{ "data": { "a": { "slug": "amazon-bedrock-customization", "name": "Amazon Bedrock model customisation", "vendor": "Amazon Web Services", "vendorUrl": "https://aws.amazon.com/bedrock/", "kind": "http-api", "category": "fine-tuning", "summary": "Managed supervised fine-tuning, reinforcement fine-tuning and distillation of Amazon Nova, Meta Llama and selected open-weight models on Amazon Bedrock, run as asynchronous jobs through the Bedrock control-plane API, the AWS SDKs and CLI, or OpenAI-compatible endpoints.", "url": "https://www.anchorterminal.com/tools/amazon-bedrock-customization", "markdownUrl": "https://www.anchorterminal.com/tools/amazon-bedrock-customization.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/amazon-bedrock-customization.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/amazon-bedrock-customization.json", "transports": [ "http" ], "remoteUrl": "https://bedrock.{region}.amazonaws.com/model-customization-jobs", "packages": [ { "registry": "pypi", "name": "boto3" }, { "registry": "npm", "name": "@aws-sdk/client-bedrock" } ], "auth": "mixed", "authNotes": "AWS Signature Version 4 with IAM credentials or a role allowed to call `bedrock:CreateModelCustomizationJob`, plus `iam:PassRole` for a service role that Bedrock assumes to read training data from S3 and write output. Access is self-serve inside an AWS account. The OpenAI-compatible fine-tuning endpoints on `bedrock-mantle` also accept a Bedrock API key as a bearer token, short-term (up to 12 hours) or long-term, and reinforcement jobs need `lambda:InvokeFunction` on the reward function.", "pricing": "usage", "pricingNotes": "Supervised fine-tuning is billed per training token (dataset tokens times epochs). Nova Micro $1, Nova Lite $2, Nova 2 Lite $3.78 and Nova Pro $8 per 1M tokens, and Llama from $0.50 (3.2 1B) to $7.99 (3.1 70B). Reinforcement fine-tuning is $80 per training hour. Each custom model costs $1.95 a month to store. Inference is at base-model token prices where on-demand deployment is supported, otherwise Provisioned Throughput by the hour. No free tier for customisation appears on the pricing page. New AWS accounts get up to $200 in Free Tier credits, and there is no sandbox (https://aws.amazon.com/bedrock/pricing/).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the user guide, the API reference or the pricing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": 2810365, "pypiWeekly": 573748207, "asOf": "2026-10-08" }, "docsUrl": "https://docs.aws.amazon.com/bedrock/latest/userguide/custom-models.html", "llmsTxt": "https://docs.aws.amazon.com/bedrock/latest/userguide/llms.txt", "capabilities": [ "finetune.sft", "finetune.rl" ], "tags": [ "hosted", "usage-priced", "closed-source", "enterprise", "python", "typescript", "async-jobs", "llms-txt", "status-page", "soc2" ], "lastRelease": "2026-05-28", "graded": true, "anchor": { "graded": true, "score": 75.8, "grade": "BB", "agentReady": true, "rank": 39, "ranked": true, "rankOf": 842, "categoryRank": 1, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 77, "maintenance": 45, "payments": 30, "reliability": 95, "schema": 87, "security": 87, "transparency": 83 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "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.", "bestFor": "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.", "strengths": [ "`CreateModelCustomizationJob` accepts a `clientRequestToken`, so a repeated create after a timeout doesn't start a second job", "`ListModelCustomizationJobs` filters by status, name and creation time, sorts, and pages with `maxResults` up to 1,000 and `nextToken`", "Service Terms section 50.12.4 gives the customer exclusive use of a customised model and bars third-party model providers from accessing it", "Training prices are public per 1M tokens, from $0.50 for Llama 3.2 1B to $8 for Nova Pro, with storage at $1.95 a model a month", "Supervised tunes of Nova and Llama 3.3 70B deploy for on-demand, per-token inference at base-model prices, without Provisioned Throughput" ], "weaknesses": [ "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)", "No weight export was found in the reviewed documentation, and Service Terms section 50.11 forbids extracting model weights", "Setup needs an IAM service role, `iam:PassRole` and S3 buckets for input and output before the first job", "Reinforcement fine-tuning is billed at $80 a training hour and covers three models. Distillation is not available for Anthropic models", "The Bedrock SLA is dated 4 October 2023 and doesn't mention customisation jobs. No customisation launch appears in the latest 100 Bedrock What's New posts" ], "agentNotes": [ "Send a `clientRequestToken` with every `CreateModelCustomizationJob` call, and poll `GetModelCustomizationJob`. Jobs are asynchronous and can take hours", "Create