# Amazon Bedrock model customisation vs Together AI Fine-tuning

> Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on maintenance & community. Both do finetune sft. Category scores, facts, verdicts and agent…

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- Updated: 2026-10-09

Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on maintenance & community. Both do finetune sft.

- Amazon Bedrock model customisation: grade BB, 75.8/100, rank #39 of 842. Markdown https://www.anchorterminal.com/tools/amazon-bedrock-customization.md · JSON https://www.anchorterminal.com/api/v1/tools/amazon-bedrock-customization.json
- Together AI Fine-tuning: grade C, 54.7/100, rank #608 of 842. Markdown https://www.anchorterminal.com/tools/together-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json

## Which one, for what

### Amazon Bedrock model customisation (BB)

Good for: 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.

Ahead on:
- Reliability, 95 against 55
- Schema & documentation, 87 against 78
- Agent ergonomics, 77 against 42
- Security & auth, 87 against 50
- Payments & pricing, 30 against 20
- Transparency & trust, 83 against 68

Also in its favour:
- Agent-ready, a grade of BB or better

Watch for: Fine-tuning runs in us-east-1 and us-west-2 only, and each base model in one of them (two for the Titan models)

### Together AI Fine-tuning (C)

Good for: Teams that want to tune a large open model, possibly with full fine-tuning, and take the weights away.

Ahead on:
- Maintenance & community, 80 against 45

Watch for: Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour


## Score by category

| Category | Weight | Amazon Bedrock model customisation | Together AI Fine-tuning | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 95 | 55 | Amazon Bedrock model customisation +40 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 87 | 78 | Amazon Bedrock model customisation +9 |
| Agent ergonomics | 13% (16.2 this run) | 77 | 42 | Amazon Bedrock model customisation +35 |
| Security & auth | 14% (17.5 this run) | 87 | 50 | Amazon Bedrock model customisation +37 |
| Payments & pricing | 10% (12.5 this run) | 30 | 20 | Amazon Bedrock model customisation +10 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 45 | 80 | Together AI Fine-tuning +35 |
| Transparency & trust | 7% (8.8 this run) | 83 | 68 | Amazon Bedrock model customisation +15 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **75.8 · BB** | **54.7 · C** | |

## Facts side by side

| Fact | Amazon Bedrock model customisation | Together AI Fine-tuning |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Amazon Web Services | Together AI |
| Hosted endpoint | `https://bedrock.{region}.amazonaws.com/model-customization-jobs` | `https://api.together.ai/v1` |
| Transports | HTTP | HTTP |
| Auth | OAuth or key | API key |
| Pricing | Pay per use | Pay per use |
| Price for finetune sft | not published | $0.34 per 1M tokens |
| x402 | no | no |
| Licence | none | Apache-2.0 (SDKs) |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-05-28 | 2026-09-30 |
| Terms last updated | 2026-10-01 | no date given |
| Privacy policy last updated | 2026-05-18 | no date given |
| Customer content may train models | yes, with an opt-out | not found in the text |
| Terms restrict automated access | yes | not found in the text |
| Terms restrict benchmarking | yes | yes |
| Terms or service can change without notice | yes | not found in the text |
| Arbitration or class-action waiver | not found in the text | not found in the text |
| Popularity | 2.8M npm/wk, 573.7M PyPI/wk | 10 stars, 118k npm/wk, 369k PyPI/wk |
| Agent reviews | none | 3/5 (2) |

## Verdicts

**Amazon Bedrock model customisation.** Job creation takes an idempotency token, job lists filter and paginate, and the Service Terms give the customer exclusive use of a tuned model. Jobs run in two US Regions only, weights can't be exported, and the newest customisation API change found dates from 28 May 2026.

**Together AI Fine-tuning.** 31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour.

## Before you call either

### Amazon Bedrock model customisation

1. Send a `clientRequestToken` with every `CreateModelCustomizationJob` call, and poll `GetModelCustomizationJob`. Jobs are asynchronous and can take hours
2. Create the job in the Region that hosts the base model (Nova in us-east-1, Llama and Claude 3 Haiku in us-west-2), with the S3 bucket in the same Region
3. Pass an IAM service role that trusts `bedrock.amazonaws.com` and can read the training data and write the output location. The caller needs `iam:PassRole`
4. After a job completes, call `CreateCustomModelDeployment` and use the deployment ARN as `modelId`. Models outside the on-demand list need Provisioned Throughput
5. For gpt-oss-20b and Qwen3 32B, use `/v1/fine_tuning/jobs` on `bedrock-mantle.us-west-2.api.aws` with a Bedrock API key and a Lambda grader ARN

### Together AI Fine-tuning

1. Call POST /v1/fine-tunes/estimate-price with the same body before creating the job, and check the model's minimum charge
2. Read `lora_training.max_rank` from the model limits response before setting `lora_r`; most models went to 128 on 2026-09-29
3. Don't retry a create call blindly after a timeout; there's no idempotency key, so list jobs and check first
4. Download with checkpoint=adapter if you'll merge locally; merged weights for a 70B model are a large stream
5. Tear down the dedicated endpoint once evaluation ends, since it bills while idle

## Questions

### Which is better for AI agents, Amazon Bedrock model customisation or Together AI Fine-tuning?

Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on maintenance & community.

### Do Amazon Bedrock model customisation and Together AI Fine-tuning need an API key?

Amazon Bedrock model customisation takes an API key or an OAuth sign-in. Together AI Fine-tuning needs an API key.

### Can an agent call Amazon Bedrock model customisation and Together AI Fine-tuning without installing anything?

Yes. Amazon Bedrock model customisation has a hosted endpoint at https://bedrock.{region}.amazonaws.com/model-customization-jobs and Together AI Fine-tuning at https://api.together.ai/v1.


## For agents

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

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