{ "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 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 \u0026 community.", "b": { "slug": "azure-foundry-fine-tuning", "name": "Microsoft Foundry fine-tuning (Azure OpenAI)", "vendor": "Microsoft Azure", "vendorUrl": "https://azure.microsoft.com", "kind": "http-api", "category": "fine-tuning", "summary": "Azure's managed service for supervised, preference and reinforcement fine-tuning of supported OpenAI and open-weight models.", "url": "https://www.anchorterminal.com/tools/azure-foundry-fine-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/azure-foundry-fine-tuning.json", "transports": [ "http" ], "remoteUrl": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "packages": [ { "registry": "pypi", "name": "openai" }, { "registry": "npm", "name": "openai" } ], "auth": "mixed", "authNotes": "`api-key` header with a resource key, or a Microsoft Entra ID bearer token. Training a model needs the Foundry User role and deploying it needs Foundry Owner (renamed from Azure AI User and Azure AI Owner). Deployments are created through the Azure Resource Manager API at management.azure.com, a second credential.", "pricing": "usage", "pricingNotes": "SFT and DPO bill training tokens x epochs at a per-model rate. The Azure Retail Prices API lists, per 1M training tokens, gpt-4.1 at $25 global and $30.25 regional, gpt-4.1-mini at $5 and $6.05, and gpt-4.1-nano at $1.50 and $1.815 (regional is 21 per cent above global). RFT bills training hours plus grader tokens; the cost guide's example uses $100 an hour for o4-mini and jobs pause at $5,000. The developer tier is 50 per cent below global on pre-emptible capacity, without data residency. A fine-tuned model on a Standard or Global Standard deployment costs $1.70 an hour to host plus per-token inference (gpt-4.1-ft $2 input and $8 output per 1M, global); developer deployments have no hosting fee and are deleted after 24 hours (https://prices.azure.com/api/retail/prices, https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/fine-tuning-cost-management).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": 47155661, "pypiWeekly": 72103251, "asOf": "2026-09-30" }, "docsUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/fine-tuning", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.rl", "finetune.lora" ], "tags": [ "hosted", "usage-priced", "closed-source", "card-required", "enterprise", "eu", "python", "typescript", "async-jobs" ], "graded": true, "anchor": { "graded": true, "score": 61.1, "grade": "C", "agentReady": false, "rank": 434, "ranked": true, "rankOf": 842, "categoryRank": 4, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 47, "maintenance": 55, "payments": 20, "reliability": 65, "schema": 67, "security": 85, "transparency": 84 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "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.", "bestFor": "Teams that must tune an OpenAI model, need Azure's compliance and regional controls, and will serve the result on Azure.", "strengths": [ "SFT, DPO and RFT on GPT-4.1 and o4-mini through the OpenAI-shaped /openai/v1 API", "Retirement policy with 60 days' notice and published training and deployment retirement dates per tunable model", "Entra ID with RBAC, Azure Monitor logs and an activity log for every customer", "Training files and tuned models stay in the resource's geography, are deletable and exclusive to the customer", "Fine-tuning limits published with numbers, from 3 concurrent jobs to 2 billion tokens per job" ], "weaknesses": [ "No weight export; checkpoints copy only between Azure resources", "$1.70 an hour hosting on Standard deployments, and deletion after 15 idle days", "GPT-4.1 training at $25 per 1M tokens globally, and no free tier without a card", "Deployment goes through management.azure.com with a separate credential and the Foundry Owner role", "The Azure OpenAI 'what's new' page hasn't had a dated section since May 2026" ], "agentNotes": [ "Point the OpenAI SDK at https://\u003cresource\u003e.openai.azure.com/openai/v1 with the `api-key` header or an Entra token; job, file and checkpoint calls are the OpenAI shapes", "Read prices from the Azure Retail Prices API (meters named like 'gpt-4.1 FT Training global'), not the pricing page, which needs a browser", "Keep at most 3 jobs running and 20 queued per resource, and keep training files under 512 MB and 1 GB in total", "Create the deployment through the Resource Manager API with a Foundry Owner identity, then call it at least once a fortnight or it's deleted", "Query the Models API for `deprecationDate` before choosing a base model" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.1 } ], "editorialScores": { "ergonomics": 47, "maintenance": 55, "payments": 20, "reliability": 65, "schema": 67, "security": 85, "transparency": 81 }, "provenanceScore": 86 }, "connect": { "install": "pip install openai # or: npm i openai", "http": "curl \"https://$AZURE_OPENAI_RESOURCE.openai.azure.com/openai/v1/fine_tuning/jobs\" \\\n -H \"api-key: $AZURE_OPENAI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"gpt-4.1-2025-04-14\",\"training_file\":\"file-abc123\",\"seed\":105}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/azure-foundry-fine-tuning" }, "sameCompany": [ "azure-ai-content-safety", "azure-speech-to-text", "azure-text-to-speech", "microsoft-agent-framework", "microsoft-execution-containers", "microsoft-entra-agent-id", "azure-key-vault", "azure-document-intelligence", "azure-devops-mcp", "microsoft-learn-mcp", "playwright-mcp", "azure-mcp", "azure-maps", "azure-translator", "microsoft-graph-calendar", "azure-blob-storage", "onedrive-sharepoint", "microsoft-teams", "dynamics-365-sales", "power-automate", "foundry-local", "microsoft-advertising-api", "microsoft-excel-graph", "outlook-mail-graph" ], "area": "models", "provenance": { "legalEntity": "Microsoft Corporation", "domain": "microsoft.com", "domainRegistered": "1991-05-02", "domainNote": "Endpoints are on openai.azure.com and management.azure.com. microsoft.com publishes a security.txt, but it passed its Expires date on 2026-09-23.", "endpointOnVendorDomain": true, "terms": "https://www.microsoft.com/licensing/terms/product/ForOnlineServices/all", "privacy": "https://privacy.microsoft.com/en-us/privacystatement", "statusPage": "https://azure.status.microsoft/en-us/status", "changelog": "https://learn.microsoft.com/en-us/azure/ai-foundry/whats-new-foundry", "securityTxt": "expired", "checked": "2026-09-30", "notes": [ "Entity, domain, privacy statement, status page and security.txt are the same as the azure-speech-to-text listing; the terms link here is the Product Terms for online services, which hold the generative AI clause.", "The Azure status page lists Azure OpenAI Service, Foundry Agent Service and Foundry Models as components.", "The npm and PyPI figures are for the openai package as a whole, which Azure customers share with OpenAI's own API; there's no Azure-only SDK to count.", "Docs facts were read from the MicrosoftDocs/azure-ai-docs repository (articles/foundry/openai, updated 2026-09-30) because Learn pages are long; the live how-to page confirms the model table and roles." ], "score": 86 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.json", "live": { "slug": "azure-foundry-fine-tuning", "probe": { "target": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "method": "get", "lastAt": "2026-10-09T10:42:36.957023016Z", "lastOk": false, "lastStatus": 0, "lastMs": 0, "lastNote": "DNS lookup failed", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 260, "samples30d": 2098, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 0 }, { "date": "2026-10-02", "probes": 248, "ok": 0 }, { "date": "2026-10-03", "probes": 271, "ok": 0 }, { "date": "2026-10-04", "probes": 272, "ok": 0 }, { "date": "2026-10-05", "probes": 272, "ok": 0 }, { "date": "2026-10-06", "probes": 272, "ok": 0 }, { "date": "2026-10-07", "probes": 272, "ok": 0 }, { "date": "2026-10-08", "probes": 268, "ok": 0 }, { "date": "2026-10-09", "probes": 114, "ok": 0 } ] }, "vendorStatus": { "page": "https://azure.status.microsoft/en-us/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-08T10:02:53.28378098Z" }, "versions": [ { "registry": "npm", "name": "openai", "version": "7.30.1", "seenAt": "2026-10-08T16:00:58.639202123Z" }, { "registry": "pypi", "name": "openai", "version": "3.26.1", "released": "2026-10-08", "seenAt": "2026-10-08T16:00:55.524523612Z" } ], "npmWeekly": 50858207, "pypiWeekly": 74231726, "securityTxt": { "url": "https://microsoft.com/.well-known/security.txt", "state": "expired", "expires": "2026-09-23T16:00:00.000Z", "checkedAt": "2026-10-08T15:39:08.216544687Z" }, "domain": { "domain": "microsoft.com", "registered": "1991-05-02", "source": "https://rdap.verisign.com/com/v1/domain/microsoft.com", "checkedAt": "2026-10-04T13:04:13.488857536Z" }, "pages": [ { "url": "https://learn.microsoft.com/en-us/azure/ai-foundry/whats-new-foundry", "kind": "changelog", "status": 304, "checkedAt": "2026-10-08T18:21:22.172079422Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "237edf8365ef" }, { "url": "https://prices.azure.com/api/retail/prices", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:23:18.650260122Z", "changedAt": "0001-01-01T00:00:00Z" }, { "url": "https://www.microsoft.com/licensing/terms/product/ForOnlineServices/all", "kind": "terms", "status": 502, "checkedAt": "2026-10-07T18:13:00.414399508Z", "changedAt": "0001-01-01T00:00:00Z" } ], "updatedAt": "2026-10-09T10:42:36.957023016Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Amazon Web Services", "b": "Microsoft Azure", "name": "Vendor" }, { "a": "https://bedrock.{region}.amazonaws.com/model-customization-jobs", "b": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "OAuth or key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-05-28", "b": "none", "name": "Last release" }, { "a": "2026-10-01", "b": "no date given", "name": "Terms last updated" }, { "a": "2026-05-18", "b": "2026-09-01", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "yes", "name": "Customer content may train models" }, { "a": "yes", "b": "yes", "name": "Terms restrict automated access" }, { "a": "yes", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "yes", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "2.8M npm/wk, 573.7M PyPI/wk", "b": "47.2M npm/wk, 72.1M PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "3.