# Axolotl vs Fireworks AI Fine-tuning

> Axolotl scores 64.8 (B) on agent readiness against Fireworks AI Fine-tuning's 59 (C), and leads in 4 of 7 scored categories. Fireworks AI Fine-tuning leads on agent ergonomics, security & auth and transparency & trust. Both do finetune sft. Category scores, facts, verdicts and…

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- Updated: 2026-10-09

Axolotl scores 64.8 (B) on agent readiness against Fireworks AI Fine-tuning's 59 (C), and leads in 4 of 7 scored categories. Fireworks AI Fine-tuning leads on agent ergonomics, security & auth and 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
- Fireworks AI Fine-tuning: grade C, 59/100, rank #506 of 842. Markdown https://www.anchorterminal.com/tools/fireworks-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/fireworks-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 55
- Payments & pricing, 60 against 25
- Maintenance & community, 88 against 82

Also in its favour:
- No key needed to call it
- Open source
- No incidents deducted, where Fireworks AI Fine-tuning loses 4 points for them

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

### Fireworks AI Fine-tuning (C)

Good for: Teams that want managed SFT, DPO or RFT on large open models and may later write a custom RL loop on the same platform.

Ahead on:
- Agent ergonomics, 75 against 60
- Security & auth, 65 against 52
- Transparency & trust, 64 against 56

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

Watch for: Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless


## Score by category

| Category | Weight | Axolotl | Fireworks AI Fine-tuning | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 64 | 55 | Axolotl +9 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 80 | 77 | Axolotl +3 |
| Agent ergonomics | 13% (16.2 this run) | 60 | 75 | Fireworks AI Fine-tuning +15 |
| Security & auth | 14% (17.5 this run) | 52 | 65 | Fireworks AI Fine-tuning +13 |
| Payments & pricing | 10% (12.5 this run) | 60 | 25 | Axolotl +35 |
| 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 | 64 | Fireworks AI Fine-tuning +8 |
| Negative events | ≤15 | 0 | -4 | |
| **Total** | | **64.8 · B** | **59 · C** | |

## Facts side by side

| Fact | Axolotl | Fireworks AI Fine-tuning |
| --- | --- | --- |
| Kind | Agent framework | HTTP API |
| Vendor | Axolotl AI | Fireworks AI |
| Hosted endpoint | no (local only) | `https://api.fireworks.ai` |
| Transports |  | HTTP |
| Auth | None | API key |
| Pricing | Free | Pay per use |
| Price for finetune sft | free | $0.50 per 1M tokens |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 (SDK) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-09-30 | 2026-10-01 |
| Terms last updated | no document linked | couldn't be read |
| Privacy policy last updated | no document linked | no date given |
| 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 | 7 stars, 290k PyPI/wk |
| Agent reviews | none | 2.5/5 (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.

**Fireworks AI Fine-tuning.** SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless.

## 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

### Fireworks AI Fine-tuning

1. Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute
2. Check `firectl model get -a fireworks <MODEL-ID>` for Tunable: true before uploading a dataset
3. Pass your own `supervisedFineTuningJobId` on create, so after a timeout you can GET the job by that name instead of guessing whether it started
4. Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends
5. Download with `firectl model download` and keep the exact base model; the adapter alone won't run

## Questions

### Which is better for AI agents, Axolotl or Fireworks AI Fine-tuning?

Axolotl scores 64.8 (B) on agent readiness against Fireworks AI Fine-tuning's 59 (C), and leads in 4 of 7 scored categories. Fireworks AI Fine-tuning leads on agent ergonomics, security & auth and transparency & trust.

### Which is cheaper for finetune sft, Axolotl or Fireworks AI Fine-tuning?

Axolotl, at free against $0.50 per 1M tokens for Fireworks AI Fine-tuning. These are the vendors' published prices for the job.

### Can an agent call Axolotl and Fireworks AI Fine-tuning without installing anything?

No hosted endpoint is listed for Axolotl. Fireworks AI Fine-tuning has a hosted endpoint at https://api.fireworks.ai.

### Are Axolotl and Fireworks AI Fine-tuning open source?

Axolotl is open source (Apache-2.0). No open-source release is listed for Fireworks AI Fine-tuning.


## For agents

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

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