Pricing overview

Amazon SageMaker AI 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 AI supports the leading ML frameworks, toolkits, and programming languages.

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

                                           

SageMaker AI Free Tier

SageMaker AI is free to try. You can get started with SageMaker AI for free. Your SageMaker AI Free Tier starts from the first month when you create your first SageMaker AI resource.

Amazon SageMaker AI 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.t3.medium instance on notebook instances
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On-demand pricing

  • JupyterLab

  • Amazon SageMaker JupyterLab
    Launch fully managed JupyterLab in seconds. Use the latest web-based interactive development environment for notebooks, code, and data. 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.

  • 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.

Instance details

Amazon SageMaker P6 instance product details

Instance Size Blackwell GPUs GPU memory (GB) vCPUs System memory (GiB) Instance storage (TB) Network bandwidth (Gbps) EBS bandwidth (Gbps) Available in UltraServers
ml.p6-b200.48xlarge 8 1,440 HBM3e 192 2048 8 x 3.84 8 x 400 100 No

ml.p6e- gb200.36xlarge		 4 740 HBM3e 144 960 3 x 7.5 4 x 400 60 Yes

*P6e-GB200 instances are only available in UltraServers

Amazon SageMaker UltraServer details

UltraServers offer a set of instances interconnected via a high bandwidth network domain. For example, the P6e-GB200 UltraServer connects up to 18 p6e-gb200.36xlarge instances under one NVIDIA NVLink domain. With 4 NVIDIA Blackwell GPUs per instance, each P6e-GB200 UltraServer therefore supports 72 GPUs, enabling you to run your largest AI workloads with high performance on SageMaker.

Instance Size Blackwell GPUs GPU memory (GB) vCPUs System memory (GiB) UltraServer Storage (TB) Aggregate EFA bandwidth (Gbps) EBS bandwidth (Gbps) Available in  UltraServers
ml.u-p6e-gb200x72 72 13320 2529 17280 405 28800 1080 Yes
ml.u-p6e-gb200x36 36 6660 1296 8640 202.5 14400 540 Yes

Amazon SageMaker P5 instance product details

Instance Size vCPUs Instance Memory (TiB) GPU Model  GPU Total GPU memory (GB) Memory per GPU (GB) Network Bandwidth (Gbps) GPUDirect RDMA GPU Peer to Peer Instance Storage (TB) EBS Bandwidth (Gbps)
ml.p5.48xlarge 192 2 NVIDIA H100 8 640 HBM3 80 3200 EFAv2   Yes 900 GB/s NVSwitch   8x3.84 NVMe SSD 80

Amazon SageMaker P4d instance product details

Instance Size vCPUs Instance Memory (GiB) GPU Model GPUs Total GPU memory (GB) Memory per GPU (GB) Network Bandwidth (Gbps) GPUDirect RDMA GPU Peer to Peer Instance Storage (GB) EBS Bandwidth (Gbps)
ml.p4d.24xlarge 96 1152 NVIDIA A100 8 320 HBM 2 40 400 ENA AND EFA Yes 600 GB/s NVSwitch 8x1000 NVMe SSD 19
ml.p4de.24xlarge 96 1152 NVIDIA A100 8 640 HNM2e 80 400 ENA and EFA Yes 600 GB/s NVSwitch 8X1000 NVMe SSD 19

Amazon SageMaker P3 instance product details

Instance Size vCPUs Instance Memory (GiB) GPU Model GPUs Total GPU memory (GB) Memory per GPU (GB) Network Bandwidth (Gbps) GPU Peer to Peer Instance Storage (GB) EBS Bandwidth (Gbps)
ml.p3.2xlarge 8 61 NVIDIA V100 1 16 16 Up to 10 N/A EBS-Only 1.5
ml.p3.8xlarge 32 244 NVIDIA V100 4. 64 16 10 NVLink EBS-Only 7
ml.p3.16xlarge 64 488 NVIDIA V100 8 128 16 25 NVLink EBS-Only 14
ml.p3dn.24xlarge 96 768 NVIDIA V100 8 256 32 100 NVLink 2 x 900 NVMeSSD 19