the job in the Region that hosts the base model (Nova in us-east-1, Llama and Claude 3 Haiku in us-west-2), with the S3 bucket in the same Region", "Pass an IAM service role that trusts `bedrock.amazonaws.com` and can read the training data and write the output location. The caller needs `iam:PassRole`", "After a job completes, call `CreateCustomModelDeployment` and use the deployment ARN as `modelId`. Models outside the on-demand list need Provisioned Throughput", "For gpt-oss-20b and Qwen3 32B, use `/v1/fine_tuning/jobs` on `bedrock-mantle.us-west-2.api.aws` with a Bedrock API key and a Lambda grader ARN" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 75.8 } ], "editorialScores": { "ergonomics": 77, "maintenance": 45, "payments": 30, "reliability": 95, "schema": 87, "security": 87, "transparency": 77 }, "provenanceScore": 88 }, "connect": { "install": "pip install boto3 # or: npm i @aws-sdk/client-bedrock", "http": "curl -X POST \"https://bedrock.us-east-1.amazonaws.com/model-customization-jobs\" \\\n --aws-sigv4 \"aws:amz:us-east-1:bedrock\" --user \"$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY\" \\\n -H \"content-type: application/json\" \\\n -d '{\"jobName\":\"my-tune\",\"customModelName\":\"my-nova-tune\",\"roleArn\":\"arn:aws:iam::123456789012:role/BedrockCustomisationRole\",\"baseModelIdentifier\":\"amazon.nova-2-lite-v1:0:256k\",\"clientRequestToken\":\"my-tune-001\",\"trainingDataConfig\":{\"s3Uri\":\"s3://my-bucket/train.jsonl\"},\"outputDataConfig\":{\"s3Uri\":\"s3://my-bucket/output/\"}}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/amazon-bedrock-customization" }, "sameCompany": [ "agentcore-code-interpreter", "amazon-textract", "aws-end-user-messaging", "amazon-sns", "amazon-s3" ], "area": "models", "unitPrices": [ { "item": "Nova 2 Lite, supervised fine-tuning", "unit": "1m-tokens", "usd": 3.78, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Nova Pro, supervised fine-tuning", "unit": "1m-tokens", "usd": 8, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Nova Lite, supervised fine-tuning", "unit": "1m-tokens", "usd": 2, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Nova Micro, supervised fine-tuning", "unit": "1m-tokens", "usd": 1, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Llama 3.1 70B Instruct, fine-tuning", "unit": "1m-tokens", "usd": 7.99, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Llama 3.1 8B Instruct, fine-tuning", "unit": "1m-tokens", "usd": 1.49, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Llama 3.2 90B Instruct, fine-tuning", "unit": "1m-tokens", "usd": 7.9, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Llama 3.2 11B Instruct, fine-tuning", "unit": "1m-tokens", "usd": 3.5, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Llama 3.2 3B Instruct, fine-tuning", "unit": "1m-tokens", "usd": 1.1, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Llama 3.2 1B Instruct, fine-tuning", "unit": "1m-tokens", "usd": 0.5, "note": "Training tokens, counted as dataset tokens times epochs" } ], "provenance": { "legalEntity": "Amazon Web Services, Inc.", "domain": "amazonaws.com", "domainRegistered": "2005-08-18", "domainNote": "The control-plane endpoints are `bedrock.\u003cregion\u003e.amazonaws.com`. The OpenAI-compatible fine-tuning endpoints are on `bedrock-mantle.\u003cregion\u003e.api.aws`, a second AWS domain. The security.txt on aws.amazon.com passed its Expires date on 2026-09-24.", "endpointOnVendorDomain": true, "terms": "https://aws.amazon.com/service-terms/", "privacy": "https://aws.amazon.com/privacy/", "statusPage": "https://health.aws.amazon.com/health/status", "changelog": "https://docs.aws.amazon.com/bedrock/latest/userguide/doc-history.html", "securityTxt": "expired", "checked": "2026-10-08", "notes": [ "The AWS Service Terms (last updated 1 October 2026) hold the Bedrock clauses in section 50.12, including 50.12.4 on customised models. They sit under the AWS Customer Agreement at aws.amazon.com/agreement (last updated 14 August 2026), where the contracting party depends on the account country.", "The AWS Privacy Notice was last updated on 18 May 2026 and names Amazon Web Services, Inc., 410 Terry Avenue North, Seattle.", "RDAP for amazonaws.com gives a registration date of 2005-08-18, read on 8 October 2026.", "aws.amazon.com/.well-known/security.txt carries Expires 2026-09-24T16:25:03Z and was still expired on 8 October 2026. It points to vdp.aws.security and a HackerOne disclosure programme.", "The pricing page's customisation tables are filled by script. Token prices were read from the price file the page loads from b0.p.awsstatic.com." ], "score": 88 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/amazon-bedrock-customization.json", "live": { "slug": "amazon-bedrock-customization", "probe": { "target": "https://bedrock.