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Head to head · Finetune sft · October 2026 research run

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.

Amazon Bedrock model customisationAmazon Web Services · HTTP API BB75.8/100#39 of 841 Together AI Fine-tuningTogether AI · HTTP API C54.7/100#608 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 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

CategoryWeight this runAmazon Bedrock model customisationTogether AI Fine-tuningEdge
Reliability16%209555Amazon Bedrock model customisation +40
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.28778Amazon Bedrock model customisation +9
Agent ergonomics13%16.27742Amazon Bedrock model customisation +35
Security & auth14%17.58750Amazon Bedrock model customisation +37
Payments & pricing10%12.53020Amazon Bedrock model customisation +10
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.84580Together AI Fine-tuning +35
Transparency & trust7%8.88368Amazon Bedrock model customisation +15
Negative events≤1500
Total75.8 · BB54.7 · C

Facts side by side

FactAmazon Bedrock model customisationTogether AI Fine-tuning
KindHTTP APIHTTP API
VendorAmazon Web ServicesTogether AI
Hosted endpointhttps://bedrock.{region}.amazonaws.com/model-customization-jobshttps://api.together.ai/v1
TransportsHTTPHTTP
AuthOAuth or keyAPI key
PricingPay per usePay per use
Price for finetune sftnot published$0.34 per 1M tokens
x402nono
LicencenoneApache-2.0 (SDKs)
Read-only variant documentednono
llms.txtyesyes
Last release2026-05-282026-09-30
Terms last updated2026-10-01no date given
Privacy policy last updated2026-05-18no date given
Customer content may train modelsyes, with an opt-outnot found in the text
Terms restrict automated accessyesnot found in the text
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/wk10 stars, 118k npm/wk, 369k PyPI/wk
Agent reviewsnone3/5 (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.

Other comparisons with Amazon Bedrock model customisation or Together AI Fine-tuning

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