AutoGluon vs FLAML
AutoGluon
auto.gluon.aiFLAML
microsoft.github.ioSide by side
| Figure | AutoGluon | FLAML |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Predictive Analytics Software | 8.3 (#6) | 7.9 (#14) |
| Spec score · AutoML Software | 8.3 (#6) | 7.7 (#15) |
| Integrations | AutoMM adapts models from Hugging Face, TIMM, and MMDetection model zoos. | The documentation describes MLflow logging and integration with Azure Machine Learning. |
| License | The AutoGluon library is licensed under Apache 2.0. | The repository is distributed under the MIT License, permitting users to use, copy, modify, publish, distribute, sublicense, and sell the software subject to its conditions. |
| Purpose | AutoGluon automates machine learning for data such as tables and time series, helping users build predictive models with a few lines of code. | FLAML automates machine learning model selection and hyperparameter tuning with low computational resources. |
| Security reporting | The contribution guide asks users to report potential security issues to AWS/Amazon Security through its vulnerability reporting page rather than filing a public GitHub issue. | The project directs security vulnerability reports to Microsoft Security Response Center for coordinated disclosure rather than public issues. |
| Support | The project welcomes bug reports and feature requests through its GitHub issue tracker. | The official site links to a Discord community. |
| Supported tasks | The documentation includes image and text prediction, object detection, image segmentation, document prediction, entity extraction, and semantic matching examples. | Task-oriented AutoML supports classification, regression, time-series forecasting, panel forecasting, learning to rank, and sequence classification. |
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.
FLAML
FLAML is free, MIT-licensed open-source software for automated machine learning and tuning. It seeks accurate models or configurations while using low computational resources, and can adapt large language models to applications to reduce monetary costs. Supported AutoML tasks include classification, regression, time-series and panel forecasting, learning to rank, and sequence classification. The flaml.default package recommends data-dependent hyperparameter defaults without runtime tuning. Tuning handles large search spaces, different evaluation costs, constraints, guidance, and early stopping; users can restrict learners or tune a customized learner. Optional integrations include OpenAI models, CatBoost, Vowpal Wabbit, Prophet, statsmodels, Hugging Face Transformers, Ray, Spark, NNI, BlendSearch, Azure Synapse, MLflow, and Azure Machine Learning. Python support for the latest version is 3.10 or later and earlier than 3.14. Ray and Spark enable parallel tuning, but not together, and Spark parallel training does not support GPUs. A .NET implementation is available through ML.NET.