Darts alternatives

3 tools to consider instead of Darts, shown against it.

	Darts	sktime	Nixtla	PyTorch Forecasting
Vendor	Unit8 SA	sktime community (open source)	Nixtla	PyTorch Forecasting community (open source)
Pricing model	Open source + paid options	Open source + paid options	Free tier + paid plans	Open source + paid options
Free tier	Yes	Yes	Yes	Yes
Deployment	Self-hosted	Self-hosted	Cloud, Self-hosted	Self-hosted
Open source	Yes (Apache-2.0)	Yes (BSD-3-Clause)	Yes (Apache-2.0)	Yes (MIT)
Best for	Teams that want to benchmark many forecasting model types against the same dataset through one consistent interface.	Data scientists who want rigorous, leakage-free forecast evaluation using scikit-learn-style pipelines.	Teams forecasting at very large scale (thousands to millions of series) or wanting a no-training hosted forecasting API.	ML engineers forecasting large panels of related series (many products, stores or sensors) who want deep-learning models with covariates.
Pricing	Free and open source with no commercial tier. Pricing has not been verified yet — see the vendor's site.	Free and open source with no commercial tier. Pricing has not been verified yet — see the vendor's site.	Open-source libraries (StatsForecast, MLForecast, NeuralForecast) are free; TimeGPT is a custom enterprise subscription with no published price list. Pricing has not been verified yet — see the vendor's site.	Free and open source with no commercial tier. Pricing has not been verified yet — see the vendor's site.
Features	Unified fit/predict API across 30+ forecasting models Classical, gradient-boosting, deep-learning and foundation models in one library Probabilistic forecasting with prediction intervals Covariate (external variable) support Backtesting and model comparison utilities Anomaly detection via scorers and detectors Ensemble and conformal prediction wrappers	scikit-learn-compatible estimator interface for forecasting Time-series classification, regression and clustering in the same library Pipeline composition with detrending/deseasonalizing transformers Rolling-window and temporal cross-validation utilities Wrappers around statistical, ML and deep-learning forecasters Hierarchical and panel-data forecasting support Consistent API for model comparison and ensembling	AutoARIMA, AutoETS, AutoCES and Theta statistical models at scale Parallelized fitting across millions of time series TimeGPT hosted foundation model for zero-training forecasting Anomaly detection via TimeGPT Fine-tuning of TimeGPT on custom data External regressor and prediction-interval support Python-first API across the ecosystem	Temporal Fusion Transformer, N-BEATS, DeepAR-style and other neural forecasters TimeSeriesDataSet abstraction for panel/multi-series data Static and time-varying covariate support Built on PyTorch Lightning for training loops and logging Attention-based interpretability for Temporal Fusion Transformer Quantile/probabilistic forecasting output GPU-accelerated training for large panels

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