Data science & ML platforms · Amazon Web Services

Amazon SageMaker

AWS's fully managed service for building, training, and deploying machine learning models at cloud scale.

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Amazon SageMaker is AWS's managed machine learning platform, covering the full lifecycle from data preparation and notebook-based development through training, tuning, and production deployment. It includes SageMaker Studio as a unified IDE, Autopilot for automated model building, a feature store, model monitoring, and Pipelines for orchestrating repeatable ML workflows. Because it is deeply integrated with the rest of AWS, teams typically pair it with S3 for storage, Glue for data preparation, and IAM for access control rather than running it as a standalone product. SageMaker is cloud-only and billed per component used (notebook instance hours, training compute, endpoint hours, storage) rather than as a flat subscription, so total cost depends heavily on usage patterns and instance choices.

At a glance

Vendor Amazon Web Services
Pricing model Usage-based
Free tier Yes
Deployment Cloud
Open source No
Best for AWS-centric teams building, training, and deploying ML models at production scale.

Pricing

No flat subscription; billed per service component (notebooks, training, endpoints, storage) with some limited free monthly allowances.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Features

  • Managed Jupyter notebooks (SageMaker Studio)
  • Autopilot automated model building
  • Built-in algorithms and framework support
  • Distributed and large-scale model training
  • One-click real-time and batch inference endpoints
  • Feature Store for reusable features
  • Model Monitor for drift and quality checks
  • Pipelines for ML workflow orchestration

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Google Vertex AI Google Cloud's unified platform for AutoML, custom model training, and access to foundation models. Azure Machine Learning Microsoft's cloud platform for building, training, and operationalizing machine learning and generative AI models. Dataiku Collaborative platform spanning data preparation and machine learning, used by both analysts and data scientists.

Amazon SageMaker in the index now

Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Predictive analytics Analytics that uses historical data and statistical models to estimate what is likely to happen next. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Feature store A centralized system that stores, versions and serves machine learning features so training and live prediction use identical definitions. Model monitoring Ongoing tracking of a deployed model's inputs, predictions and outcomes to catch accuracy loss, drift or failures after launch. Workflow orchestration Scheduling, sequencing and monitoring interdependent data tasks so each step runs only after the ones it depends on succeed.

Related guides

How to choose a data science & ML platform The decisions that matter are who builds the models, how automated the modeling should be, and whether you want a cloud-native platform or a portable one.