Application SageMaker Studio instance Days Duration Total duration Cost per hour Cost sub-total SageMaker Data Wrangler ml.m5.4xlarge 3 6 hours 18 hours $0.922 $16.596 SageMaker Data Wrangler job ml.m5.4xlarge - 40 minutes 2.67 hours $0.922 $2.461
From the table, you use Amazon SageMaker Data Wrangler for a total of 18 hours over 3 days to prepare your data. Additionally, you create a SageMaker Data Wrangler job to prepare updated data on a weekly basis. Each job lasts 40 minutes, and the job runs weekly for one month.
Total monthly charges for using Data Wrangler = $16.596 + $2.461 = $19.097
Pricing example #5: Feature Store
You have a web application that issues reads and writes of 25 KB each to the Amazon SageMaker Feature Store. For the first 10 days of a month, you receive little traffic to your application, resulting in 10,000 writes and 10,000 reads each day to the SageMaker Feature Store. On day 11 of the month, your application gains attention on social media and application traffic spikes to 200,000 writes and 200,000 reads that day. Your application then settles into a more regular traffic pattern, averaging 80,000 writes and 80,000 reads each day through the end of the month.
The table below summarizes your total usage for the month and the associated charges for using Amazon SageMaker Feature Store.
Day of the month Total writes Total write units Total reads Total read units Days 1 to 10 100,000 writes
(10,000 writes * 10 days)
(100,000 * 25KB )
(10,000 * 10 days)
(100,000 * 25/4 KB )
Day 11 200,000 writes 5,000,000
200,000 reads 1,400,000++
Days 12 to 30 1,520,000 writes
(80,000 * 19 days)
(1,520,000 * 25KB)
(80,000 * 19 days)
(1,520,000 * 25/4KB)
Total chargeable units 45,500,000 write units 12,740,000 read units Monthly charges for writes and reads $56.875
(45.5 million write units * $1.25 per million writes)
(12.74M read units * $0.25 per million reads)
++ All fractional read units are rounded to the next whole number
Total data stored = 31.5 GB
Monthly charges for data storage = 31.5 GB * $0.45 = $14.175
Total monthly charges for Amazon SageMaker Feature Store = $56.875 + $3.185 + $14.175 = $74.235
Pricing example #6: Training
A data scientist has spent a week working on a model for a new idea. She trains the model 4 times on an ml.m4.4xlarge for 30 minutes per training run with Amazon SageMaker Debugger enabled using 2 built-in rules and 1 custom rule that she wrote. For the custom rule, she specified ml.m5.xlarge instance. She trains using 3 GB of training data in Amazon S3, and pushes 1 GB model output into Amazon S3. SageMaker creates general-purpose SSD (gp2) volumes for each training instance. SageMaker also creates general-purpose SSD (gp2) volumes for each rule specified. In this example, a total of 4 general-purpose SSD (gp2) volumes will be created. SageMaker Debugger emits 1 GB of debug data to the customer’s Amazon S3 bucket.
Hours Training instance Debug instance Cost per hour Subtotal 4 * 0.5 = 2.00 ml.m4.4xlarge n/a $0.96 $1.92 4 * 0.5 * 2 = 4 n/a No additional charges for built-in rule instances $0 $0 4 * 0.5 = 2 ml.m5.xlarge n/a $0.23 $0.46 ------- $2.38 General purpose (SSD) storage for training (GB) General purpose (SSD) storage for debugger built-in rules (GB) General purpose (SSD) storage for debugger custom rules (GB) Cost per GB-month Subtotal Capacity used 3 2 1 Cost $0 No additional charges for built-in rule storage volumes $0 $0.10 $0
The total charges for training and debugging in this example are $2.38. The compute instances and general purpose storage volumes used by Amazon SageMaker Debugger built-in rules do not incur additional charges.
Pricing example #7: Real-time inference
The model in example #5 is then deployed to production to two (2) ml.c5.xlarge instances for reliable multi-AZ hosting. Amazon SageMaker Model Monitor is enabled with one (1) ml.m5.4xlarge instance and monitoring jobs are scheduled once per day. Each monitoring job take 5 minutes to complete. The model receives 100 MB of data per day, and inferences are 1/10 the size of the input data.
Hours per month Hosting instances Model Monitor instances Cost per hour Total 24 * 31 * 2 = 1488 ml.c5.xlarge $0.204 $303.522 31*0.08 = 2.5 ml.m5.4xlarge $0.922 $2.305 Data In per month - hosting Data Out per month - hosting Cost per GB In or Out Total 100 MB * 31 = 3,100 MB $0.016 $0.0496 10 MB * 31 = 310 MB $0.016 $0.00496
The subtotal for training, hosting, and monitoring = $305.827. The subtotal for 3,100 MB of data processed In and 310MB of data processed Out for hosting per month = $0.054. The total charges for this example would be $305.881 per month.
