AutoGluon vs PyCaret
AutoGluon
auto.gluon.aiPyCaret
pycaret.orgSide by side
| Figure | AutoGluon | PyCaret |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | — | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 2 |
| Spec score · Predictive Analytics Software | 8.3 (#6) | 8.4 (#5) |
| Deployment | The project points to AutoGluon Cloud and deep learning containers for cloud training and deployment, and describes SageMaker Autopilot as a managed AutoGluon experience. | Models can be saved as portable scikit-learn pipelines, served behind FastAPI, or deployed through the optional pycaret-server layer. |
| Founded | 2019 | LinkedIn lists PyCaret as founded in 2020. |
| Integrations | AutoMM adapts models from Hugging Face, TIMM, and MMDetection model zoos. | PyCaret wraps or interoperates with scikit-learn, XGBoost, Microsoft LightGBM, spaCy, sktime, pyod, FastAPI, React, and Plotly. |
| Purpose | AutoGluon automates machine learning for data such as tables and time series, helping users build predictive models with a few lines of code. | PyCaret is an open-source, low-code Python tool for automating machine-learning workflows. |
| Support | The project welcomes bug reports and feature requests through its GitHub issue tracker. | The FAQ directs users to GitHub Discussions for questions and GitHub Issues for bug reports. |
Plans
In their own words
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.