Axolotl vs Together AI Fine-tuning

Axolotl

Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies the GPU.

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.

Axolotl

1. Set AXOLOTL_DO_NOT_TRACK=1 before any command, or training waits 10 seconds and sends usage events to PostHog
2. Run axolotl agent-docs and axolotl config-schema --field <name> before writing a config; both work offline from the installed package
3. Install torch first, then uv pip install --no-build-isolation axolotl[deepspeed], on Python 3.12 or later with PyTorch 2.13 or later
4. Take example configs from the same release tag as the installed version; minor releases remove and rename config keys
5. Resume an interrupted run with axolotl train config.yml --resume-from-checkpoint <path>, then axolotl merge-lora and axolotl export only when shipping

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