{region}.amazonaws.com/model-customization-jobs", "method": "get", "lastAt": "2026-10-09T10:42:35.125049822Z", "lastOk": false, "lastStatus": 0, "lastMs": 0, "lastNote": "invalid character \"{\" in host name", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 33, "samples30d": 33, "days": [ { "date": "2026-10-09", "probes": 33, "ok": 0 } ], "outages": [ { "start": "2026-10-09T07:40:18.733946397Z", "end": "0001-01-01T00:00:00Z", "note": "invalid character \"{\" in host name" } ] }, "updatedAt": "2026-10-09T10:42:35.125049822Z" } }, "answer": "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 \u0026 community.", "b": { "slug": "fireworks-fine-tuning", "name": "Fireworks AI Fine-tuning", "vendor": "Fireworks AI", "vendorUrl": "https://fireworks.ai", "kind": "http-api", "category": "fine-tuning", "summary": "Managed supervised, preference and reinforcement fine-tuning for open models, with a training API for custom workflows.", "url": "https://www.anchorterminal.com/tools/fireworks-fine-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/fireworks-fine-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/fireworks-fine-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json", "repo": "https://github.com/fw-ai-external/python-sdk", "license": "Apache-2.0 (SDK)", "transports": [ "http" ], "remoteUrl": "https://api.fireworks.ai", "packages": [ { "registry": "pypi", "name": "fireworks-ai" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with an account key, read from `FIREWORKS_API_KEY` by the SDK and `firectl`. The Training API wants a training-scoped key. Resources are addressed as accounts/\u003caccount\u003e/..., and the SDK resolves the account from the key.", "pricing": "usage", "pricingNotes": "Managed training per 1M training tokens by model size. LoRA SFT $0.50 up to 16B parameters, $3 from 16.1B to 80B, $6 from 80B to 300B, $10 above; DPO is double, and full-parameter training is double LoRA. Serving a fine-tuned model costs the same as the base model. The serverless Training API is priced per model (Qwen 3.8 27B at $4.103 per 1M training tokens); dedicated training is $8 a GPU-hour for an H100 or H200, $13 for a B200, $15 for a B300 and $20 for a GB300, effective 2026-09-01. On-demand inference deployments cost $8 an hour for an H100 or H200 and $13 for a B200. New accounts get $1 of credit (https://fireworks.ai/pricing).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 7, "npmWeekly": null, "pypiWeekly": 290162, "asOf": "2026-09-30" }, "docsUrl": "https://docs.fireworks.ai/fine-tuning/fine-tuning-models", "llmsTxt": "https://docs.fireworks.ai/llms.txt", "openapi": "https://docs.fireworks.ai/merged.openapi.yaml", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.rl", "finetune.lora", "finetune.export" ], "tags": [ "hosted", "usage-priced", "card-required", "open-weights", "llms-txt", "python", "async-jobs" ], "lastRelease": "2026-10-01", "graded": true, "anchor": { "graded": true, "score": 59, "grade": "C", "agentReady": false, "rank": 506, "ranked": true, "rankOf": 842, "categoryRank": 5, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 75, "maintenance": 82, "payments": 25, "reliability": 55, "schema": 77, "security": 65, "transparency": 64 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": -4, "negativeNotes": [ "-4: on 2026-08-26 the changelog deprecated Qwen 3.5 9B and Qwen 3.6 27B from Serverless Training 'effective August 26, 2026', with no earlier entry announcing it, and told users to move existing workloads to Qwen 3.8 27B (https://docs.fireworks.ai/updates/changelog)" ], "verdict": "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.", "bestFor": "Teams that want managed SFT, DPO or RFT on large open models and may later write a custom RL loop on the same platform.", "strengths": [ "SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available", "LoRA SFT from $0.50 per 1M training tokens up to 16B parameters, with serving at base-model prices", "List endpoints take readMask, pageSize up to 200, AIP-160 filters and orderBy", "An Inference User role and revocable keys with an expiry date", "A public control-plane OpenAPI file, llms.txt and Markdown twins of every docs page" ], "weaknesses": [ "Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless", "No training without a payment method; the $1 sign-up credit buys inference only", "The status page has no training component; a 46-hour cloud provider incident in August hit dedicated deployments", "Two Serverless Training base models were deprecated with same-day effect on 2026-08-26", "Audit logs are Enterprise only and security.txt returns 404" ], "agentNotes": [ "Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute", "Check `firectl model get -a fireworks \u003cMODEL-ID\u003e` for Tunable: true before uploading a dataset", "Pass your own `supervisedFineTuningJobId` on create, so after a timeout you can GET the job by that name instead of guessing whether it started", "Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends", "Download with `firectl model download` and keep the exact base model; the adapter alone won't run" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "ergonomics": 75, "maintenance": 82, "payments": 25, "reliability": 55, "schema": 77, "security": 65, "transparency": 57 }, "provenanceScore": 71 }, "connect": { "install": "pip install fireworks-ai # add [training] for the Training API", "http": "curl https://api.fireworks.ai/v1/accounts/$FIREWORKS_ACCOUNT_ID/supervisedFineTuningJobs \\\n -H \"Authorization: Bearer $FIREWORKS_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"baseModel\":\"accounts/fireworks/models/gemma-4-31b-it\",\"dataset\":\"accounts/'$FIREWORKS_ACCOUNT_ID'/datasets/my-dataset\",\"outputModel\":\"accounts/'$FIREWORKS_ACCOUNT_ID'/models/my-tune\",\"loraRank\":16}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/fireworks-fine-tuning" }, "area": "models", "unitPrices": [ { "item": "LoRA SFT, models up to 16B", "unit": "1m-tokens", "usd": 0.5 }, { "item": "LoRA DPO, models up to 16B", "unit": "1m-tokens", "usd": 1 }, { "item": "Full-parameter SFT, models up to 16B", "unit": "1m-tokens", "usd": 1 }, { "item": "LoRA SFT, 16.1B to 80B", "unit": "1m-tokens", "usd": 3 }, { "item": "LoRA SFT, 80B to 300B", "unit": "1m-tokens", "usd": 6 }, { "item": "LoRA SFT, over 300B", "unit": "1m-tokens", "usd": 10 }, { "item": "Serverless Training API, Qwen 3.8 27B", "unit": "1m-tokens", "usd": 4.103 }, { "item": "Dedicated training, H100 or H200", "unit": "gpu-hour", "usd": 8, "note": "Effective 2026-09-01" }, { "item": "Dedicated training, B200", "unit": "gpu-hour", "usd": 13 }, { "item": "Dedicated training, B300", "unit": "gpu-hour", "usd": 15 }, { "item": "Dedicated training, GB300", "unit": "gpu-hour", "usd": 20 } ], "provenance": { "legalEntity": "Fireworks.ai, Inc.", "domain": "fireworks.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://fireworks.ai/terms-of-service", "privacy": "https://fireworks.ai/privacy-policy", "statusPage": "https://status.fireworks.ai", "changelog": "https://docs.fireworks.ai/updates/changelog", "securityTxt": "none", "checked": "2026-09-30", "notes": [ "The privacy policy, last updated 8/11/2026, names Fireworks.ai, Inc. and gives no address.", "fireworks.ai/robots.txt disallows the terms of service page to crawlers, so we read the entity from the privacy policy instead.", "fireworks.ai/.well-known/security.txt returns 404.", "The status page lists 16 serverless model components and no training component.", "The .ai registry's RDAP server refused our requests, so the registration date is blank.", "The MCP registry holds a third-party io.usefulapi/fireworks server; Fireworks doesn't publish one." ], "score": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/fireworks-fine-tuning.json", "live": { "slug": "fireworks-fine-tuning", "probe": { "target": "https://api.fireworks.ai", "method": "get", "lastAt": "2026-10-09T10:42:43.41945038Z", "lastOk": true, "lastStatus": 404, "lastMs": 39, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 30, "p95ms24h": 86, "samples24h": 260, "samples30d": 2098, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 109 }, { "date": "2026-10-02", "probes": 248, "ok": 248 }, { "date": "2026-10-03", "probes": 271, "ok": 271 }, { "date": "2026-10-04", "probes": 272, "ok": 272 }, { "date": "2026-10-05", "probes": 272, "ok": 272 }, { "date": "2026-10-06", "probes": 272, "ok": 272 }, { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 114, "ok": 114 } ] }, "vendorStatus": { "page": "https://status.fireworks.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-09T10:41:39.99848206Z" }, "versions": [ { "registry": "github", "name": "fw-ai-external/python-sdk", "version": "v1.2.20", "released": "2026-10-06", "seenAt": "2026-10-08T16:11:59.751275418Z" }, { "registry": "pypi", "name": "fireworks-ai", "version": "1.2.20", "released": "2026-10-06", "seenAt": "2026-10-08T16:11:59.542957612Z" } ], "githubStars": 10, "pypiWeekly": 281946, "securityTxt": { "url": "https://fireworks.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:39:06.393055593Z" }, "llmsTxt": { "url": "https://docs.fireworks.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:26.379688262Z" }, "domain": { "domain": "fireworks.ai", "registered": "2020-03-11", "source": "https://rdap.identitydigital.services/rdap/domain/fireworks.ai", "checkedAt": "2026-10-04T13:05:45.131398465Z" }, "pages": [ { "url": "https://docs.fireworks.ai/updates/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-10-08T18:18:43.993471365Z", "changedAt": "2026-10-08T18:18:43.993471365Z", "fingerprint": "73724d4498c3" }, { "url": "https://fireworks.ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:20:23.293613999Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "9f6b298d1e67" }, { "url": "https://fireworks.ai/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-08T18:20:25.487694157Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "7220d287a92b" }, { "url": "https://fireworks.ai/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:20:27.561123635Z", "changedAt": "0001-01-01T00:00:00Z" } ], "updatedAt": "2026-10-09T10:42:43.41945038Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Amazon Web Services", "b": "Fireworks AI", "name": "Vendor" }, { "a": "https://bedrock.{region}.amazonaws.com/model-customization-jobs", "b": "https://api.fireworks.ai", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "not published", "b": "$0.50 per 1M tokens", "name": "Price for finetune sft" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "none", "b": "Apache-2.0 (SDK)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-05-28", "b": "2026-10-01", "name": "Last release" }, { "a": "2026-10-01", "b": "couldn't be read", "name": "Terms last updated" }, { "a": "2026-05-18", "b": "no date given", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "couldn't be read", "name": "Customer content may train models" }, { "a": "yes", "b": "couldn't be read", "name": "Terms restrict automated access" }, { "a": "yes", "b": "couldn't be read", "name": "Terms restrict benchmarking" }, { "a": "yes", "b": "couldn't be read", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Arbitration or class-action waiver" }, { "a": "2.8M npm/wk, 573.7M PyPI/wk", "b": "7 stars, 290k PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "2.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "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 \u0026 community.", "question": "Which is better for AI agents, Amazon Bedrock model customisation or Fireworks AI Fine-tuning?" }, { "answer": "Amazon Bedrock model customisation takes an API key or an OAuth sign-in. Fireworks AI Fine-tuning needs an API key.", "question": "Do Amazon Bedrock model customisation and Fireworks AI Fine-tuning need an API key?" }, { "answer": "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.", "question": "Can an agent call Amazon Bedrock model customisation and Fireworks AI Fine-tuning without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 95 against 55", "Schema \u0026 documentation, 87 against 77", "Security \u0026 auth, 87 against 65", "Payments \u0026 pricing, 30 against 25", "Transparency \u0026 trust, 83 against 64" ], "also": [ "Agent-ready, a grade of BB or better", "No incidents deducted, where Fireworks AI Fine-tuning loses 4 points for them" ], "goodFor": "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.", "slug": "amazon-bedrock-customization", "watchFor": "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)" }, { "aheadOn": [ "Maintenance \u0026 community, 82 against 45" ], "also": null, "goodFor": "Teams that want managed SFT, DPO or RFT on large open models and may later write a custom RL loop on the same platform.", "slug": "fireworks-fine-tuning", "watchFor": "Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless" } ], "job": { "capability": "finetune.sft", "name": "Finetune sft" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.json", "title": "Amazon Bedrock model customisation vs Axolotl", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-azure-foundry-fine-tuning.json", "title": "Amazon Bedrock model customisation vs Microsoft Foundry fine-tuning (Azure OpenAI)", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-azure-foundry-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-nebius-token-factory-fine-tuning.json", "title": "Amazon Bedrock model customisation vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-nebius-token-factory-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-tinker.json", "title": "Amazon Bedrock model customisation vs Tinker", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-tinker" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-fine-tuning.json", "title": "Amazon Bedrock model customisation vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-unsloth.json", "title": "Amazon Bedrock model customisation vs Unsloth", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning.json", "title": "Amazon Bedrock model customisation vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.json", "title": "Axolotl vs Fireworks AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Fireworks AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.json", "title": "Fireworks AI Fine-tuning vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-tinker.json", "title": "Fireworks AI Fine-tuning vs