Amazon SageMaker Pricing: Options, Examples, and 7 Ways to Cut Costs

Amazon SageMaker is a comprehensive machine-learning service designed to simplify the process of building, training, and deploying machine-learning models. It provides essential tools and capabilities that enable organizations to efficiently manage their ML workflows. As businesses increasingly adopt machine learning to gain insights and improve operations, SageMaker's integration with advanced AI features positions it as a key enabler of these transformations.

Trends such as the integration of generative AI capabilities and enhanced support for large-scale machine learning models are expected to boost its adoption further. For organizations looking to leverage SageMaker, understanding its pricing structure is crucial to avoid unexpected costs. This article explores SageMaker's pricing models and offers tips for cost optimization to help you manage your cloud spending effectively. Read on to learn more!

How Is Amazon SageMaker Priced?

Amazon SageMaker pricing is based on your usage of various services and features. You'll be charged for compute instances, storage, data transfer, and other services used during training, hosting, and data processing. There are different pricing models like on-demand and Savings Plans to optimize costs based on your needs, according to AWS. 

Amazon SageMaker is a fully managed service that provides a wide range of tools for high-performance, cost-effective machine learning across various use cases. It enables users to build, train, and deploy models at scale through an integrated development environment (IDE) that includes Jupyter notebooks, debuggers, profilers, pipelines, and other MLOps capabilities. 

Here's a breakdown of the key SageMaker pricing components:

• Compute instances: SageMaker offers a wide range of instance types optimized for different workloads (general purpose, compute-optimized, memory-optimized, GPU-based, etc.). Pricing varies based on the instance type, the number of vCPUs, memory, and GPU configuration. 
• Storage: You'll be charged for storage used for your data, models, and other artifacts in Amazon S3 and other storage services. 
• Data transfer: Data transfer costs apply when moving data in and out of SageMaker and other AWS services. 
• SageMaker features: Specific features like SageMaker Studio notebooks, Ground Truth data labeling, and SageMaker JumpStart have their own pricing models. For example, Ground Truth is priced per labeling task, and JumpStart costs vary based on the resources used and the model's complexity. 
• Training and inference: Costs are incurred for the time instances are used for training your models and for running inference (generating predictions). 

SageMaker offers the following pricing models:

• On-Demand: Pay for resources as you use them, without long-term commitments. 
• Savings Plans: Offer lower prices in exchange for a commitment to a consistent amount of usage over a period (one or three years). 
• AWS Marketplace: If you use products from the AWS Marketplace within SageMaker, you'll be charged based on the vendor's pricing model for those products. 

AWS SageMaker Pricing Overview

Amazon SageMaker uses a flexible, pay-as-you-go pricing model with no upfront costs or long-term commitments. Pricing varies depending on the specific SageMaker component used, such as compute resources, storage, and data processing. 

Each AWS service accessed through SageMaker is billed separately, and detailed pricing is available on the individual service pricing pages.

Here is how pricing works for AWS Sagemaker tools and components:

• SageMaker Unified Studio itself has no direct cost, but users are billed for the underlying AWS services consumed through it, such as compute, storage, and third-party integrations. If you use the quick setup option to create a domain, AWS may charge additional fees for networking resources configured on your behalf. To avoid unexpected costs, manual setup is recommended for visibility into resource usage.
  • The SageMaker Unified Studio Free Tier provides no-cost access to essential setup features like domain creation, project and user management, and policy configuration. It also supports AWS Free Tier allocations for services used through Studio—such as notebooks, SageMaker Catalog, and Amazon Q.
• SageMaker Lakehouse pricing depends on the underlying data storage and processing services used, such as Amazon S3 and Amazon Redshift. Costs may include data storage, compute resources, metadata management (billed under AWS Glue Data Catalog), and optional automated operations. Features like fine-grained permissions via AWS Lake Formation are provided at no additional charge.
• SageMaker AI is billed based on usage of compute instances, storage, deployment, and MLOps tools like Pipelines and Model Monitor. Additional services such as Feature Store and Data Wrangler may carry separate charges. Costs vary based on region, instance types, and workload.
• SageMaker Catalog, which governs data and model access, charges based on requests, metadata storage, compute, and AI-driven recommendations, with limited free usage included monthly.
• Amazon Bedrock (for generative AI), SQL Analytics (via Amazon Redshift), and SageMaker Data Processing follow their own usage-based pricing structures. For example, SQL workloads are charged based on Redshift instance hours, and data processing leverages services like AWS Glue and Amazon EMR which are priced separately.
• Amazon Q Developer is priced per user per month and supports tasks such as testing, security scanning, and optimization. 

