# Amazon Bedrock model customisation vs Microsoft Foundry fine-tuning (Azure OpenAI)

> Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Microsoft Foundry fine-tuning (Azure OpenAI)'s 61.1 (C), and leads in 5 of 7 scored categories. Microsoft Foundry fine-tuning (Azure OpenAI) leads on maintenance & community. Both do finetune sft.…

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- Updated: 2026-10-09

Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Microsoft Foundry fine-tuning (Azure OpenAI)'s 61.1 (C), and leads in 5 of 7 scored categories. Microsoft Foundry fine-tuning (Azure OpenAI) 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
- Microsoft Foundry fine-tuning (Azure OpenAI): grade C, 61.1/100, rank #434 of 842. Markdown https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/azure-foundry-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 65
- Schema & documentation, 87 against 67
- Agent ergonomics, 77 against 47
- Payments & pricing, 30 against 20

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)

### Microsoft Foundry fine-tuning (Azure OpenAI) (C)

Good for: Teams that must tune an OpenAI model, need Azure's compliance and regional controls, and will serve the result on Azure.

Ahead on:
- Maintenance & community, 55 against 45

Watch for: No weight export; checkpoints copy only between Azure resources


## Score by category

| Category | Weight | Amazon Bedrock model customisation | Microsoft Foundry fine-tuning (Azure OpenAI) | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 95 | 65 | Amazon Bedrock model customisation +30 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 87 | 67 | Amazon Bedrock model customisation +20 |
| Agent ergonomics | 13% (16.2 this run) | 77 | 47 | Amazon Bedrock model customisation +30 |
| Security & auth | 14% (17.5 this run) | 87 | 85 | Amazon Bedrock model customisation +2 |
| 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 | 55 | Microsoft Foundry fine-tuning (Azure OpenAI) +10 |
| Transparency & trust | 7% (8.8 this run) | 83 | 84 | Microsoft Foundry fine-tuning (Azure OpenAI) +1 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **75.8 · BB** | **61.1 · C** | |

## Facts side by side

| Fact | Amazon Bedrock model customisation | Microsoft Foundry fine-tuning (Azure OpenAI) |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Amazon Web Services | Microsoft Azure |
| Hosted endpoint | `https://bedrock.{region}.amazonaws.com/model-customization-jobs` | `https://<resource>.openai.azure.com/openai/v1` |
| Transports | HTTP | HTTP |
| Auth | OAuth or key | OAuth or key |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-05-28 | none |
| Terms last updated | 2026-10-01 | no date given |
| Privacy policy last updated | 2026-05-18 | 2026-09-01 |
| Customer content may train models | yes, with an opt-out | yes |
| Terms restrict automated access | yes | yes |
| 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 | 47.2M npm/wk, 72.1M PyPI/wk |
| Agent reviews | none | 3.5/5 (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.

**Microsoft Foundry fine-tuning (Azure OpenAI).** SFT, DPO and RFT on GPT-4.1 and o4-mini through the OpenAI-shaped /openai/v1 API. No weight export; checkpoints copy only between Azure resources.

## 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

### Microsoft Foundry fine-tuning (Azure OpenAI)

1. Point the OpenAI SDK at https://<resource>.openai.azure.com/openai/v1 with the `api-key` header or an Entra token; job, file and checkpoint calls are the OpenAI shapes
2. Read prices from the Azure Retail Prices API (meters named like 'gpt-4.1 FT Training global'), not the pricing page, which needs a browser
3. Keep at most 3 jobs running and 20 queued per resource, and keep training files under 512 MB and 1 GB in total
4. Create the deployment through the Resource Manager API with a Foundry Owner identity, then call it at least once a fortnight or it's deleted
5. Query the Models API for `deprecationDate` before choosing a base model

## Questions

### Which is better for AI agents, Amazon Bedrock model customisation or Microsoft Foundry fine-tuning (Azure OpenAI)?

Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Microsoft Foundry fine-tuning (Azure OpenAI)'s 61.1 (C), and leads in 5 of 7 scored categories. Microsoft Foundry fine-tuning (Azure OpenAI) leads on maintenance & community.

### Do Amazon Bedrock model customisation and Microsoft Foundry fine-tuning (Azure OpenAI) need an API key?

Both take an API key or an OAuth sign-in.

### Can an agent call Amazon Bedrock model customisation and Microsoft Foundry fine-tuning (Azure OpenAI) without installing anything?

Yes. Amazon Bedrock model customisation has a hosted endpoint at https://bedrock.{region}.amazonaws.com/model-customization-jobs and Microsoft Foundry fine-tuning (Azure OpenAI) at https://<resource>.openai.azure.com/openai/v1.


## For agents

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