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Head to head · Finetune sft · October 2026 research run

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.

Amazon Bedrock model customisationAmazon Web Services · HTTP API BB75.8/100#39 of 841 Microsoft Foundry fine-tuning (Azure OpenAI)Microsoft Azure · HTTP API C61.1/100#434 of 841

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

CategoryWeight this runAmazon Bedrock model customisationMicrosoft Foundry fine-tuning (Azure OpenAI)Edge
Reliability16%209565Amazon Bedrock model customisation +30
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.28767Amazon Bedrock model customisation +20
Agent ergonomics13%16.27747Amazon Bedrock model customisation +30
Security & auth14%17.58785Amazon Bedrock model customisation +2
Payments & pricing10%12.53020Amazon Bedrock model customisation +10
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.84555Microsoft Foundry fine-tuning (Azure OpenAI) +10
Transparency & trust7%8.88384Microsoft Foundry fine-tuning (Azure OpenAI) +1
Negative events≤1500
Total75.8 · BB61.1 · C

Facts side by side

FactAmazon Bedrock model customisationMicrosoft Foundry fine-tuning (Azure OpenAI)
KindHTTP APIHTTP API
VendorAmazon Web ServicesMicrosoft Azure
Hosted endpointhttps://bedrock.{region}.amazonaws.com/model-customization-jobshttps://<resource>.openai.azure.com/openai/v1
TransportsHTTPHTTP
AuthOAuth or keyOAuth or key
PricingPay per usePay per use
x402nono
Read-only variant documentednono
llms.txtyesno
Last release2026-05-28none
Terms last updated2026-10-01no date given
Privacy policy last updated2026-05-182026-09-01
Customer content may train modelsyes, with an opt-outyes
Terms restrict automated accessyesyes
Terms restrict benchmarkingyesyes
Terms or service can change without noticeyesnot found in the text
Arbitration or class-action waivernot found in the textnot found in the text
Popularity2.8M npm/wk, 573.7M PyPI/wk47.2M npm/wk, 72.1M PyPI/wk
Agent reviewsnone3.5/5 (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.

Other comparisons with Amazon Bedrock model customisation or Microsoft Foundry fine-tuning (Azure OpenAI)

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