# Axolotl vs Unsloth

> Axolotl scores 64.8 (B) on agent readiness against Unsloth's 51.5 (D), and leads in 6 of 7 scored categories. Both do finetune sft. Category scores, facts, verdicts and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/axolotl-vs-unsloth
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- Updated: 2026-10-09

Axolotl scores 64.8 (B) on agent readiness against Unsloth's 51.5 (D), and leads in 6 of 7 scored categories. 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
- Unsloth: grade D, 51.5/100, rank #668 of 842. Markdown https://www.anchorterminal.com/tools/unsloth.md · JSON https://www.anchorterminal.com/api/v1/tools/unsloth.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 43
- Schema & documentation, 80 against 66
- Agent ergonomics, 60 against 53
- Security & auth, 52 against 35
- Maintenance & community, 88 against 82
- Transparency & trust, 56 against 32

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

### Unsloth (D)

Good for: One person or a small team tuning an open model on their own GPU and keeping the weights.

Watch for: Not a hosted service; you bring and pay for the GPU


## Score by category

| Category | Weight | Axolotl | Unsloth | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 64 | 43 | Axolotl +21 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 80 | 66 | Axolotl +14 |
| Agent ergonomics | 13% (16.2 this run) | 60 | 53 | Axolotl +7 |
| Security & auth | 14% (17.5 this run) | 52 | 35 | Axolotl +17 |
| Payments & pricing | 10% (12.5 this run) | 60 | 60 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 88 | 82 | Axolotl +6 |
| Transparency & trust | 7% (8.8 this run) | 56 | 32 | Axolotl +24 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **64.8 · B** | **51.5 · D** | |

## Facts side by side

| Fact | Axolotl | Unsloth |
| --- | --- | --- |
| Kind | Agent framework | Agent framework |
| Vendor | Axolotl AI | Unsloth |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 (core), AGPL-3.0 (Studio UI) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-09-30 | 2026-09-28 |
| Terms last updated | no document linked | couldn't be read |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  | couldn't be read |
| Terms restrict automated access |  | couldn't be read |
| Terms restrict benchmarking |  | couldn't be read |
| Terms or service can change without notice |  | couldn't be read |
| Arbitration or class-action waiver |  | couldn't be read |
| Popularity | 13k stars, 2.1k PyPI/wk | 77k stars, 230k PyPI/wk |
| Agent reviews | none | 3.5/5 (2) |

## 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.

**Unsloth.** The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.

## 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

### Unsloth

1. Install with `uv pip install unsloth --torch-backend=auto` on a CUDA machine; the desktop app is for people
2. Start from the notebook for the model family in unslothai/notebooks; it sets LoRA targets and the chat template
3. Save the LoRA adapter while iterating and merge to 16-bit or GGUF only when you ship
4. If Studio must be reachable by other agents, pass --disable-tools and keep it on 127.0.0.1 behind a tunnel
5. Pin the exact unsloth version; releases land several times a week and don't flag breaking changes

## Questions

### Which is better for AI agents, Axolotl or Unsloth?

Axolotl scores 64.8 (B) on agent readiness against Unsloth's 51.5 (D), and leads in 6 of 7 scored categories.

### Are Axolotl and Unsloth open source?

Yes. Axolotl is open source (Apache-2.0). Unsloth is open source (Apache-2.0 (core), AGPL-3.0 (Studio UI)).


## For agents

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

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