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📖 The AI Tool Bible

LLaMA Factory vs OpenAI Fine-tuning

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

 LLaMA Factory logo
LLaMA Factory
Fine-tuning
OpenAI Fine-tuning logo
OpenAI Fine-tuning
Fine-tuning
TaglineOpen-source, no-code WebUI for fine-tuning 100+ open LLMs with LoRA, QLoRA, DPO, and PPO.Fine-tune GPT-4o-mini and friends on your own data.
CategoryFine-tuningFine-tuning
PricingFree· Free, open-source (Apache-2.0); self-hostedPaid· Basic: $10 · Pro: $25 · Enterprise: Contact sales
ModelMulti-model (LLaMA, Mistral, Qwen, Gemma, Phi, LLaVA, ChatGLM, Yi)GPT-4o-mini / GPT-3.5
Editorial score7.2 / 108.4 / 10
Use cases
lora-fine-tuningqloradpo-alignmentinstruction-tuningrlhfvlm-fine-tuning
styleformatdomain knowledge
Pros
  • 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
  • Easiest fine-tuning UX
  • Vision FT now supported
  • Works inside the OpenAI ecosystem
  • Same infra/SLA as base models
Cons
  • 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
  • Pricier than open-model FT
  • No weights export
Websitellamafactory.readthedocs.ioplatform.openai.com
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 OpenAI Fine-tuning if
  • ✅ Easiest fine-tuning UX
  • ✅ Vision FT now supported
  • ✅ Works inside the OpenAI ecosystem
  • ✅ Same infra/SLA as base models
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