Amazon SageMaker Pricing in 2026

Pricing Overview

Amazon SageMaker uses a pure usage-based pricing model, which means there are no fixed monthly plans or per-seat subscriptions. You pay for each AWS service you consume: notebook instances, training jobs, inference endpoints, storage, and data processing. Pricing starts at $0.04 per hour for basic notebook instances and scales steeply depending on instance type, with some GPU instances costing over $10 per hour. A free tier covers 250 hours of notebook usage, 50 hours of training, and 125 hours of hosting on ml.t3.medium instances during the first two months. After that, every minute of compute counts toward your bill. We find this model transparent in theory but dangerously unpredictable in practice. Without careful governance and budget alerts, monthly bills can balloon quickly, especially when GPU-intensive training jobs or idle inference endpoints are left running unmonitored.

Plan Comparison

SageMaker does not offer traditional subscription plans. Instead, costs are determined entirely by compute instance selection and usage duration across three core activities: notebooks, training, and inference hosting. AWS provides three purchasing approaches to manage these costs.

The free tier is useful for evaluation only. Any serious ML workload will quickly exhaust the included hours. We recommend Savings Plans for teams with predictable training schedules, as the discount of up to 64% is substantial and applies across notebooks, training, and inference. Serverless inference looks attractive for low-traffic models at just $0.01 per request, but cold starts of 5-10 seconds make it unsuitable for latency-sensitive applications. For most production use cases, real-time inference endpoints at $0.23 per hour remain the practical choice despite the continuous cost.

Hidden Costs and Considerations

SageMaker's headline per-hour rates tell only part of the story. S3 storage costs for training data and model artifacts add up quickly, especially for teams working with large datasets. Data transfer fees between S3 and SageMaker instances are an often-overlooked line item that compounds with scale. EBS volumes attached to notebook instances incur charges even when the notebook is stopped but not deleted. We strongly recommend setting up AWS Budgets alerts and implementing auto-shutdown policies for idle resources. Feature Store operations at $0.03 to $0.07 per call can also accumulate for high-throughput pipelines.

How Amazon SageMaker Pricing Compares

SageMaker's usage-based model stands in sharp contrast to the freemium tiers and per-seat pricing offered by MLOps-focused competitors. For teams that only need experiment tracking and model registry capabilities rather than managed compute infrastructure, the alternatives are dramatically cheaper and more cost-predictable.

The comparison is not entirely apples-to-apples, and that distinction matters. SageMaker provides fully managed compute infrastructure for training and inference, while Weights & Biases, ClearML, and Comet ML focus on the experiment tracking and orchestration layer without provisioning GPU clusters. If you are already committed to AWS and need end-to-end managed infrastructure from notebooks to production endpoints, SageMaker is hard to beat despite its complexity. But if your team primarily needs experiment tracking and model versioning while running training on your own infrastructure or Kubernetes clusters, a tool like Weights & Biases at $60 per user per month or ClearML starting at $15 per month is far more cost-predictable with no risk of runaway compute bills. We think SageMaker makes the most economic sense for enterprise teams running heavy GPU workloads who can commit to Savings Plans and have dedicated ML platform engineers to manage resource utilization.