FLAML vs Forecast Pro
FLAML
microsoft.github.ioForecast Pro
forecastpro.comSide by side
| Figure | FLAML | Forecast Pro |
|---|---|---|
| Price from | — | $199/mo |
| Free plan | — | — |
| Free trial | — | Yes |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 2 |
| Spec score · Predictive Analytics Software | 7.9 (#14) | 8.9 (#2) |
| Integrations | The documentation describes MLflow logging and integration with Azure Machine Learning. | Data can be exchanged through Excel, CSV or text files and direct ODBC database connections, with outputs commonly sent to ERP or MRP systems. |
| Purpose | FLAML automates machine learning model selection and hyperparameter tuning with low computational resources. | Forecast Pro creates statistically based business forecasts that can be integrated into broader planning systems. |
| Support | The official site links to a Discord community. | Subscriptions include product updates plus unlimited technical support by phone and email while active. |
Plans
Forecast Pro
- Forecast Pro Professional$199 a month
- Forecast Pro TRACPriced on request
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.
Forecast Pro
Forecast Pro creates statistically based business forecasts for use in broader planning systems. Its automatic best-pick algorithm considers seasonal patterns, trends, business cycles, promotional activity, and other factors. Users can forecast at multiple hierarchy levels while working with units, dollars, and other measures at once. Adjustments can be made at any level, with comments and override reports documenting and reconciling changes. The software archives prior forecasts so users can compare them with actual results and review forecast value added across process steps. Data can move through Excel, CSV, text files, or ODBC, and the maker says the product integrates with ERP, MRP, and BI systems. Forecast Pro runs on Windows and can be installed on a client machine or server, including Citrix or Windows Server. The SDK is also available as a Linux shared library, with.NET and Java interfaces. Professional starts at $199 a month, billed annually, with a 12-month minimum. A free trial provides all features for 21 days and ends automatically.