Amazon SageMaker Autopilot
6.9#5 in automl softwareFree planFree planWebAPI
VisitAmazon SageMaker Autopilot vs MLJAR AutoML
MLJAR AutoML
7.1#3 in automl softwareFrom $20 a monthFree planWebLinuxmacOSSelf-hosted
VisitWhat both publish
| Fact | Amazon SageMaker Autopilot | MLJAR AutoML |
|---|---|---|
| Deployment | After training, users can deploy Autopilot models to real-time endpoints or run batch inference jobs. | Generated Mercury apps can be previewed locally, published through MLJAR Platform, or self-hosted with Docker. |
| Headquarters | Seattle, Washington, United States | Łapy, Poland |
| Integrations | Reviewer evidence describes integrations with Amazon S3, IAM, CloudWatch and ECR, while AWS documents Studio and Canvas integration. | The Python package integrates with Mercury to generate prediction apps that support single predictions and batch CSV predictions. |
| Purpose | Autopilot automatically creates machine learning models with full visibility and is now in SageMaker Canvas. | MLJAR AutoML automates supervised machine learning for tabular data, including binary classification, multiclass classification, and regression. |
Amazon SageMaker Autopilot plans
- On-demandPriced on request
- Savings PlansPriced on request
- Amazon SageMaker AI pay-as-you-goFree
- SageMaker AI Free TierFree
- On-demand SageMaker AI—
MLJAR AutoML plans
- FreeFree
- mljar-supervised Python packageFree
- Pro$20 a month
- Business$60 a month
- Perpetual license$199, paid once
Pick Amazon SageMaker Autopilot if…
It suits users who want automated model building and comparison while retaining visibility into data preparation and training choices. It can serve prediction tasks such as price or churn prediction, risk assessment, and time series forecasting.
Pick MLJAR AutoML if…
MLJAR AutoML suits people building supervised models from tabular data who want notebook or Python workflows. It is not aimed at forecasting or time-series modeling, and users should account for variation in reports and explanations.