Nebius Token Factory fine-tuning vs Vertex AI Gemini 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.

Vertex AI Gemini tuning

Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.

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.

Vertex AI Gemini tuning

1. Use client.tunings.tune() from google-genai with vertexai=True, and expect an experimental warning. Tuning isn't available on the Gemini Developer API
2. Add .md.txt to any docs.cloud.google.com URL to read the page as Markdown
3. Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20
4. List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it
5. Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes