LLaMA Factory vs Together AI

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

	LLaMA Factory Fine-tuning	Together AI Fine-tuning
Tagline	Open-source, no-code WebUI for fine-tuning 100+ open LLMs with LoRA, QLoRA, DPO, and PPO.	Fine-tune & serve open-weight models (Llama, Mistral, DeepSeek).
Category	Fine-tuning	Fine-tuning
Pricing	Free· Free, open-source (Apache-2.0); self-hosted	Paid· Pay-per-token; fine-tuning per-token
Model	Multi-model (LLaMA, Mistral, Qwen, Gemma, Phi, LLaVA, ChatGLM, Yi)	Llama / Mistral / Qwen / DeepSeek and others
Editorial score	—	8.6 / 10
Use cases	lora-fine-tuningqloradpo-alignmentinstruction-tuningrlhfvlm-fine-tuning	open modelsfine-tuninginference
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	Wide open-model catalogue Competitive inference pricing Fine-tune + serve in one place Dedicated endpoints for production
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	Latency varies by model Less polish than OpenAI
Website	llamafactory.readthedocs.io	www.together.ai

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