Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI)

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.

Microsoft Foundry fine-tuning (Azure OpenAI)

SFT, DPO and RFT on GPT-4.1 and o4-mini through the OpenAI-shaped /openai/v1 API. No weight export; checkpoints copy only between Azure resources.

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

Microsoft Foundry fine-tuning (Azure OpenAI)

1. Point the OpenAI SDK at https://<resource>.openai.azure.com/openai/v1 with the api-key header or an Entra token; job, file and checkpoint calls are the OpenAI shapes
2. Read prices from the Azure Retail Prices API (meters named like 'gpt-4.1 FT Training global'), not the pricing page, which needs a browser
3. Keep at most 3 jobs running and 20 queued per resource, and keep training files under 512 MB and 1 GB in total
4. Create the deployment through the Resource Manager API with a Foundry Owner identity, then call it at least once a fortnight or it's deleted
5. Query the Models API for deprecationDate before choosing a base model