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "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 \u0026 community.", "question": "Which is better for AI agents, Amazon Bedrock model customisation or Microsoft Foundry fine-tuning (Azure OpenAI)?" }, { "answer": "Both take an API key or an OAuth sign-in.", "question": "Do Amazon Bedrock model customisation and Microsoft Foundry fine-tuning (Azure OpenAI) need an API key?" }, { "answer": "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://\u003cresource\u003e.openai.azure.com/openai/v1.", "question": "Can an agent call Amazon Bedrock model customisation and Microsoft Foundry fine-tuning (Azure OpenAI) without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 95 against 65", "Schema \u0026 documentation, 87 against 67", "Agent ergonomics, 77 against 47", "Payments \u0026 pricing, 30 against 20" ], "also": [ "Agent-ready, a grade of BB or better" ], "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, 55 against 45" ], "also": null, "goodFor": "Teams that must tune an OpenAI model, need Azure's compliance and regional controls, and will serve the result on Azure.", "slug": "azure-foundry-fine-tuning", "watchFor": "No weight export; checkpoints copy only between Azure resources" } ], "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-fireworks-fine-tuning.json", "title": "Amazon Bedrock model customisation vs Fireworks AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-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-azure-foundry-fine-tuning.json", "title": "Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI)", "url": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-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/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-tinker.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Tinker", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-tinker" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-unsloth.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Unsloth", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-vertex-ai-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-vertex-ai-tuning" } ], "scores": [ { "amazon-bedrock-customization": 95, "azure-foundry-fine-tuning": 65, "by": 30, "edge": "amazon-bedrock-customization", "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "amazon-bedrock-customization": 87, "azure-foundry-fine-tuning": 67, "by": 20, "edge": "amazon-bedrock-customization", "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "amazon-bedrock-customization": 77, "azure-foundry-fine-tuning": 47, "by": 30, "edge": "amazon-bedrock-customization", "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "amazon-bedrock-customization": 87, "azure-foundry-fine-tuning": 85, "by": 2, "edge": "amazon-bedrock-customization", "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "amazon-bedrock-customization": 30, "azure-foundry-fine-tuning": 20, "by": 10, "edge": "amazon-bedrock-customization", "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "amazon-bedrock-customization": 45, "azure-foundry-fine-tuning": 55, "by": 10, "edge": "azure-foundry-fine-tuning", "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "amazon-bedrock-customization": 83, "azure-foundry-fine-tuning": 84, "by": 1, "edge": "azure-foundry-fine-tuning", "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "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 \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.", "azure-foundry-fine-tuning": "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-azure-foundry-fine-tuning", "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-azure-foundry-fine-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-azure-foundry-fine-tuning.md", "slim": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-azure-foundry-fine-tuning.min.md" }, "markdown": "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 \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- 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\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 65\n- Schema \u0026 documentation, 87 against 67\n- Agent ergonomics, 77 against 47\n- Payments \u0026 pricing, 30 against 20\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\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### Microsoft Foundry fine-tuning (Azure OpenAI) (C)\n\nGood for: Teams that must tune an OpenAI model, need Azure's compliance and regional controls, and will serve the result on Azure.