📖 The AI Tool Bible

Modal vs Unsloth

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

 
Modal
Fine-tuning
Unsloth
Fine-tuning
TaglineServerless GPUs and infra for training & serving ML.Open-source LLM fine-tuning toolkit with custom kernels that train 2-30x faster and use up to 90% less VRAM.
CategoryFine-tuningFine-tuning
PricingFreemium· $30/mo free credits; pay-as-you-go GPU ratesFreemium· Free open-source; Pro and Enterprise contact sales
ModelInfrastructure (any model you can host)Llama, Mistral, Gemma, Qwen, GLM (multi-model)
Editorial score8.7 / 10—
Use cases
serverless GPUfine-tuningbatch inference
lora-finetuningqloralocal-trainingdpo-orpomodel-quantizationgguf-export
Pros
  • Zero-ops GPU access
  • Python-native
  • Auto-scaling
  • Honest pay-per-second pricing
  • 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
  • Cold start latency on big models
  • Bills can surprise at scale
  • 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
Websitemodal.comunsloth.ai
Pick Modal if
  • ✅ Zero-ops GPU access
  • ✅ Python-native
  • ✅ Auto-scaling
  • ✅ Honest pay-per-second pricing
Pick Unsloth if
  • ✅ 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
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