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Ludwig vs openpi

Ludwig or openpi? Their plans monthly and yearly, the capabilities their makers state, security and latest updates, side by side — read from the makers' own pages.

In inglese

In short

  • Both: Command line, Choice of models, Self-hosted, API

Ludwig

Open-source framework for training, fine-tuning, evaluating, and serving custom AI models with YAML configuration.

Plans

Not read from the maker’s page yet.

Prices checked 2026-09-24 on the maker’s page.

Capabilities

  • Command line — “One command to serve your model as a REST API.” source
  • Choice of models — “Use any HuggingFace model as a backbone.” source
  • Self-hosted — “Train locally with CPU or GPU. Fast iteration, no setup.” source
  • API — “One command to serve your model as a REST API.” source

Latest updates

  • v0.17.9 (v0.17.9)

    Checkpoint loading now uses PyTorch’s restricted unpickler to prevent code execution from crafted checkpoints.

  • v0.17.8: path traversal fix in dataset archive extraction (v0.17.8)

    Fixed path traversal in dataset archive extraction through symlink members.

  • v0.17.7 (v0.17.7)

    Fixed Ray preprocessing tests for Arrow-backed data and row ordering.

About Ludwig

openpi

Open-source robotics models and packages for running, fine-tuning, and serving vision-language-action policies.

Plans

Free

Free

  • Unlimited public/private repositories
  • Dependabot security and version updates
  • 2,000 CI/CD minutes/month
  • 500MB of Packages storage
  • Issues & Projects
  • Community support

Team

  • $4 per user/month
  • Access to GitHub Codespaces
  • Repository rules
  • Multiple reviewers in pull requests
  • Draft pull requests
  • Code owners
  • 3,000 CI/CD minutes/month

Enterprise

  • Starting at $21 per user/month
  • Data residency
  • Enterprise Managed Users
  • User provisioning through SCIM
  • Enterprise account to centrally manage multiple organizations
  • Environment protection rules
  • Audit Log API

Prices checked 2026-09-25 on the maker’s page.

Capabilities

  • Command line — “Now we can kick off training with the following command” source
  • Choice of models — “Currently, this repo contains three types of models:” source
  • Self-hosted — “To run the models in this repository, you will need an NVIDIA GPU with at least the following specifications.” source
  • API — “The PyTorch implementation uses the same API as the JAX version - you only need to change the checkpoint path to point to the converted PyTorch model:” source
About openpi