Amazon SageMaker G4 instance product details

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

Amazon SageMaker G5 instance product details

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

Amazon SageMaker Trn1 instance product details

Instance Size vCPUs Memory (GiB) Trainium Accelerators Total Accelerator Memory (GB) Memory per Accelerator (GB) Instance Storage (GB) Network Bandwidth (Gbps) EBS Bandwidth (Gbps)
ml.trn1.2xlarge 8 32 1 32 32 1 x 500 NVMe SSD Up to 12.5 Up to 20
ml.trn1.32xlarge 128 512 16 512 32 4 x 2000 NVMe SSD 800 80

Amazon SageMaker Inf1 instance product details

Instance Size vCPUs Memory (GiB) Inferentia Accelerators Total Accelerator Memory (GB) Memory per Accelerator (GB) Instance Storage  Inter-accelerator Interconnect Network Bandwidth (Gbps) EBS Bandwidth (Gbps)
ml.inf1.xlarge 4 8 1 8 8 EBS only N/A Up to 25 Up to 4.75
ml.inf1.2xlarge 8 16 1 8 8 EBS only N/A Up to 25 Up to 4.75
ml.inf1.6xlarge 24 48 4 32 8 EBS only Yes 25 4.75
ml.inf1.24xlarge 96 192 16 128 8 EBS only yes 100 19

Amazon SageMaker Inf2 instance product details

Instance Size vCPUs Memory (GiB) Inferentia Accelerators Total Accelerator Memory (GB) Memory per Accelerator (GB) Instance Storage  Inter-accelerator Interconnect Network Bandwidth (Gbps) EBS Bandwidth (Gbps)
ml.inf2.xlarge 4 16 1 32 32 EBS only N/A Up to 25 Up to 10
ml.inf2.8xlarge 32 128 1 32 32 EBS only N/A Up to 25 10
ml.inf2.24xlarge 96 384 6 196 32 EBS only Yes 50 30
ml.inf2.48xlarge 192 768 12 384 32 EBS only Yes 100 60

Amazon SageMaker Studio

Amazon SageMaker Studio is a single web-based interface for complete ML development, offering a choice of fully managed integrated development environments (IDEs) and purpose-built tools. You can access SageMaker Studio free of charge. You are only charged for the underlying compute and storage that you use for different IDEs and ML tools within SageMaker Studio.

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

  • IDEs on SageMaker Studio to perform complete ML development with a broad set of fully managed IDEs, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code – Open Source), and RStudio
  • SageMaker Pipelines to automate and manage ML workflows
  • SageMaker Autopilot to automatically create ML models with full visibility
  • SageMaker Experiments to organise 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.

To use Amazon Q Developer Free Tier on Jupyter Lab and Code Editor, follow the instructions here. To use Amazon Q Developer Pro on Jupyter Lab, you must subscribe to Amazon Q Developer. Amazon Q Developer pricing is available here.

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

  • As a data scientist, you spend 20 days using JupyterLab for quick experimentation on notebooks, code, and data for 6 hours per day on an ml.g4dn.xlarge instance. You create and then run a JupyterLab space to access the JupyterLab IDE. The compute is only charged for the instance used when the JupyterLab space is running. Storage charges for a JupyterLab space accrued until it is deleted.

    Compute

    Instance Duration Days Total duration Cost per hour Total
    ml.r6i.4xlarge 6 hours 20 6 * 20 = 120 hours €1.440 €172.80

    Storage

    You will be using General Purpose SSD storage for 480 hours (24 hours * 20 days). In a Region that charges €0.1211 per GB-month:
    €0.1211 per GB-month * 5 GB * 480 / (24 hours/day * 30-day month) = €0.4037

  • 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.c6i.2xlarge €0.459481 €0.150460
    General purpose (SSD) storage (GB)  Cost per hour Total
    100 GB * 2 = 200 €0.14 €0.0032

    The subtotal for Amazon SageMaker Processing job = €0.15046
    Assume the subtotal for 200 GB of general purpose SSD storage = €0.0032
    The total price for this example would be €0.15366

  • The total charges for training and debugging in this example are €24.077292. The compute instances and general purpose storage volumes used by Amazon SageMaker Debugger built-in rules do not incur additional charges.

      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

     

    Hours Training instance Debug instance Cost per hour  Subtotal
    4 * 0.5 = 2.00 ml.c6i.2xlarge n/a €0.459481 €0.91896
    4 * 0.5 * 2 = 4 n/a No additional charges for built-in rule instances €0 €0
    4 * 0.5 = 2 ml.c7i.48xlarge n/a €11.579165 €23.1583
            -------
            €24.07729

    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.