Together AI Fine-tuning vs Unsloth

Together AI Fine-tuning

31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour.

Unsloth

The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.

Together AI Fine-tuning

1. Call POST /v1/fine-tunes/estimate-price with the same body before creating the job, and check the model's minimum charge
2. Read lora_training.max_rank from the model limits response before setting lora_r; most models went to 128 on 2026-09-29
3. Don't retry a create call blindly after a timeout; there's no idempotency key, so list jobs and check first
4. Download with checkpoint=adapter if you'll merge locally; merged weights for a 70B model are a large stream
5. Tear down the dedicated endpoint once evaluation ends, since it bills while idle

Unsloth

1. Install with uv pip install unsloth --torch-backend=auto on a CUDA machine; the desktop app is for people
2. Start from the notebook for the model family in unslothai/notebooks; it sets LoRA targets and the chat template
3. Save the LoRA adapter while iterating and merge to 16-bit or GGUF only when you ship
4. If Studio must be reachable by other agents, pass --disable-tools and keep it on 127.0.0.1 behind a tunnel
5. Pin the exact unsloth version; releases land several times a week and don't flag breaking changes