Tinker", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-tinker" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning.json", "title": "Fireworks AI Fine-tuning vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.json", "title": "Fireworks AI Fine-tuning vs Unsloth", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.json", "title": "Fireworks AI Fine-tuning vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning" } ], "scores": [ { "amazon-bedrock-customization": 95, "by": 40, "edge": "amazon-bedrock-customization", "fireworks-fine-tuning": 55, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "amazon-bedrock-customization": 87, "by": 10, "edge": "amazon-bedrock-customization", "fireworks-fine-tuning": 77, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "amazon-bedrock-customization": 77, "by": 2, "edge": "amazon-bedrock-customization", "fireworks-fine-tuning": 75, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "amazon-bedrock-customization": 87, "by": 22, "edge": "amazon-bedrock-customization", "fireworks-fine-tuning": 65, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "amazon-bedrock-customization": 30, "by": 5, "edge": "amazon-bedrock-customization", "fireworks-fine-tuning": 25, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "amazon-bedrock-customization": 45, "by": 37, "edge": "fireworks-fine-tuning", "fireworks-fine-tuning": 82, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "amazon-bedrock-customization": 83, "by": 19, "edge": "amazon-bedrock-customization", "fireworks-fine-tuning": 64, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "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 \u0026 community. Both do finetune sft.", "verdicts": { "amazon-bedrock-customization": "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-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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning", "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning.md", "slim": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning.min.md" }, "markdown": "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 \u0026 community. Both do finetune sft.\n\n- 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\n- Fireworks AI Fine-tuning: grade C, 59/100, rank #506 of 842. Markdown https://www.anchorterminal.com/tools/fireworks-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json\n\n## Which one, for what\n\n### Amazon Bedrock model customisation (BB)\n\nGood 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.\n\nAhead on:\n- Reliability, 95 against 55\n- Schema \u0026 documentation, 87 against 77\n- Security \u0026 auth, 87 against 65\n- Payments \u0026 pricing, 30 against 25\n- Transparency \u0026 trust, 83 against 64\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- No incidents deducted, where Fireworks AI Fine-tuning loses 4 points for them\n\nWatch 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)\n\n### Fireworks AI Fine-tuning (C)\n\nGood 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.\n\nAhead on:\n- Maintenance \u0026 community, 82 against 45\n\nWatch for: Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless\n\n\n## Score by category\n\n| Category | Weight | Amazon Bedrock model customisation | Fireworks AI Fine-tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 95 | 55 | Amazon Bedrock model customisation +40 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 87 | 77 | Amazon Bedrock model customisation +10 |\n| Agent ergonomics | 13% (16.2 this run) | 77 | 75 | Amazon Bedrock model customisation +2 |\n| Security \u0026 auth | 14% (17.5 this run) | 87 | 65 | Amazon Bedrock model customisation +22 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 25 | Amazon Bedrock model customisation +5 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 45 | 82 | Fireworks AI Fine-tuning +37 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 83 | 64 | Amazon Bedrock model customisation +19 |\n| Negative events | ≤15 | 0 | -4 | |\n| **Total** | | **75.8 · BB** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Amazon Bedrock model customisation | Fireworks AI Fine-tuning |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Amazon Web Services | Fireworks AI |\n| Hosted endpoint | `https://bedrock.{region}.amazonaws.com/model-customization-jobs` | `https://api.fireworks.ai` |\n| Transports | HTTP | HTTP |\n| Auth | OAuth or key | API key |\n| Pricing | Pay per use | Pay per use |\n| Price for finetune sft | not published | $0.50 per 1M tokens |\n| x402 | no | no |\n| Licence | none | Apache-2.0 (SDK) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-05-28 | 2026-10-01 |\n| Terms last updated | 2026-10-01 | couldn't be read |\n| Privacy policy last updated | 2026-05-18 | no date given |\n| Customer content may train models | yes, with an opt-out | couldn't be read |\n| Terms restrict automated access | yes | couldn't be read |\n| Terms restrict benchmarking | yes | couldn't be read |\n| Terms or service can change without notice | yes | couldn't be read |\n| Arbitration or class-action waiver | not found in the text | couldn't be read |\n| Popularity | 2.8M npm/wk, 573.7M PyPI/wk | 7 stars, 290k PyPI/wk |\n| Agent reviews | none | 2.5/5 (2) |\n\n## Verdicts\n\n**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.