SageMaker Savings Plans

As machine learning workloads scale, cloud costs can quickly get out of hand. AWS SageMaker Savings Plans offer a flexible pricing model where you commit to a steady level of SageMaker usage in exchange for lower rates than standard On-Demand pricing.

What Are SageMaker Savings Plans?

SageMaker Savings Plans are a flexible pricing model from AWS that help you save on Amazon SageMaker usage in exchange for a long-term commitment. Instead of paying purely On-Demand rates, you commit to a consistent amount of SageMaker usage (in $/hour) for 1 or 3 years, and in return you can save up to about 64% compared to standard On-Demand pricing.

These plans automatically apply to eligible SageMaker ML usage—including Studio notebooks, notebook instances, Processing, Data Wrangler, Training, Real-Time Inference, and Batch Transform—regardless of instance family, size, or AWS Region. This means you can switch between instance types or Regions as your ML workloads evolve and still benefit from the same SageMaker Savings Plan discounts.

How SageMaker Savings Plans Work

At a high level, SageMaker Savings Plans are simple: you commit to a steady level of SageMaker usage, and AWS automatically gives you lower rates on that usage.

Commit to an hourly spend

You choose a commitment amount, like $10/hour of SageMaker usage, measured in $/hour. AWS then uses this as your baseline commitment.

Discounts are applied automatically

AWS automatically applies the SageMaker Savings Plan rates to any eligible SageMaker usage (up to your committed amount) without you needing to manage specific instances or regions.

Covers many SageMaker workloads

The plan works across key SageMaker components such as training, inference, processing, notebooks, and Data Wrangler, as long as they’re billed as eligible SageMaker ML instance usage.

Flexible payment options
You can choose how you want to pay for the commitment:

• All Upfront
• Partial Upfront
• No Upfront (All three options give you the same functional discount model; they just differ in cash flow and effective savings)

Imagine a team that runs nightly training jobs plus a couple of always-on inference endpoints. On On-Demand pricing, their SageMaker usage might fluctuate between $4 and $7 per hour depending on the day. By committing to $5/hour under a SageMaker Savings Plan, most of their steady training and inference usage is covered by the discounted rate. When your SageMaker usage in an hour stays at or below $5, that usage is charged at the discounted Savings Plan rate; only the spikes above $5 fall back to standard On-Demand pricing.

Over time, this adds up to meaningful savings without the team having to constantly resize or reshuffle instances just to optimize costs.

Benefits of Using SageMaker Savings Plans

SageMaker Savings Plans are designed for teams that want to keep ML costs under control without giving up flexibility.

• Lower costs for steady ML workloads
  If you’re running long-lived training jobs, always-on inference endpoints, or recurring pipelines, committing to a spend through SageMaker Savings Plans can significantly reduce your bill compared to pure On-Demand pricing—especially for predictable, repeatable workloads.
• No manual reservation management
  You don’t have to track individual Reserved Instances, match them to specific instance types, or worry about under-utilized reservations. The discount is applied automatically to any eligible SageMaker usage up to your committed amount.
• More flexibility than classic Reserved Instances
  SageMaker Savings Plans apply across instance families, sizes, Regions, and SageMaker components, so you can change instance types, scale up or down, or shift workloads geographically and still keep the benefit of the plan. This is a big improvement over traditional RIs, which are usually tied to a specific instance family and Region.
• Built for continuous ML operations
  These plans work especially well for teams doing continuous model training, retraining, and inference at scale—for example, production systems that retrain models nightly and serve predictions 24/7. In those scenarios, the combination of steady usage and built-in flexibility makes SageMaker Savings Plans a natural fit.

Conclusion

SageMaker Savings Plans offer a simple, commitment-based way to reduce the cost of running machine learning workloads on AWS. If your ML workloads are more “always on” than “one-off experiment,” it’s worth analyzing your current SageMaker spend to see whether a Savings Plan can turn that existing usage into guaranteed discounts and meaningfully lower your long-term cloud bill — all while adding very little operational complexity.