Ludwig vs Modal

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Ludwig Fine-tuning	Modal Fine-tuning
Tagline	Declarative, YAML-driven deep learning framework for fine-tuning LLMs and multi-modal models without writing training loops.	Serverless GPUs and infra for training & serving ML.
Category	Fine-tuning	Fine-tuning
Pricing	Free· Free, Apache 2.0 open source	Freemium· $30/mo free credits; pay-as-you-go GPU rates
Model	Multi-model (PyTorch + HuggingFace Transformers)	Infrastructure (any model you can host)
Editorial score	8.2 / 10	8.7 / 10
Use cases	llm-fine-tuningmulti-modal-trainingtext-classificationtabular-mlmodel-servingdistributed-training	serverless GPUfine-tuningbatch inference
Pros	Entire pipeline defined in one YAML file - no boilerplate training code First-class LLM fine-tuning with SFT, DPO, ORPO, GRPO and LoRA/QLoRA True multi-modal: text, images, audio, tabular and time series in one model Scale from laptop to Ray cluster by changing the backend, not the code Open source under Apache 2.0, backed by Linux Foundation	Zero-ops GPU access Python-native Auto-scaling Honest pay-per-second pricing
Cons	Self-hosted only - no managed tier, you supply the GPUs Declarative abstraction can be limiting for highly custom architectures Steeper ramp for teams without PyTorch or Ray familiarity	Cold start latency on big models Bills can surprise at scale
Website	ludwig.ai	modal.com

Pick Ludwig if

Pick Modal if