Ludwig

ludwig.ai · checked 9 Oct 2026

#5 · Deep Learning Software

Visit ludwig.ai ↗ Alternatives Ludwig vs ONNX Runtime

How it plugs in

NoPublic API

NoOpen source

YesFree tier

Signal on Deep Learning Software6.6#5 of 35

Ludwig is an open-source declarative deep learning framework for building, fine-tuning, and deploying custom models without writing training loops.

Pricing

Open sourceApache 2.0 license · no paid plans listed on the official siteFree

As ludwig.ai lists them, checked 9 Oct 2026.

Near it

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Runs on

Web
Windows
Mac
Linux
iOS
Android

Facts

Website: ludwig.ai
Price: Free tier
Free trial: Not stated
Configuration: Users define preprocessing, encoders, architecture, training, and hyperparameter optimization in a validated YAML file.
Customization: Users can plug in custom encoders, decoders, combiners, loss functions, and metrics, and use HuggingFace models as backbones.
Data and tasks: The framework supports tabular, text, image, audio, time series, geospatial, vector, date/time, sequence, and anomaly data tasks.
Distributed training: A Ray backend enables distributed training using DDP, FSDP, or DeepSpeed, and the site also lists Kubernetes and KubeRay support.
Experiment tracking: The site says Ludwig integrates with W&B, MLflow, TensorBoard, Comet ML, and Aim, and generates training reports and visualizations.
Explainability: The site lists automatic baseline training, feature importance, model explainability, and visualizations.
Extensibility: Users can plug in custom encoders, decoders, combiners, loss functions, and metrics, and use HuggingFace models as backbones.
Formats: Supported data formats include CSV, TSV, JSON, Parquet, Feather, HDF5, Pandas DataFrames, and Dask DataFrames.
Hyperparameter optimization: Built-in HPO integrates Ray Tune and Optuna, with SQLite or PostgreSQL persistence.
License: The site identifies Ludwig as open source under the Apache 2 License.
License and hosting: The project is described as open source under the Apache 2.0 License and hosted by Linux Foundation AI & Data.
LLM fine-tuning: The site lists SFT, DPO, KTO, ORPO, and GRPO, plus LoRA, QLoRA, DoRA, and VeRA methods.

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