Together AI vs Unsloth

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

	Together AI Fine-tuning	Unsloth Fine-tuning
Tagline	Fine-tune & serve open-weight models (Llama, Mistral, DeepSeek).	Open-source LLM fine-tuning toolkit with custom kernels that train 2-30x faster and use up to 90% less VRAM.
Category	Fine-tuning	Fine-tuning
Pricing	Paid· Pay-per-token; fine-tuning per-token	Freemium· Free open-source; Pro and Enterprise contact sales
Model	Llama / Mistral / Qwen / DeepSeek and others	Llama, Mistral, Gemma, Qwen, GLM (multi-model)
Editorial score	8.6 / 10	—
Use cases	open modelsfine-tuninginference	lora-finetuningqloralocal-trainingdpo-orpomodel-quantizationgguf-export
Pros	Wide open-model catalogue Competitive inference pricing Fine-tune + serve in one place Dedicated endpoints for production	Real, measurable 2-5x speedups and big VRAM savings on consumer GPUs Open-source core with permissive license and active GitHub Drop-in compatible with Hugging Face TRL, PEFT and transformers Excellent ready-to-run Colab notebooks for most popular models Exports cleanly to GGUF/llama.cpp, vLLM and Ollama
Cons	Latency varies by model Less polish than OpenAI	Multi-GPU and multi-node are gated behind paid tiers with opaque pricing Not a hosted service — you still bring your own GPU and MLOps Cutting-edge model support sometimes lags official releases by days
Website	www.together.ai	unsloth.ai

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