Amazon SageMaker: Basics, Pricing, and Cost Optimization Tips

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!

Table of Contents

1. What is Amazon SageMaker?

2. Amazon SageMaker Pricing Models

3. Best Practices for Managing Amazon SageMaker Costs

4. How Can Finout Help You Manage AWS SageMaker Costs?

What is Amazon SageMaker?

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. 

Image Source: Amazon SageMaker

In addition, SageMaker facilitates the creation of large foundational models (FMs) that are trained on extensive datasets. It provides specialized tools for fine-tuning, experimenting, retraining, and deploying these models. SageMaker also offers access to hundreds of pre-trained models, including publicly available foundational models, which can be deployed with just a few clicks. This versatility allows organizations to streamline their machine-learning workflows and focus on delivering value through data-driven insights.

Use Cases of SageMaker:

• Data Preparation and Analysis: Tools like SageMaker Data Wrangler and Canvas simplify data preparation and analysis, enabling users to transform raw data into actionable insights without extensive coding.
• Model Training and Tuning: SageMaker provides built-in algorithms and supports custom model training, allowing users to train models efficiently. Features like SageMaker Experiments help track and manage iterations for improved model performance.
• Deployment and Monitoring: SageMaker offers multiple deployment options, including real-time, batch, and asynchronous inference, making it suitable for a wide range of applications. With SageMaker Model Monitor, users can maintain model accuracy and performance.

For more detailed and updated information on Amazon SageMaker, you can keep a check on Amazon SageMaker Documentation.

Amazon SageMaker Pricing Models

Amazon SageMaker offers a variety of pricing models to accommodate different usage needs and budget constraints:

Amazon SageMaker Free Tier

Amazon SageMaker is available to try for free as part of the AWS Free Tier, offering new users two months of free usage to explore its features without initial costs. Here’s what the free tier includes:

• Studio Notebooks and Notebook Instances: 250 hours of ml.t3.medium instance on Studio notebooks OR 250 hours of ml.t2.medium or ml.t3.medium instance on notebook instances per month for the first 2 months.

• RStudio on SageMaker: 250 hours of ml.t3.medium instance on RSession app AND a free ml.t3.medium instance for RStudioServerPro app per month for the first 2 months.

• Data Wrangler: 25 hours of ml.m5.4xlarge instance per month for the first 2 months.

• Feature Store: 10 million write units, 10 million read units, and 25 GB storage (standard online store) per month for the first 2 months.

• Training: 50 hours of m4.xlarge or m5.xlarge instances per month for the first 2 months.

• Amazon SageMaker with TensorBoard: 300 hours of ml.r5.large instance per month for the first 2 months.

• Real-Time Inference: 125 hours of m4.xlarge or m5.xlarge instances per month for the first 2 months.

• Serverless Inference: 150,000 seconds of on-demand inference duration per month for the first 2 months.

• Canvas: 160 hours/month for session time per month for the first 2 months.

• HyperPod: 50 hours of m5.xlarge instance per month for the first 2 months.

This comprehensive free tier lets users experiment with various SageMaker capabilities, making it ideal for learning and testing without financial commitment. For more updated information, you can check Amazon SageMaker Free Tier Pricing. 

Amazon SageMaker On-Demand Pricing

The on-demand pricing model charges users based on the resources they consume, with no upfront commitments. This flexibility allows organizations to scale their machine learning workloads according to their needs. SageMaker On-Demand billing applies to multiple features including Studio Classic, JupyterLab, Code Editor, RStudio, and many more. You can access all aspects of on-demand pricing here. 

Amazon SageMaker Savings Plan

The SageMaker Savings Plan offers significant cost savings in exchange for a commitment to a consistent amount of usage over a one- or three-year term. By opting for a savings plan, organizations can reduce their SageMaker costs by up to 64% compared to on-demand pricing. Key features include:

• Commitment Options: Users can choose between one-year or three-year commitments, with lower rates for longer terms.
  
  
• Eligibility: 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.

This model is suitable for organizations with predictable workloads looking to optimize their cloud spending while maintaining flexibility.

Best Practices for Managing Amazon SageMaker Costs

Effectively managing costs in Amazon SageMaker is essential for keeping machine learning projects within budget while maximizing their potential. Here are some best practices to optimize your cloud spending and ensure cost efficiency.

• Regularly Monitor Usage and Spending:  Use AWS Cost Explorer to gain insights into your SageMaker spending patterns and identify cost drivers. Regular monitoring allows you to stay on top of your budget and make informed decisions to control costs effectively. You can also set up AWS Budgets and alerts to notify you when costs approach predefined thresholds, allowing proactive management.
• Analyze and Leverage Savings Plans:  Consider AWS Savings Plans to reduce overall costs by committing to consistent usage over one or three years for significant discounts. Analyze your SageMaker workloads to determine the best savings plan strategy based on usage patterns, and regularly check your plan utilization to maximize benefits and adjust commitments as needed.
• Establish Robust Forecasting: Develop forecasting models using historical usage data to predict future SageMaker needs, helping you anticipate demand changes and adjust resource allocation. Incorporate industry trends and upcoming projects into forecasts, and align these forecasts with business goals to make informed decisions about resource allocation and budget planning.

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!