# Axolotl vs Fireworks AI Fine-tuning (slim)

> 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…

- Full: https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.md (~2,350 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.json · canonical https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · 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: B 64.8, rank #307 of 842 · https://www.anchorterminal.com/tools/axolotl.min.md
- Fireworks AI Fine-tuning: C 59, rank #506 of 842 · https://www.anchorterminal.com/tools/fireworks-fine-tuning.min.md
- Axolotl, 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 No key needed to call it; Open source; No incidents deducted, where Fireworks AI Fine-tuning loses 4 points for them.
- Fireworks AI Fine-tuning, 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 A hosted endpoint, with nothing to install.

| Category | Axolotl | Fireworks AI Fine-tuning |
| --- | --- | --- |
| Reliability (16%) | 64 | 55 |
| Performance (10%) | pending | pending |
| Schema & documentation (13%) | 80 | 77 |
| Agent ergonomics (13%) | 60 | 75 |
| Security & auth (14%) | 52 | 65 |
| Payments & pricing (10%) | 60 | 25 |
| Task success (10%) | pending | pending |
| Maintenance & community (7%) | 88 | 82 |
| Transparency & trust (7%) | 56 | 64 |

| Fact (where they differ) | 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 |
| Licence | Apache-2.0 | Apache-2.0 (SDK) |
| 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) |