Fireworks AI Fine-tuning vs Together AI Fine-tuning

Fireworks AI Fine-tuning

SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless.

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.

Fireworks AI Fine-tuning

1. Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute
2. Check firectl model get -a fireworks <MODEL-ID> for Tunable: true before uploading a dataset
3. Pass your own supervisedFineTuningJobId on create, so after a timeout you can GET the job by that name instead of guessing whether it started
4. Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends
5. Download with firectl model download and keep the exact base model; the adapter alone won't run

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