# Axolotl vs Nebius Token Factory fine-tuning

> Axolotl scores 64.8 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 5 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency & trust. Both do finetune sft. Category scores, facts, verdicts and agent notes side by…

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- Updated: 2026-10-09

Axolotl scores 64.8 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 5 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency & trust. Both do finetune sft.

- Axolotl: grade B, 64.8/100, rank #307 of 842. Markdown https://www.anchorterminal.com/tools/axolotl.md · JSON https://www.anchorterminal.com/api/v1/tools/axolotl.json
- Nebius Token Factory fine-tuning: grade D, 47.7/100, rank #738 of 842. Markdown https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/nebius-token-factory-fine-tuning.json

## Which one, for what

### Axolotl (B)

Good for: A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.

Ahead on:
- Reliability, 64 against 45
- Schema & documentation, 80 against 68
- Agent ergonomics, 60 against 51
- Payments & pricing, 60 against 0
- Maintenance & community, 88 against 61

Also in its favour:
- No key needed to call it
- Open source

Watch for: Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way

### Nebius Token Factory fine-tuning (D)

Good for: Teams that want supervised LoRA or full fine-tuning of a wide list of open models, up to Qwen3 Coder 480B and DeepSeek, through OpenAI-style calls, with EU storage and the weights to take away.

Ahead on:
- Transparency & trust, 79 against 56

Also in its favour:
- A hosted endpoint, with nothing to install

Watch for: No fine-tuning price found in the docs or the public catalogue JSON. The price page is a script-drawn console page that robots.txt disallows


## Score by category

| Category | Weight | Axolotl | Nebius Token Factory fine-tuning | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 64 | 45 | Axolotl +19 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 80 | 68 | Axolotl +12 |
| Agent ergonomics | 13% (16.2 this run) | 60 | 51 | Axolotl +9 |
| Security & auth | 14% (17.5 this run) | 52 | 52 | even |
| Payments & pricing | 10% (12.5 this run) | 60 | 0 | Axolotl +60 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 88 | 61 | Axolotl +27 |
| Transparency & trust | 7% (8.8 this run) | 56 | 79 | Nebius Token Factory fine-tuning +23 |
| Negative events | ≤15 | 0 | -2 | |
| **Total** | | **64.8 · B** | **47.7 · D** | |

## Facts side by side

| Fact | Axolotl | Nebius Token Factory fine-tuning |
| --- | --- | --- |
| Kind | Agent framework | HTTP API |
| Vendor | Axolotl AI | Nebius |
| Hosted endpoint | no (local only) | `https://api.tokenfactory.nebius.com/v1` |
| Transports |  | HTTP |
| Auth | None | API key |
| Pricing | Free | Pay per use |
| x402 | no | no |
| Licence | Apache-2.0 | Proprietary service (cookbook examples MIT) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-09-30 | 2026-09-30 |
| Terms last updated | no document linked | 2026-09-28 |
| Privacy policy last updated | no document linked | 2026-09-23 |
| Customer content may train models |  | not found in the text |
| Terms restrict automated access |  | not found in the text |
| Terms restrict benchmarking |  | yes |
| Terms or service can change without notice |  | not found in the text |
| Arbitration or class-action waiver |  | yes |
| Popularity | 13k stars, 2.1k PyPI/wk | none |

## Verdicts

**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.

**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.

## Before you call either

### 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

### 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.

## Questions

### Which is better for AI agents, Axolotl or Nebius Token Factory fine-tuning?

Axolotl scores 64.8 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 5 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency & trust.

### Can an agent call Axolotl and Nebius Token Factory fine-tuning without installing anything?

No hosted endpoint is listed for Axolotl. Nebius Token Factory fine-tuning has a hosted endpoint at https://api.tokenfactory.nebius.com/v1.

### Are Axolotl and Nebius Token Factory fine-tuning open source?

Axolotl is open source (Apache-2.0). No open-source release is listed for Nebius Token Factory fine-tuning.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "axolotl", "b": "nebius-token-factory-fine-tuning"}`. From a terminal: `anchor compare axolotl nebius-token-factory-fine-tuning`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/axolotl.json and https://www.anchorterminal.com/api/v1/tools/nebius-token-factory-fine-tuning.json

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