Anaplan vs FLAML
Anaplan
anaplan.comFLAML
microsoft.github.ioSide by side
| Figure | Anaplan | FLAML |
|---|---|---|
| Price from | — | — |
| Free plan | — | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| iOS | Yes | — |
| Android | Yes | — |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 1 | 0 |
| Spec score · Predictive Analytics Software | 8.1 (#11) | 7.9 (#14) |
| Integrations | Anaplan connects to third-party services through connectors or integration platforms, and its Integration API supports data movement, model interaction, and process execution. | The documentation describes MLflow logging and integration with Azure Machine Learning. |
| Support | Anaplan's customer care team is available 24x7 by phone, according to its support contact page. | The official site links to a Discord community. |
Plans
In their own words
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.