\n\nAhead on:\n- Maintenance \u0026 community, 55 against 45\n\nWatch for: No weight export; checkpoints copy only between Azure resources\n\n\n## Score by category\n\n| Category | Weight | Amazon Bedrock model customisation | Microsoft Foundry fine-tuning (Azure OpenAI) | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 95 | 65 | Amazon Bedrock model customisation +30 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 87 | 67 | Amazon Bedrock model customisation +20 |\n| Agent ergonomics | 13% (16.2 this run) | 77 | 47 | Amazon Bedrock model customisation +30 |\n| Security \u0026 auth | 14% (17.5 this run) | 87 | 85 | Amazon Bedrock model customisation +2 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 20 | Amazon Bedrock model customisation +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 45 | 55 | Microsoft Foundry fine-tuning (Azure OpenAI) +10 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 83 | 84 | Microsoft Foundry fine-tuning (Azure OpenAI) +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **75.8 · BB** | **61.1 · C** | |\n\n## Facts side by side\n\n| Fact | Amazon Bedrock model customisation | Microsoft Foundry fine-tuning (Azure OpenAI) |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Amazon Web Services | Microsoft Azure |\n| Hosted endpoint | `https://bedrock.{region}.amazonaws.com/model-customization-jobs` | `https://\u003cresource\u003e.openai.azure.com/openai/v1` |\n| Transports | HTTP | HTTP |\n| Auth | OAuth or key | OAuth or key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-05-28 | none |\n| Terms last updated | 2026-10-01 | no date given |\n| Privacy policy last updated | 2026-05-18 | 2026-09-01 |\n| Customer content may train models | yes, with an opt-out | yes |\n| Terms restrict automated access | yes | yes |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | yes | not found in the text |\n| Arbitration or class-action waiver | not found in the text | not found in the text |\n| Popularity | 2.8M npm/wk, 573.7M PyPI/wk | 47.2M npm/wk, 72.1M PyPI/wk |\n| Agent reviews | none | 3.5/5 (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**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.\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### Microsoft Foundry fine-tuning (Azure OpenAI)\n\n1. Point the OpenAI SDK at https://\u003cresource\u003e.openai.azure.com/openai/v1 with the `api-key` header or an Entra token; job, file and checkpoint calls are the OpenAI shapes\n2. 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\n3. Keep at most 3 jobs running and 20 queued per resource, and keep training files under 512 MB and 1 GB in total\n4. 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\n5. Query the Models API for `deprecationDate` before choosing a base model\n\n## Questions\n\n### Which is better for AI agents, Amazon Bedrock model customisation or Microsoft Foundry fine-tuning (Azure OpenAI)?\n\nAmazon 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 \u0026 community.\n\n### Do Amazon Bedrock model customisation and Microsoft Foundry fine-tuning (Azure OpenAI) need an API key?\n\nBoth take an API key or an OAuth sign-in.\n\n### Can an agent call Amazon Bedrock model customisation and Microsoft Foundry fine-tuning (Azure OpenAI) without installing anything?\n\nYes. 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://\u003cresource\u003e.openai.azure.com/openai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-azure-foundry-fine-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-azure-foundry-fine-tuning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"amazon-bedrock-customization\", \"b\": \"azure-foundry-fine-tuning\"}`. From a terminal: `anchor compare amazon-bedrock-customization azure-foundry-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/azure-foundry-fine-tuning.json\n\n## Other comparisons with Amazon Bedrock model customisation or Microsoft Foundry fine-tuning (Azure OpenAI)\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 Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-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 Microsoft Foundry fine-tuning (Azure OpenAI)](https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-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- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Tinker](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-tinker.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Unsloth](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-unsloth.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/azure-foundry-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 Microsoft Foundry fine-tuning (Azure OpenAI)", "url": "" } ], "description": "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 \u0026 community. Both do finetune sft.…", "facts": [ "Amazon Bedrock model customisation BB 75.8", "Microsoft Foundry fine-tuning (Azure OpenAI) C 61.1", "scores" ], "h1": "Amazon Bedrock model customisation vs Microsoft Foundry fine-tuning (Azure OpenAI)", "image": "https://www.anchorterminal.com/assets/og/compare-amazon-bedrock-customization-vs-azure-foundry-fine-tuning.png", "path": "/compare/amazon-bedrock-customization-vs-azure-foundry-fine-tuning", "published": "2026-10-01", "section": "tools", "title": "Amazon Bedrock model customisation vs Microsoft Foundry fine-tuning", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-azure-foundry-fine-tuning" }, "tokens": { "markdown": 2650, "slim": 730 }, "version": 1 }