Nebius Token Factory fine-tuning vs Together AI Fine-tuning

Nebius Token Factory fine-tuning

Supervised fine-tuning on 49 open base models through OpenAI-style /v1/fine_tuning/jobs calls, with LoRA or full weights and every checkpoint file downloadable. No fine-tuning price was found outside the script-drawn console, and the docs say tuned models deploy only to dedicated endpoints, with custom weights in beta on request.

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.

Nebius Token Factory fine-tuning

1. Use the OpenAI client with base_url https://api.tokenfactory.nebius.com/v1/ and NEBIUS_API_KEY. Upload JSONL with purpose=fine-tune, then create the job.
2. Set hyperparameters.lora to true for an adapter. The default is false, which runs full fine-tuning.
3. Poll GET /v1/fine_tuning/jobs/{job_id} no faster than every 15 seconds. There is no idempotency key, so list jobs before recreating one after a timeout.
4. Download every ID in a checkpoint's result_files before relying on hosted copies. The terms allow deletion of tuned models at three days' notice.
5. The spec requires wandb.api_key although the guide omits it, and it also accepts mlflow and hf integrations. Check the price in the console before starting a job.

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