{ "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": "2026-10-09T11:46:21.208433901Z", "lastOk": false, "lastStatus": 0, "lastMs": 0, "lastNote": "invalid character \"{\" in host name", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 44, "samples30d": 44, "days": [ { "date": "2026-10-09", "probes": 44, "ok": 0 } ], "outages": [ { "start": "2026-10-09T07:40:18.733946397Z", "end": "0001-01-01T00:00:00Z", "note": "invalid character \"{\" in host name" } ] }, "updatedAt": "2026-10-09T11:46:21.208433901Z" } }, "answer": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on maintenance \u0026 community.", "b": { "slug": "together-fine-tuning", "name": "Together AI Fine-tuning", "vendor": "Together AI", "vendorUrl": "https://www.together.ai", "kind": "http-api", "category": "fine-tuning", "summary": "Managed LoRA and full fine-tuning, supervised or DPO, on about 30 open models from Qwen3.5 0.8B to Kimi K2.7, billed per training token with a $4 minimum.", "url": "https://www.anchorterminal.com/tools/together-fine-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json", "repo": "https://github.com/togethercomputer/together-py", "license": "Apache-2.0 (SDKs)", "transports": [ "http" ], "remoteUrl": "https://api.together.ai/v1", "packages": [ { "registry": "pypi", "name": "together" }, { "registry": "npm", "name": "together-ai" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with the key from the console, read from `TOGETHER_API_KEY` by the SDKs and the `tg` CLI. One key covers files, fine-tuning jobs, downloads and endpoints.", "pricing": "usage", "pricingNotes": "Per training token, where tokens = epochs x training tokens + evaluations x validation tokens. LoRA SFT from $0.34 per 1M (Llama 3.1 8B, Qwen3.5 9B) through $1.05 (Qwen3.8 27B), $2.03 (Llama 3.3 70B), $2.50 (gpt-oss-120b), $7 (DeepSeek V3.1) and $15 (Kimi K2.6) to $40 (GLM-5.2). DPO is 2.5x the SFT rate ($0.84 for Llama 3.1 8B, $37.50 for Kimi K2.6). Full fine-tuning $0.38 (8B and 9B models) to $2.24 (Llama 3.3 70B). Minimum $4 a job, rising to $6 for gpt-oss-120b, $20 for DeepSeek V3.1 and $60 for Kimi K2.6. Hosting the result needs a dedicated endpoint; the pricing page lists dedicated endpoint GPUs at $5.49 an hour for an H100 and $8.99 for a B200, with H200 and B300 by quote. No free trial; access needs a $5 prepaid credit purchase (https://www.together.ai/pricing, https://docs.together.ai/docs/billing-credits).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 10, "npmWeekly": 117852, "pypiWeekly": 369054, "asOf": "2026-09-30" }, "docsUrl": "https://docs.together.ai/docs/fine-tuning/overview", "llmsTxt": "https://docs.together.ai/llms.txt", "openapi": "https://docs.together.ai/openapi.yaml", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.lora", "finetune.export" ], "tags": [ "hosted", "usage-priced", "card-required", "open-weights", "llms-txt", "python", "typescript", "async-jobs" ], "lastRelease": "2026-09-30", "graded": true, "anchor": { "graded": true, "score": 54.7, "grade": "C", "agentReady": false, "rank": 608, "ranked": true, "rankOf": 842, "categoryRank": 6, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 42, "maintenance": 80, "payments": 20, "reliability": 55, "schema": 78, "security": 50, "transparency": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour.", "bestFor": "Teams that want to tune a large open model, possibly with full fine-tuning, and take the weights away.", "strengths": [ "31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA", "GET /v1/finetune/download returns merged weights or the adapter, at any saved checkpoint", "POST /v1/fine-tunes/estimate-price quotes a job before it runs", "Project-scoped API keys with expiry dates from 1 hour", "Python and TypeScript SDKs, an OpenAPI file and llms.txt" ], "weaknesses": [ "Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour", "No free trial, a $5 prepaid purchase before the first call, and job minimums up to $60", "The status page covers serverless models only, and no fine-tuning rate limits are published", "No pagination on the job list and no documented error responses for fine-tuning calls", "No read-only project role and no audit log found" ], "agentNotes": [ "Call POST /v1/fine-tunes/estimate-price with the same body before creating the job, and check the model's minimum charge", "Read `lora_training.max_rank` from the model limits response before setting `lora_r`; most models went to 128 on 2026-09-29", "Don't retry a create call blindly after a timeout; there's no idempotency key, so list jobs and check first", "Download with checkpoint=adapter if you'll merge locally; merged weights for a 70B model are a large stream", "Tear down the dedicated endpoint once evaluation ends, since it bills while idle" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54.7 } ], "editorialScores": { "ergonomics": 42, "maintenance": 80, "payments": 20, "reliability": 55, "schema": 78, "security": 50, "transparency": 55 }, "provenanceScore": 81 }, "connect": { "install": "pip install together # or: npm i together-ai", "http": "curl https://api.together.ai/v1/fine-tunes \\\n -H \"Authorization: Bearer $TOGETHER_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"Qwen/Qwen3.5-9B\",\"training_file\":\"file-abc123\",\"n_epochs\":3,\"training_type\":{\"type\":\"Lora\",\"lora_r\":16,\"lora_alpha\":32},\"training_method\":{\"method\":\"sft\"},\"suffix\":\"my-run\"}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/together-fine-tuning" }, "sameCompany": [ "together-code-sandbox" ], "area": "models", "unitPrices": [ { "item": "LoRA SFT, Llama 3.1 8B", "unit": "1m-tokens", "usd": 0.34, "note": "Same rate for Qwen3.5 9B. $4 minimum" }, { "item": "LoRA DPO, Llama 3.1 8B", "unit": "1m-tokens", "usd": 0.84 }, { "item": "Full SFT, Llama 3.1 8B", "unit": "1m-tokens", "usd": 0.38 }, { "item": "LoRA SFT, Qwen3.8 27B", "unit": "1m-tokens", "usd": 1.05 }, { "item": "LoRA SFT, Llama 3.3 70B", "unit": "1m-tokens", "usd": 2.03, "note": "Full SFT $2.24" }, { "item": "LoRA SFT, gpt-oss-120b", "unit": "1m-tokens", "usd": 2.5, "note": "$6 minimum" }, { "item": "LoRA SFT, DeepSeek V3.1", "unit": "1m-tokens", "usd": 7, "note": "$20 minimum" }, { "item": "LoRA SFT, Kimi K2.6", "unit": "1m-tokens", "usd": 15, "note": "$60 minimum" }, { "item": "H100 on demand", "unit": "gpu-hour", "usd": 3.99 }, { "item": "H200 on demand", "unit": "gpu-hour", "usd": 5.99 }, { "item": "B200 on demand", "unit": "gpu-hour", "usd": 8.19 } ], "provenance": { "legalEntity": "Together Computer, Inc.", "domain": "together.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://www.together.ai/terms-of-service", "privacy": "https://www.together.ai/privacy", "statusPage": "https://status.together.ai", "changelog": "https://docs.together.ai/docs/changelog", "securityTxt": "valid", "checked": "2026-09-30", "notes": [ "The terms (2026-05-19) name Together Computer, Inc., a Delaware corporation. The privacy policy (2025-12-17) says data isn't used to train models without opt-in.", "security.txt points Contact and Policy at hackerone.com/together_ai and has no Expires field.", "The status page monitors serverless inference models only; there's no fine-tuning component.", "The .ai registry's RDAP server refused our requests, so the registration date is blank.", "The MCP registry has a third-party io.usefulapi/together-ai server that wraps fine-tunes; Together doesn't publish one." ], "score": 81 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.json", "live": { "slug": "together-fine-tuning", "probe": { "target": "https://api.together.ai/v1", "method": "get", "lastAt": "2026-10-09T11:46:43.285947559Z", "lastOk": true, "lastStatus": 404, "lastMs": 226, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 229, "p95ms24h": 522, "samples24h": 259, "samples30d": 2109, "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": 125, "ok": 125 } ] }, "vendorStatus": { "page": "https://status.together.ai", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-09T07:58:36.144917886Z" }, "versions": [ { "registry": "github", "name": "togethercomputer/together-py", "version": "v2.40.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:32:29.219602138Z" }, { "registry": "npm", "name": "together-ai", "version": "0.58.0", "seenAt": "2026-10-08T16:32:28.317335771Z" }, { "registry": "pypi", "name": "together", "version": "2.40.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:32:28.188942399Z" } ], "githubStars": 10, "npmWeekly": 120454, "pypiWeekly": 388333, "securityTxt": { "url": "https://together.ai/.well-known/security.txt", "state": "valid", "expires": "2028-09-30T00:00:00.000Z", "checkedAt": "2026-10-08T15:38:43.914614283Z" }, "llmsTxt": { "url": "https://docs.together.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:56.837586142Z" }, "domain": { "domain": "together.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/together.ai", "checkedAt": "2026-10-04T13:04:15.476837291Z" }, "pages": [ { "url": "https://docs.together.ai/docs/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-10-08T18:19:36.548210442Z", "changedAt": "2026-10-08T18:19:36.548210442Z", "fingerprint": "ac6616429c58" }, { "url": "https://www.together.ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:31:00.887281809Z", "changedAt": "2026-10-08T18:31:00.887281809Z", "fingerprint": "ccd78896d28a" }, { "url": "https://www.together.ai/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-08T18:31:03.071776633Z", "changedAt": "2026-10-08T18:31:03.071776633Z", "fingerprint": "ca24e4a2e8da" }, { "url": "https://www.together.ai/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:31:05.089468507Z", "changedAt": "2026-10-08T18:31:05.089468507Z", "fingerprint": "e55cb7869607" } ], "updatedAt": "2026-10-09T11:46:43.285947559Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Amazon Web Services", "b": "Together AI", "name": "Vendor" }, { "a": "https://bedrock.