LLaMA Factory vs Ludwig

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

Tagline

LLaMA Factory

Open-source, no-code WebUI for fine-tuning 100+ open LLMs with LoRA, QLoRA, DPO, and PPO.

Ludwig

Declarative, YAML-driven deep learning framework for fine-tuning LLMs and multi-modal models without writing training loops.

Pricing

LLaMA Factory

Free· Free, open-source (Apache-2.0); self-hosted

Ludwig

Free· Free, Apache 2.0 open source

Free trial

LLaMA Factory

Yes

Ludwig

Yes

API

LLaMA Factory

Yes

Ludwig

Yes

Platforms

LLaMA Factory

api

Ludwig

linuxapi

Open source

LLaMA Factory

Yes · Apache-2.0

Ludwig

Yes · Apache-2.0

GitHub stars

LLaMA Factory

75,193

checked 2026-09-29

Ludwig

11,771

checked 2026-09-29

Last GitHub push

LLaMA Factory

2026-09-28

Ludwig

2026-09-28

First commit

LLaMA Factory

2023-05

Ludwig

2018-12

Model used

LLaMA Factory

Multi-model (LLaMA, Mistral, Qwen, Gemma, Phi, LLaVA, ChatGLM, Yi)

Ludwig

Multi-model (PyTorch + HuggingFace Transformers)

Best for

LLaMA Factory

Pick LLaMA Factory if you want one tool to fine-tune any open-weight LLM on your own hardware without writing custom training scripts.

Ludwig

Pick Ludwig if you want reproducible, config-driven fine-tuning runs across LLMs and multi-modal tasks without writing training loops from scratch.

Not for

LLaMA Factory

Skip it if you want a managed, click-to-train cloud service or don't have access to suitable GPUs.

Ludwig

Skip it if you want a hosted, click-to-fine-tune service or if your model needs deeply custom layers that don't fit a YAML schema.

Editorial score

LLaMA Factory

7.2 / 10

Ludwig

8.2 / 10

Use cases

LLaMA Factory

lora-fine-tuningqloradpo-alignmentinstruction-tuningrlhfvlm-fine-tuning

Ludwig

llm-fine-tuningmulti-modal-trainingtext-classificationtabular-mlmodel-servingdistributed-training

Pros

LLaMA Factory

No-code WebUI (LlamaBoard) covers SFT, DPO, PPO, KTO, and reward modeling
Supports 100+ open models including multimodal VLMs out of the box
Full QLoRA stack (2-8 bit) plus LoRA+, DoRA, PiSSA variants
Acceleration via FlashAttention-2, Unsloth, Liger Kernel, vLLM inference
Exports to GGUF / Ollama and integrates with W&B, MLflow, TensorBoard

Ludwig

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

Cons

LLaMA Factory

Self-hosted only — you bring the GPUs and the ops
Rapid release cadence means version pinning is essential
WebUI abstracts but does not solve VRAM and dataset-formatting pitfalls

Ludwig

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

Website

LLaMA Factory

llamafactory.readthedocs.io

Ludwig

ludwig.ai

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.

Pick LLaMA Factory if

✅ No-code WebUI (LlamaBoard) covers SFT, DPO, PPO, KTO, and reward modeling
✅ Supports 100+ open models including multimodal VLMs out of the box
✅ Full QLoRA stack (2-8 bit) plus LoRA+, DoRA, PiSSA variants
✅ Acceleration via FlashAttention-2, Unsloth, Liger Kernel, vLLM inference

Pick Ludwig if

✅ 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

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