# Best fine-tuning services for AI models (slim)

> Amazon Bedrock model customisation (BB), Axolotl (B) and Vertex AI Gemini tuning (B) lead the 9 ranked fine-tuning services for AI models. Picks by need, strengths, weaknesses and prices from the Anchor benchmark.

- Full: https://www.anchorterminal.com/best/fine-tuning/index.md (~5,300 tokens) · this version ~1,330 tokens · JSON https://www.anchorterminal.com/best/fine-tuning/index.json · canonical https://www.anchorterminal.com/best/fine-tuning/
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · Updated: 2026-10-08

All 9 ranked fine-tuning services for AI models on the Anchor benchmark, with a pick for each need and where each one falls short. Scores come from public evidence, re-checked as vendors change.

- Ranked: 9 · agent-ready (BB or better): 1 · accept x402: 0 · hosted endpoints: 6
- Full ranked table: https://www.anchorterminal.com/categories/fine-tuning.md
- Head-to-head comparisons: https://www.anchorterminal.com/compare/fine-tuning/index.md (36)
- Methodology: https://www.anchorterminal.com/benchmark/index.md

## The shortlist

| # | Tool | Grade | Score | Best for | Price | Where |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | [Amazon Bedrock model customisation](https://www.anchorterminal.com/tools/amazon-bedrock-customization.md) | BB | 75.8 | Teams already on AWS that want to tune Amazon Nova or Llama models, or run reinforcement fine-tuning with a Lambda reward function, and serve the result inside Bedrock. | Pay per use | hosted |
| 2 | [Axolotl](https://www.anchorterminal.com/tools/axolotl.md) | B | 64.8 | 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. | Free · OSS | library |
| 3 | [Vertex AI Gemini tuning](https://www.anchorterminal.com/tools/vertex-ai-tuning.md) | B | 64.2 | Teams already on Google Cloud who need to tune Gemini itself, especially with RL, and will serve it there. | Pay per use | hosted |
| 4 | [Microsoft Foundry fine-tuning (Azure OpenAI)](https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.md) | C | 61.1 | Teams that must tune an OpenAI model, need Azure's compliance and regional controls, and will serve the result on Azure. | Pay per use | hosted |
| 5 | [Fireworks AI Fine-tuning](https://www.anchorterminal.com/tools/fireworks-fine-tuning.md) | C | 59 | Teams that want managed SFT, DPO or RFT on large open models and may later write a custom RL loop on the same platform. | Pay per use | hosted |
| 6 | [Together AI Fine-tuning](https://www.anchorterminal.com/tools/together-fine-tuning.md) | C | 54.7 | Teams that want to tune a large open model, possibly with full fine-tuning, and take the weights away. | Pay per use | hosted |
| 7 | [Unsloth](https://www.anchorterminal.com/tools/unsloth.md) | D | 51.5 | One person or a small team tuning an open model on their own GPU and keeping the weights. | Free · OSS | library |
| 8 | [Tinker](https://www.anchorterminal.com/tools/tinker.md) | D | 51 | Researchers and teams writing custom post-training loops, especially RL, who want per-token billing and the weights at the end. | Pay per use | local |
| 9 | [Nebius Token Factory fine-tuning](https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.md) | D | 47.7 | 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. | Pay per use | hosted |

## Picks by need

- Highest score overall: [Amazon Bedrock model customisation](https://www.anchorterminal.com/tools/amazon-bedrock-customization.md), BB, 75.8/100 on the benchmark. Also [Axolotl](https://www.anchorterminal.com/tools/axolotl.md), B, 64.8/100.
- Maintenance & community: [Axolotl](https://www.anchorterminal.com/tools/axolotl.md), 88/100 on maintenance & community, against 45 for the overall leader.
- Transparency & trust: [Vertex AI Gemini tuning](https://www.anchorterminal.com/tools/vertex-ai-tuning.md), 88/100 on transparency & trust, against 83 for the overall leader.
- Self-hosting under an open licence: [Axolotl](https://www.anchorterminal.com/tools/axolotl.md), self-hosted, Apache-2 licence. Also [Unsloth](https://www.anchorterminal.com/tools/unsloth.md), self-hosted, Apache-2 licence.

## How to choose

- Weight export and licence: Check whether the tuned weights can be downloaded and under what licence, because an agent that must run offline or on your own hardware needs them.
- Tunable base models and methods: Check which base models can be tuned and whether your chosen method is supported for each model, since method support can differ between them.
- Training price by method: Check the training price per token for supervised and reinforcement runs separately, because the reinforcement price can differ from the supervised price on the same data.
- Serving and idle costs: Check the serving price, idle deletion and checkpoint storage fees, because an agent that calls a tuned model only occasionally can end up paying for idle time.

- How the benchmark tests this category: The same small dataset used to tune a comparable open model on each service, then served. We check the job flow, how long training takes, whether the weights can leave, and the training and serving cost.

Each listing's verdict, strengths and weaknesses: https://www.anchorterminal.com/best/fine-tuning/index.md