Amazon SageMaker Pricing Models with Examples

SageMaker Free Tier

The SageMaker Free Tier provides limited, no-cost access to several services to help users get started. For SageMaker Catalog, each AWS account receives:

• 20 MB of metadata storage
• 4,000 API requests
• 0.2 compute units
  These allocations reset monthly and are available across any domain created in Amazon DataZone. Usage beyond these limits is billed at standard rates.

Examples:

• If your account uses 15 MB of metadata storage in a billing month, there’s no charge.
• If you make 10,000 API requests (excluding free API calls), you pay
  (10,000 - 4,000) × $10 / 100,000 = $0.60.
• For 1 compute unit, the charge is
  (1 - 0.2) × $1.776 = $1.42.

Additionally, certain core APIs such as CreateDomain, CreateProject, and Search are always free and don't count toward the 4,000 API request limit.

SageMaker On-Demand Pricing

SageMaker follows a pay-as-you-go pricing model. Users are charged based on their actual resource consumption across compute, storage, API usage, and data processing jobs.

Pricing Dimensions and Examples:

• Metadata Storage:
  Charged at $0.40 per GB beyond the free 20 MB
  • Using 100 MB results in
    (100 MB - 20 MB) × $0.40 / 1024 = $0.03125.
• API Requests:
  After 4,000 free requests, pricing is $10 per 100,000 requests
  • At 100,000 requests, cost is
    (100,000 - 4,000) × $10 / 100,000 = $9.60.
• Compute Usage:
  Billed at $1.776 per compute unit beyond the first 0.2 units
  • For 10 compute units, charge is
    (10 - 0.2) × $1.776 = $17.40.
• AI Recommendations (Tokens):
  • Input tokens: $0.015 per 1,000 tokens
  • Output tokens: $0.075 per 1,000 tokens
    These apply when generating business data descriptions or column summaries.

Amazon SageMaker Savings Plans

Amazon SageMaker Savings Plans offer a way to significantly reduce costs—up to 64%—in exchange for committing to a consistent hourly spend ($/hour) over a one- or three-year term. These plans apply automatically to a wide range of SageMaker workloads, including:

• SageMaker Studio Notebooks
• SageMaker On-Demand Notebooks
• SageMaker Processing
• SageMaker Data Wrangler
• SageMaker Training
• Real-Time Inference
• Batch Transform

The plans provide flexibility across instance families, regions, and SageMaker capabilities. For example, you can switch from a ml.c5.xlarge CPU-based training instance in US East (Ohio) to an ml.Inf1 inference instance in US West (Oregon), and the discounted Savings Plans rate will still apply.

Each eligible instance type has a specific Savings Plans rate and a corresponding On-Demand rate, as seen in the pricing table. For instance:

• ml.t3.large-Notebook
  • Savings Plans rate: $0.072/hour
  • On-Demand rate: $0.10/hour
  • Savings: 28%
• ml.m5.4xlarge-Notebook
  • Savings Plans rate: $0.6768/hour
  • On-Demand rate: $0.922/hour
  • Savings: 27%
• ml.m5d.24xlarge-Notebook
  • Savings Plans rate: $4.7664/hour
  • On-Demand rate: $6.509/hour
  • Savings: 27%

Best Practices for Optimizing Amazon SageMaker Costs

1. Choose Appropriate Inference Options

Inference costs can vary significantly depending on the deployment method chosen. SageMaker supports several inference options: real-time endpoints, batch transform, and serverless inference.

Real-time endpoints are ideal for low-latency applications but require always-on infrastructure, which can become expensive if the endpoint is underutilized. For workloads with sporadic or unpredictable traffic, serverless inference is often more cost-effective, as you only pay for compute time during active requests, not idle periods. Batch transform is best for high-volume offline predictions where latency is less important, such as processing historical data or scoring datasets in bulk.

Selecting the right inference mode requires analyzing traffic patterns, latency requirements, and cost per invocation. Consider setting up monitoring for invocation frequency and model latency to decide whether to switch to a more efficient deployment option.

2. Implement Multi-Model Endpoints (MME)

Multi-model endpoints (MMEs) allow a single SageMaker endpoint to host and serve multiple models. Instead of deploying a separate endpoint for each model, MME dynamically loads models into memory on-demand, serving predictions as requests arrive.

This significantly reduces infrastructure costs, especially in scenarios where many models are used intermittently—for example, in personalized recommendations, where each user might have a dedicated model. With MMEs, resources like compute instances and memory are shared, leading to higher utilization and lower total cost of ownership.

To get the most out of MMEs, organize models into Amazon S3 prefixes and monitor loading times. Use caching strategies to keep frequently accessed models in memory and configure memory allocation based on model sizes to prevent performance degradation.