Note, for built-in rules with ml.m5.xlarge instance, you get up to 30 hours of monitoring aggregated across all endpoints each month, at no charge.
Pricing example #8: Asynchronous Inference
Amazon SageMaker Asynchronous Inference charges you for instances used by your endpoint. When not actively processing requests, you can configure auto-scaling to scale the instance count to zero to save on costs. For input payloads in Amazon S3, there is no cost for reading input data from Amazon S3 and writing the output data to S3 in the same Region.
The model in example #5 is used to run an SageMaker Asynchronous Inference endpoint. The endpoint is configured to run on 1 ml.c5.xlarge instance and scale down the instance count to zero when not actively processing requests. The ml.c5.xlarge instance in the endpoint has a 4 GB general-purpose (SSD) storage attached to it. In this example, the endpoint maintains an instance count of 1 for 2 hours per day and has a cooldown period of 30 minutes, after which it scales down to an instance count of zero for the rest of the day. Therefore, you are charged for 2.5 hours of usage per day.
The endpoint processes 1,024 requests per day. The size of each invocation request/response body is 10 KB, and each inference request payload in Amazon S3 is 100 MB. Inference outputs are 1/10 the size of the input data, which are stored back in Amazon S3 in the same Region. In this example, the data processing charges apply to the request and response body, but not to the data transferred to/from Amazon S3.
Hours per month Hosting instances Cost per hour Total 2.5 * 31 * 1 = 77.5 ml.c5.xlarge $0.20 $15.81 General-purpose (SSD) storage (GB) Cost per Gb-month Total 4 $0.14 $0.56 Data In per month Data Out per month Cost per GB In or Out Total 10 KB * 1,024 * 31 = 310 MB 10 KB * 1,024 * 31 = 310 MB $0.02 0.0048 10 KB * 1,024 * 31 = 310 MB $0.02 0.0048
The subtotal for SageMaker Asynchronous Inference = $15.81 + $0.56 + 2 * .0048 = $16.38. The total Asynchronous Inference charges for this example would be $16.38 per month.
Pricing example #9: Batch Transform
Amazon SageMaker Batch Transform only charges you for the instances used while your jobs are running. If your data is already in Amazon S3, then there is no cost for reading input data from S3 and writing output data to S3 in the same Region.
The model in example #5 is used to run SageMaker Batch Transform. The data scientist runs four separate SageMaker Batch Transform jobs on 3 ml.m4.4xlarge for 15 minutes per job run. She uploads an evaluation dataset of 1 GB in S3 for each run, and inferences are 1/10 the size of the input data, which are stored back in S3.
Hours Training instances Cost per hour Total 3 * 0.25 * 4 = 3 hours ml.m4.4xlarge $0.96 $2.88 GB data In - Batch Transform GB data Out - Batch Transform Cost per GB In or Out Total 0 0 $0.02 $0
The subtotal for SageMaker Batch Transform job = $2.88. The subtotal for 4.4 GB into Amazon S3 = $0. The total charges for this example would be $2.90.
Pricing example #10: Serverless Inference
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.
If you allocated 2 GB of memory to your endpoint, executed it 10 million times in one month and it ran for 100 ms each time, and processed 10 GB of Data-In/Out total, your charges would be calculated as follows:
Monthly compute charges
Number of requests Duration of each request Total inference duration (sec) Cost per sec Monthly inference duration charge 10 M 100 ms 1M $0.00004 $40
Monthly data process charges
Data processing (GB) Cost per GB In or Out Monthly data processing charge 10 GB $0.016 $0.16
The subtotal for SageMaker Serverless Inference duration charge = $40. The subtotal for 10 GB data processing charge = $0.16. The total charges for this example would be $40.16.
Pricing example #11: Jumpstart
Customer uses JumpStart to deploy a pre-trained BERT Base Uncased model to classify customer review sentiment as positive or negative.
The customer deploys the model to two (2) ml.c5.xlarge instances for reliable multi-AZ hosting. The model receives 100 MB of data per day, and inferences are 1/10 the size of the input data.
Hours per month Hosting instances Cost per hour Total 24 * 31 * 2 = 1488 ml.c5.xlarge $0.204 $303.55 Data In per month - Hosting Data Out per month - Hosting Cost per GB In or Out
100 MB * 31 = 3,100 MB $0.02 $0.06 10 MB * 31 = 310 MB $0.02 $0.01
The subtotal for training, hosting, and monitoring = $305.827. The sub-total for 3,100 MB of data processed In and 310 MB of data processed Out for Hosting per month = $0.06. The total charges for this example would be $305.887 per month.