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Amazon SageMaker Autopilot vs AutoGluon

Amazon SageMaker Autopilot

aws.amazon.com

AutoGluon

auto.gluon.ai

Side by side

FigureAmazon SageMaker AutopilotAutoGluon
Price fromFreeFree
Free planYesYes
Free trialYes—
Open source——
WebYes—
Windows—Yes
macOS—Yes
Linux—Yes
APIYes—
Self-hosted—Yes
Plans published51
Spec score · AutoML Software8.7 (#3)8.3 (#6)
DeploymentAfter training, users can deploy Autopilot models to real-time endpoints or run batch inference jobs.The project points to AutoGluon Cloud and deep learning containers for cloud training and deployment, and describes SageMaker Autopilot as a managed AutoGluon experience.
Founded20242019
IntegrationsReviewer evidence describes integrations with Amazon S3, IAM, CloudWatch and ECR, while AWS documents Studio and Canvas integration.AutoMM adapts models from Hugging Face, TIMM, and MMDetection model zoos.
PurposeAutopilot automatically creates machine learning models with full visibility and is now in SageMaker Canvas.AutoGluon automates machine learning for data such as tables and time series, helping users build predictive models with a few lines of code.
SupportAWS provides support plans with proactive guidance, issue resolution, and tools.The project welcomes bug reports and feature requests through its GitHub issue tracker.

Plans

Amazon SageMaker Autopilot

  • On-demandPriced on request
  • Savings PlansPriced on request
  • Amazon SageMaker AI pay-as-you-goFree
  • SageMaker AI Free TierFree
  • On-demand SageMaker AIPrice not published

Amazon SageMaker Autopilot pricing →

AutoGluon

  • AutoGluonFree

AutoGluon pricing →

In their own words

Amazon SageMaker Autopilot

Amazon SageMaker Autopilot has no write-up on Specifiction yet; its figures are above.

Visit aws.amazon.com

AutoGluon

AutoGluon is a free, open-source Python library for automating machine learning across tabular, time-series, and multimodal data. For tables, it trains classification and regression models while handling data cleaning, feature engineering, hyperparameter optimization, and model selection. Its TimeSeriesPredictor produces probabilistic forecasts for multiple time series using historical data and related covariates. AutoMM reduces manual work in adapting foundation models to specialized data. Documentation examples cover image and text prediction, object detection, image segmentation, document prediction, entity extraction, and semantic matching. AutoMM can adapt models from Hugging Face, TIMM, and MMDetection collections. Forecasting methods include ETS and ARIMA from StatsForecast, LightGBM, DeepAR and Temporal Fusion Transformer from GluonTS, and Chronos. AutoGluon supports Python 3.10 through 3.13 on Linux, macOS, and Windows. GPU usage is not supported on macOS; the installation guide points GPU users to Linux or Windows. AutoGluon is licensed under Apache 2.0. Cloud options include AutoGluon Cloud, described as a free wrapper, and Amazon SageMaker, where compute and S3 storage are billed to the user’s AWS account.

Visit auto.gluon.ai