AutoGluon vs TPOT
AutoGluon
auto.gluon.aiTPOT
epistasislab.github.ioSide by side
| Figure | AutoGluon | TPOT |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Predictive Analytics Software | 8.3 (#6) | 7.3 (#23) |
| Spec score · AutoML Software | 8.3 (#6) | 7.3 (#18) |
| Founded | 2019 | 2016 |
| Integrations | AutoMM adapts models from Hugging Face, TIMM, and MMDetection model zoos. | The installation documentation lists scikit-learn and offers extra scikit-learn extensions through the tpot[sklearnex] installation option. |
| License | The AutoGluon library is licensed under Apache 2.0. | TPOT is free software distributed under the GNU Lesser General Public License version 3 or later, without warranty. |
| Purpose | AutoGluon automates machine learning for data such as tables and time series, helping users build predictive models with a few lines of code. | TPOT is a Python automated machine learning tool that optimizes machine learning pipelines using genetic programming. |
| Support | The project welcomes bug reports and feature requests through its GitHub issue tracker. | The project directs users to its GitHub issues to report bugs or suggest enhancements and to discuss extensions. |
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.