{region}.amazonaws.com/model-customization-jobs", "b": "https://api.together.ai/v1", "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": "$0.34 per 1M tokens", "name": "Price for finetune sft" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "none", "b": "Apache-2.0 (SDKs)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-05-28", "b": "2026-09-30", "name": "Last release" }, { "a": "2026-10-01", "b": "no date given", "name": "Terms last updated" }, { "a": "2026-05-18", "b": "no date given", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "yes", "b": "not found in the text", "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": "10 stars, 118k npm/wk, 369k PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "3/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on maintenance \u0026 community.", "question": "Which is better for AI agents, Amazon Bedrock model customisation or Together AI Fine-tuning?" }, { "answer": "Amazon Bedrock model customisation takes an API key or an OAuth sign-in. Together AI Fine-tuning needs an API key.", "question": "Do Amazon Bedrock model customisation and Together 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 Together AI Fine-tuning at https://api.together.ai/v1.", "question": "Can an agent call Amazon Bedrock model customisation and Together AI Fine-tuning without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 95 against 55", "Schema \u0026 documentation, 87 against 78", "Agent ergonomics, 77 against 42", "Security \u0026 auth, 87 against 50", "Payments \u0026 pricing, 30 against 20", "Transparency \u0026 trust, 83 against 68" ], "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, 80 against 45" ], "also": null, "goodFor": "Teams that want to tune a large open model, possibly with full fine-tuning, and take the weights away.", "slug": "together-fine-tuning", "watchFor": "Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour" } ], "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-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-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-together-fine-tuning.json", "title": "Axolotl vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/axolotl-vs-together-fine-tuning" }, { "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/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/nebius-token-factory-fine-tuning-vs-together-fine-tuning.json", "title": "Nebius Token Factory fine-tuning vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.json", "title": "Tinker vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.json", "title": "Together AI Fine-tuning vs Unsloth", "url": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning.json", "title": "Together AI Fine-tuning vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning" } ], "scores": [ { "amazon-bedrock-customization": 95, "by": 40, "edge": "amazon-bedrock-customization", "key": "reliability", "name": "Reliability", "together-fine-tuning": 55, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "amazon-bedrock-customization": 87, "by": 9, "edge": "amazon-bedrock-customization", "key": "schema", "name": "Schema \u0026 documentation", "together-fine-tuning": 78, "weight": 13 }, { "amazon-bedrock-customization": 77, "by": 35, "edge": "amazon-bedrock-customization", "key": "ergonomics", "name": "Agent ergonomics", "together-fine-tuning": 42, "weight": 13 }, { "amazon-bedrock-customization": 87, "by": 37, "edge": "amazon-bedrock-customization", "key": "security", "name": "Security \u0026 auth", "together-fine-tuning": 50, "weight": 14 }, { "amazon-bedrock-customization": 30, "by": 10, "edge": "amazon-bedrock-customization", "key": "payments", "name": "Payments \u0026 pricing", "together-fine-tuning": 20, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "amazon-bedrock-customization": 45, "by": 35, "edge": "together-fine-tuning", "key": "maintenance", "name": "Maintenance \u0026 community", "together-fine-tuning": 80, "weight": 7 }, { "amazon-bedrock-customization": 83, "by": 15, "edge": "amazon-bedrock-customization", "key": "transparency", "name": "Transparency \u0026 trust", "together-fine-tuning": 68, "weight": 7 } ], "summary": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on maintenance \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.", "together-fine-tuning": "31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-fine-tuning", "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-fine-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-fine-tuning.md", "slim": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-fine-tuning.min.md" }, "markdown": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on maintenance \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- Together AI Fine-tuning: grade C, 54.7/100, rank #608 of 842. Markdown https://www.anchorterminal.com/tools/together-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/together-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 78\n- Agent ergonomics, 77 against 42\n- Security \u0026 auth, 87 against 50\n- Payments \u0026 pricing, 30 against 20\n- Transparency \u0026 trust, 83 against 68\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### Together AI Fine-tuning (C)\n\nGood for: Teams that want to tune a large open model, possibly with full fine-tuning, and take the weights away.