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Head to head · Finetune sft · October 2026 research run

Amazon Bedrock model customisation vs Fireworks AI Fine-tuning

Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Fireworks AI Fine-tuning's 59 (C), and leads in 6 of 7 scored categories. Fireworks 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 Fireworks AI Fine-tuningFireworks AI · HTTP API C59/100#506 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 77
  • Security & auth, 87 against 65
  • Payments & pricing, 30 against 25
  • Transparency & trust, 83 against 64

Also in its favour

  • Agent-ready, a grade of BB or better
  • No incidents deducted, where Fireworks AI Fine-tuning loses 4 points for them

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)

Fireworks AI Fine-tuning C

Good for Teams that want managed SFT, DPO or RFT on large open models and may later write a custom RL loop on the same platform.

Ahead on

  • Maintenance & community, 82 against 45

Watch for

Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless

Score by category

CategoryWeight this runAmazon Bedrock model customisationFireworks AI Fine-tuningEdge
Reliability16%209555Amazon Bedrock model customisation +40
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.28777Amazon Bedrock model customisation +10
Agent ergonomics13%16.27775Amazon Bedrock model customisation +2
Security & auth14%17.58765Amazon Bedrock model customisation +22
Payments & pricing10%12.53025Amazon Bedrock model customisation +5
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.84582Fireworks AI Fine-tuning +37
Transparency & trust7%8.88364Amazon Bedrock model customisation +19
Negative events≤150-4
Total75.8 · BB59 · C

Facts side by side

FactAmazon Bedrock model customisationFireworks AI Fine-tuning
KindHTTP APIHTTP API
VendorAmazon Web ServicesFireworks AI
Hosted endpointhttps://bedrock.{region}.amazonaws.com/model-customization-jobshttps://api.fireworks.ai
TransportsHTTPHTTP
AuthOAuth or keyAPI key
PricingPay per usePay per use
Price for finetune sftnot published$0.50 per 1M tokens
x402nono
LicencenoneApache-2.0 (SDK)
Read-only variant documentednono
llms.txtyesyes
Last release2026-05-282026-10-01
Terms last updated2026-10-01couldn't be read
Privacy policy last updated2026-05-18no date given
Customer content may train modelsyes, with an opt-outcouldn't be read
Terms restrict automated accessyescouldn't be read
Terms restrict benchmarkingyescouldn't be read
Terms or service can change without noticeyescouldn't be read
Arbitration or class-action waivernot found in the textcouldn't be read
Popularity2.8M npm/wk, 573.7M PyPI/wk7 stars, 290k PyPI/wk
Agent reviewsnone2.5/5 (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.

Fireworks AI Fine-tuning

SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless.

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

Fireworks AI Fine-tuning

  1. Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute
  2. Check firectl model get -a fireworks <MODEL-ID> for Tunable: true before uploading a dataset
  3. Pass your own supervisedFineTuningJobId on create, so after a timeout you can GET the job by that name instead of guessing whether it started
  4. Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends
  5. Download with firectl model download and keep the exact base model; the adapter alone won't run

Questions

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

Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Fireworks AI Fine-tuning's 59 (C), and leads in 6 of 7 scored categories. Fireworks AI Fine-tuning leads on maintenance & community.

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

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

Can an agent call Amazon Bedrock model customisation and Fireworks 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 Fireworks AI Fine-tuning at https://api.fireworks.ai.

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

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