\n\n**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.\n\n## Before you call either\n\n### Amazon Bedrock model customisation\n\n1. Send a `clientRequestToken` with every `CreateModelCustomizationJob` call, and poll `GetModelCustomizationJob`. Jobs are asynchronous and can take hours\n2. 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\n3. 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`\n4. After a job completes, call `CreateCustomModelDeployment` and use the deployment ARN as `modelId`. Models outside the on-demand list need Provisioned Throughput\n5. 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\n\n### Fireworks AI Fine-tuning\n\n1. Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute\n2. Check `firectl model get -a fireworks \u003cMODEL-ID\u003e` for Tunable: true before uploading a dataset\n3. Pass your own `supervisedFineTuningJobId` on create, so after a timeout you can GET the job by that name instead of guessing whether it started\n4. 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\n5. Download with `firectl model download` and keep the exact base model; the adapter alone won't run\n\n## Questions\n\n### Which is better for AI agents, Amazon Bedrock model customisation or Fireworks AI Fine-tuning?\n\nAmazon 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 \u0026 community.\n\n### Do Amazon Bedrock model customisation and Fireworks AI Fine-tuning need an API key?\n\nAmazon Bedrock model customisation takes an API key or an OAuth sign-in. Fireworks AI Fine-tuning needs an API key.\n\n### Can an agent call Amazon Bedrock model customisation and Fireworks AI Fine-tuning without installing anything?\n\nYes. 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.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"amazon-bedrock-customization\", \"b\": \"fireworks-fine-tuning\"}`. From a terminal: `anchor compare amazon-bedrock-customization fireworks-fine-tuning`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/amazon-bedrock-customization.json and https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json\n\n## Other comparisons with Amazon Bedrock model customisation or Fireworks AI Fine-tuning\n\n- [Amazon Bedrock model customisation vs Axolotl](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.md)\n- [Amazon Bedrock model customisation vs Microsoft Foundry fine-tuning (Azure OpenAI)](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-azure-foundry-fine-tuning.md)\n- [Amazon Bedrock model customisation vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-nebius-token-factory-fine-tuning.md)\n- [Amazon Bedrock model customisation vs Tinker](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-tinker.md)\n- [Amazon Bedrock model customisation vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-fine-tuning.md)\n- [Amazon Bedrock model customisation vs Unsloth](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-unsloth.md)\n- [Amazon Bedrock model customisation vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning.md)\n- [Axolotl vs Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning.md)\n- [Fireworks AI Fine-tuning vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.md)\n- [Fireworks AI Fine-tuning vs Tinker](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-tinker.md)\n- [Fireworks AI Fine-tuning vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning.md)\n- [Fireworks AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.md)\n- [Fireworks AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Amazon Bedrock model customisation vs Fireworks AI Fine-tuning", "url": "" } ], "description": "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 \u0026 community. Both do finetune sft. Category scores, facts, verdicts and agent…", "facts": [ "Amazon Bedrock model customisation BB 75.8", "Fireworks AI Fine-tuning C 59", "scores" ], "h1": "Amazon Bedrock model customisation vs Fireworks AI Fine-tuning", "image": "https://www.anchorterminal.com/assets/og/compare-amazon-bedrock-customization-vs-fireworks-fine-tuning.png", "path": "/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning", "published": "2026-10-01", "section": "tools", "title": "Amazon Bedrock model customisation vs Fireworks AI Fine-tuning", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning" }, "tokens": { "markdown": 2600, "slim": 780 }, "version": 1 }