\n\nAhead on:\n- Maintenance \u0026 community, 80 against 45\n\nWatch for: Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour\n\n\n## Score by category\n\n| Category | Weight | Amazon Bedrock model customisation | Together 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 | 78 | Amazon Bedrock model customisation +9 |\n| Agent ergonomics | 13% (16.2 this run) | 77 | 42 | Amazon Bedrock model customisation +35 |\n| Security \u0026 auth | 14% (17.5 this run) | 87 | 50 | Amazon Bedrock model customisation +37 |\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 | 80 | Together AI Fine-tuning +35 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 83 | 68 | Amazon Bedrock model customisation +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **75.8 · BB** | **54.7 · C** | |\n\n## Facts side by side\n\n| Fact | Amazon Bedrock model customisation | Together AI Fine-tuning |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Amazon Web Services | Together AI |\n| Hosted endpoint | `https://bedrock.{region}.amazonaws.com/model-customization-jobs` | `https://api.together.ai/v1` |\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 | $0.34 per 1M tokens |\n| x402 | no | no |\n| Licence | none | Apache-2.0 (SDKs) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-05-28 | 2026-09-30 |\n| Terms last updated | 2026-10-01 | no date given |\n| Privacy policy last updated | 2026-05-18 | no date given |\n| Customer content may train models | yes, with an opt-out | not found in the text |\n| Terms restrict automated access | yes | not found in the text |\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 | 10 stars, 118k npm/wk, 369k PyPI/wk |\n| Agent reviews | none | 3/5 (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**Together AI Fine-tuning.** 31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour.\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### Together AI Fine-tuning\n\n1. Call POST /v1/fine-tunes/estimate-price with the same body before creating the job, and check the model's minimum charge\n2. Read `lora_training.max_rank` from the model limits response before setting `lora_r`; most models went to 128 on 2026-09-29\n3. Don't retry a create call blindly after a timeout; there's no idempotency key, so list jobs and check first\n4. Download with checkpoint=adapter if you'll merge locally; merged weights for a 70B model are a large stream\n5. Tear down the dedicated endpoint once evaluation ends, since it bills while idle\n\n## Questions\n\n### Which is better for AI agents, Amazon Bedrock model customisation or Together AI Fine-tuning?\n\nAmazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on maintenance \u0026 community.\n\n### Do Amazon Bedrock model customisation and Together AI Fine-tuning need an API key?\n\nAmazon Bedrock model customisation takes an API key or an OAuth sign-in. Together AI Fine-tuning needs an API key.\n\n### Can an agent call Amazon Bedrock model customisation and Together 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 Together AI Fine-tuning at https://api.together.ai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-fine-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-fine-tuning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"amazon-bedrock-customization\", \"b\": \"together-fine-tuning\"}`. From a terminal: `anchor compare amazon-bedrock-customization together-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/together-fine-tuning.json\n\n## Other comparisons with Amazon Bedrock model customisation or Together 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 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 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 Together AI Fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-together-fine-tuning.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- [Fireworks AI Fine-tuning vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning.md)\n- [Nebius Token Factory fine-tuning vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning.md)\n- [Tinker vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.md)\n- [Together AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.md)\n- [Together AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/together-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 Together AI Fine-tuning", "url": "" } ], "description": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on maintenance \u0026 community. Both do finetune sft. Category scores, facts, verdicts and agent…", "facts": [ "Amazon Bedrock model customisation BB 75.8", "Together AI Fine-tuning C 54.7", "scores" ], "h1": "Amazon Bedrock model customisation vs Together AI Fine-tuning", "image": "https://www.anchorterminal.com/assets/og/compare-amazon-bedrock-customization-vs-together-fine-tuning.png", "path": "/compare/amazon-bedrock-customization-vs-together-fine-tuning", "published": "2026-10-01", "section": "tools", "title": "Amazon Bedrock model customisation vs Together AI Fine-tuning", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-fine-tuning" }, "tokens": { "markdown": 2550, "slim": 730 }, "version": 1 }