Lamini vs Unsloth

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

	Lamini Fine-tuning	Unsloth Fine-tuning
Tagline	Memory-tuning platform for grounding LLMs in your facts.	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· Enterprise / contact sales	Freemium· Free open-source; Pro and Enterprise contact sales
Model	Lamini (built on open base models)	Llama, Mistral, Gemma, Qwen, GLM (multi-model)
Editorial score	7.7 / 10	8.2 / 10
Use cases	enterprise FTfactual recallmemory tuning	lora-finetuningqloralocal-trainingdpo-orpomodel-quantizationgguf-export
Pros	Focused on factual recall Reduces hallucinations on your facts Self-hostable option Enterprise SLAs	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	Niche use case Enterprise-only pricing	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.lamini.ai	unsloth.ai

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