Amazon Bedrock model customisation vs Nebius Token Factory fine-tuning

Amazon Bedrock model customisation

Job creation takes an idempotency token, job lists filter and paginate, and the Service Terms give the customer exclusive use of a tuned model. Jobs run in two US Regions only, weights can't be exported, and the newest customisation API change found dates from 28 May 2026.

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.

Amazon Bedrock model customisation

1. Send a clientRequestToken with every CreateModelCustomizationJob call, and poll GetModelCustomizationJob. Jobs are asynchronous and can take hours
2. Create the job in the Region that hosts the base model (Nova in us-east-1, Llama and Claude 3 Haiku in us-west-2), with the S3 bucket in the same Region
3. Pass an IAM service role that trusts bedrock.amazonaws.com and can read the training data and write the output location. The caller needs iam:PassRole
4. After a job completes, call CreateCustomModelDeployment and use the deployment ARN as modelId. Models outside the on-demand list need Provisioned Throughput
5. For gpt-oss-20b and Qwen3 32B, use /v1/fine_tuning/jobs on bedrock-mantle.us-west-2.api.aws with a Bedrock API key and a Lambda grader ARN

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.