What is Ludwig used for?

Ludwig is an open-source declarative deep learning framework that lets users define and train custom AI models using YAML configuration files, supporting tabular, text, image, and audio data. It is a good fit for training custom LLMs and neural networks, fine-tuning large language models, and building multi-modal models.

Is Ludwig open source?

Yes. Ludwig is open source under the Apache-2.0 licence. Its source code is on GitHub at ludwig-ai/ludwig and is written mainly in Python.

Yes. Ludwig is open source, so the software itself is free to use.

Can I self-host Ludwig?

Yes. Ludwig can be self-hosted on your own server or infrastructure.

What is Ludwig an alternative to?

Ludwig is an open-source alternative to H2O.ai. Other open-source alternatives to H2O.ai include Kubeflow, BentoML and MLflow.

Is Ludwig actively maintained?

Yes. The most recent commit to Ludwig was on 12 October 2026, and the latest release is v0.17.9, published on 16 August 2026. The project has 12k stars on GitHub.

Ludwig: open-source alternative to H2O.ai

About Ludwig

Ludwig is an open-source declarative deep learning framework for building, fine-tuning, and deploying custom models across tabular data, text, images, and audio. It is designed to reduce the engineering overhead of machine learning projects by allowing users to describe their model in a YAML file, which Ludwig then builds and runs.

The framework is built on PyTorch, integrates with HuggingFace and Ray, and is hosted by the Linux Foundation AI & Data. It aims to provide the simplicity of AutoML with the flexibility of raw PyTorch, offering expert-level control through configuration fields.

Key features

Declarative configuration: Define the entire ML pipeline in a single validated YAML file, including preprocessing, encoders
Multi-modal and multi-task: Mix tabular features, raw text, images, audio, and time series in a single model, and train multiple outputs
LLM fine-tuning suite: Supports SFT, DPO, KTO, ORPO, and GRPO, with LoRA, QLoRA, DoRA, and VeRA, including 4-bit quantization and
Scale without code changes: Add a backend like Ray to turn a local job into a distributed one without modifying the model code.

Good fit for

Training custom LLMs and neural networks
Fine-tuning large language models
Building multi-modal models

Built with: Python tools
Tags: deep-learning; yaml; llm; pytorch; multi-modal; fine-tuning

Ludwig: questions and answers

What is Ludwig used for?: Ludwig is an open-source declarative deep learning framework that lets users define and train custom AI models using YAML configuration files, supporting tabular, text, image, and audio data. It is a good fit for training custom LLMs and neural networks, fine-tuning large language models, and building multi-modal models.
Is Ludwig open source?: Yes. Ludwig is open source under the Apache-2.0 licence. Its source code is on GitHub at ludwig-ai/ludwig and is written mainly in Python.
Is Ludwig free?: Yes. Ludwig is open source, so the software itself is free to use.
Can I self-host Ludwig?: Yes. Ludwig can be self-hosted on your own server or infrastructure.
What is Ludwig an alternative to?: Ludwig is an open-source alternative to H2O.ai. Other open-source alternatives to H2O.ai include Kubeflow, BentoML and MLflow.
Is Ludwig actively maintained?: Yes. The most recent commit to Ludwig was on 12 October 2026, and the latest release is v0.17.9, published on 16 August 2026. The project has 12k stars on GitHub.