Modal vs Unsloth
A side-by-side comparison of Modal and Unsloth, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Modal | Unsloth |
|---|---|---|
| Category (differs) | Inference | Fine-tuning |
| Pricing | FREEMIUM | FREEMIUM |
| License (differs) | Proprietary | Open core |
| Deployment (differs) | Cloud | Local |
| Platforms (differs) | API, CLI | CLI, Linux, Windows, macOS |
| Model support (differs) | Model-agnostic | Multi-model |
| Vendor (differs) | Modal Labs | Unsloth AI |
The honest brief
Modal
Define GPU infra in Python decorators with 2-4s cold starts — no YAML, Dockerfiles, or managed-stack lock-in.
- Python-decorator infra, no YAML/Dockerfiles
- Scale-to-zero, pay only when running
- Scales to hundreds of GPUs
- Free monthly starter credits
- SDK lock-in; migrating means rewriting
- No managed vLLM/TensorRT setup
- Costs climb under heavy usage
- Billing hard to predict
Unsloth
Hand-written CUDA kernels roughly halve fine-tuning time and VRAM, so 7B–13B models train on a single consumer GPU — free and Apache-2.0.
- LoRA, QLoRA, and full fine-tuning
- Supports Llama, Qwen, Gemma, DeepSeek
- Custom CUDA kernels under the hood
- Exports GGUF/Safetensors for llama.cpp/vLLM/Ollama
- Runs free on Colab/Kaggle
- Multi-GPU speedups are paid tiers
- NVIDIA-centric, CUDA-focused
- Supports a curated model set
- Requires ML fine-tuning know-how