3. Automate Resource Management

Manual oversight of machine learning resources can lead to unused instances and unnecessary costs. SageMaker provides automation tools like Lifecycle Configurations, Pipelines, and the Scheduler to help manage compute lifecycles.

Lifecycle Configurations can automatically shut down notebook instances after a specified period of inactivity or run custom scripts during instance startup and shutdown. Pipelines can orchestrate end-to-end ML workflows, reducing manual intervention and ensuring resources are only active during necessary processing steps. The Scheduler can start or stop resources like training jobs or inference endpoints at predefined intervals to match workload patterns.

Using these automation features reduces the risk of idle compute resources, lowers overall usage costs, and enforces consistency across development and production environments.

4. Optimize Data Storage

Efficient storage management is key to controlling long-term costs in SageMaker, especially when training large models or storing experiment results and logs. All data used in SageMaker is stored in Amazon S3, which offers different storage classes with varying cost-performance tradeoffs.

Implement lifecycle rules to move data from S3 Standard to lower-cost classes like S3 Infrequent Access, Glacier, or Deep Archive after a set period. Use versioning and tagging to identify obsolete or redundant files that can be deleted. When working with experiments, use SageMaker Experiments to log metadata and artifacts without duplicating entire datasets.

Also, avoid keeping unused models and training artifacts in active storage buckets. Regular cleanup of intermediate data and logs helps reduce storage costs without affecting operational efficiency.

5. Regularly Monitor Usage and Spending

Cost visibility is essential for avoiding unexpected charges. AWS provides tools like Cost Explorer, Budgets, and the SageMaker-specific usage dashboard to track resource consumption across training, inference, and data processing jobs. Setting up automated reports helps teams identify which workloads drive the majority of costs.

Enable CloudWatch metrics and logging to correlate spending with operational activity. For example, you can track endpoint invocation counts against real-time inference costs to verify whether usage aligns with expectations. This level of detail helps pinpoint inefficiencies, such as underutilized compute resources or oversized endpoints.

Establish alerts for budget thresholds to catch overspending early. Notifications can be integrated into Slack, email, or ticketing systems, ensuring quick action when costs deviate from projections. Continuous monitoring creates a feedback loop for refining workload planning and optimizing resource allocation.

6. Analyze and Leverage Savings Plans

Savings Plans offer predictable discounts, but maximizing their value requires careful workload analysis. Start by reviewing historical usage trends to identify services and instance families with consistent demand. Workloads like model training pipelines or production inference endpoints are strong candidates for Savings Plan commitments.

Evaluate both one-year and three-year commitments depending on business stability and long-term ML adoption. While three-year plans provide greater discounts, one-year terms allow more flexibility if workloads or budget priorities shift. Consider mixing both to balance savings and adaptability.

Regularly reassess Savings Plan coverage as usage evolves. If workloads grow beyond the committed amount, additional consumption falls back to on-demand rates, reducing overall efficiency. Monitoring utilization ensures the purchased plan matches actual usage patterns and avoids wasted capacity.

7. Establish Robust Forecasting

Accurate cost forecasting supports better budgeting and helps justify ML investments. Begin by modeling costs per project or team, factoring in compute, storage, and inference usage. Use tagging policies across SageMaker resources to categorize expenses, enabling granular forecasting and accountability.

Forecasts should account for scaling patterns, such as expected increases in training jobs during experimentation or surges in inference traffic during production rollouts. Incorporating historical seasonality and planned business initiatives makes forecasts more reliable.

Integrate forecasting with FinOps practices to align engineering and finance teams. Sharing projections and actuals creates transparency, improves decision-making, and ensures ML initiatives stay within budget while supporting growth.

How Can Finout Help You Manage AWS SageMaker Costs?

Finout helps manage AWS SageMaker costs by providing detailed cost allocation and visibility features, including unit cost per AI and telemetry-based shared cost reallocation. This allows for precise tracking of expenses by project, team, or department, even when resources are shared. With the "Virtual Tagging" feature, costs can be tagged on-the-fly, enabling refined cost tracking without extensive reconfiguration. Real-time monitoring and customizable dashboards provide up-to-date insights, helping identify cost anomalies and staying within budget.

Additionally, Finout offers actionable insights for optimizing SageMaker costs, such as instance right-sizing and utilizing more cost-effective pricing models. Integration with existing financial and operational tools ensures a unified view of cloud expenses, aligning cost management efforts with broader business strategies. Alerts and notifications for specific cost thresholds or unusual spending patterns help prevent cost overruns and ensure timely issue resolution. By leveraging these capabilities, organizations can achieve better control over their SageMaker expenses, optimize spending on machine learning projects, and enhance financial accountability.

Learn more about Finout’s AI cost management capabilities or book a demo to talk to our experts!