{ "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": "2026-10-09T12:15:14.342017986Z", "lastOk": false, "lastStatus": 0, "lastMs": 0, "lastNote": "invalid character \"{\" in host name", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 49, "samples30d": 49, "days": [ { "date": "2026-10-09", "probes": 49, "ok": 0 } ], "outages": [ { "start": "2026-10-09T07:40:18.733946397Z", "end": "0001-01-01T00:00:00Z", "note": "invalid character \"{\" in host name" } ] }, "updatedAt": "2026-10-09T12:15:14.342017986Z" } }, "answer": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Vertex AI Gemini tuning's 64.2 (B), and leads in 5 of 7 scored categories. Vertex AI Gemini tuning leads on maintenance \u0026 community and transparency \u0026 trust.", "b": { "slug": "vertex-ai-tuning", "name": "Vertex AI Gemini tuning", "vendor": "Google Cloud", "vendorUrl": "https://cloud.google.com", "kind": "http-api", "category": "fine-tuning", "summary": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen, on Google Cloud's Gemini Enterprise Agent Platform (the platform formerly called Vertex AI).", "url": "https://www.anchorterminal.com/tools/vertex-ai-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json", "repo": "https://github.com/googleapis/python-genai", "license": "Apache-2.0 (SDK)", "transports": [ "http" ], "remoteUrl": "https://us-central1-aiplatform.googleapis.com/v1", "packages": [ { "registry": "pypi", "name": "google-genai" } ], "auth": "oauth", "authNotes": "OAuth 2.0 bearer token from a service account or `gcloud auth print-access-token` on a project with billing and the platform API turned on. Training data comes from a Cloud Storage URI, so the caller also needs read access to the bucket. Tuning is a Vertex-only feature. The SDK says tuning is supported only on the enterprise platform, not the Gemini Developer API.", "pricing": "usage", "pricingNotes": "Per training token, where training tokens = dataset tokens x epochs. Gemini 3.5 Flash $10 per 1M for supervised or reinforcement learning fine-tuning (listed as $0.01 per 1,000), Gemini 3.1 Flash Lite $3, Gemini 2.5 Pro $25, Gemini 2.5 Flash $5 for supervised or preference tuning, Gemini 2.5 Flash Lite $1.50. Open models run from Gemma 3 at $0.47 (1B) to $6.83 (27B), Llama 3.1 8B $0.67, Llama 3.3 70B $6.72, Llama 4 Scout $5.77, Qwen 3 4B $1.35 to Qwen 3 32B $6.57. From Gemini 3 on, a tuned model endpoint costs 1.5x the base model's prediction price; older Gemini tuned models cost the same as base (https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 3900, "npmWeekly": null, "pypiWeekly": 32928433, "asOf": "2026-09-30" }, "docsUrl": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/tuning", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.rl", "finetune.lora" ], "tags": [ "hosted", "usage-priced", "closed-source", "card-required", "enterprise", "python", "async-jobs" ], "lastRelease": "2026-10-01", "graded": true, "anchor": { "graded": true, "score": 64.2, "grade": "B", "agentReady": false, "rank": 325, "ranked": true, "rankOf": 842, "categoryRank": 3, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 48, "maintenance": 80, "payments": 20, "reliability": 67, "schema": 82, "security": 71, "transparency": 88 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.", "bestFor": "Teams already on Google Cloud who need to tune Gemini itself, especially with RL, and will serve it there.", "strengths": [ "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen", "No Vertex or Gemini incidents on the Google Cloud status dashboard from July to September 2026", "Public proto for GenAiTuningService with filter and pagination on job lists, and docs pages served as Markdown at `.md.txt`", "Google says it won't train or fine-tune on customer data without permission, and a dated model lifecycle table promises 12 months from release", "ISO 27001, 27017 and 27018 and SOC 1, 2 and 3 cover Gemini Enterprise Agent Platform, and the subprocessor list gives locations" ], "weaknesses": [ "No weight export. The tuned model exists only as a Google Cloud endpoint", "Tuned Gemini 3 inference costs 1.5x the base model for as long as you serve it", "Setup needs a project, billing, IAM and a Cloud Storage bucket before the first job", "RL tuning is Pre-GA on v1beta1, and the SDK's `tunings.tune()` is marked experimental", "Gemini 2.5 Pro, Flash and Flash-Lite retire on 20 October 2026, and the docs don't say what happens to their tunes" ], "agentNotes": [ "Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API", "Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown", "Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20", "List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it", "Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.2 } ], "editorialScores": { "ergonomics": 48, "maintenance": 80, "payments": 20, "reliability": 67, "schema": 82, "security": 71, "transparency": 78 }, "provenanceScore": 97 }, "connect": { "install": "pip install google-genai", "http": "curl -X POST \"https://us-central1-aiplatform.googleapis.com/v1/projects/$GOOGLE_CLOUD_PROJECT/locations/us-central1/tuningJobs\" \\\n -H \"Authorization: Bearer $(gcloud auth print-access-token)\" -H \"content-type: application/json\" \\\n -d '{\"baseModel\":\"gemini-3.5-flash\",\"supervisedTuningSpec\":{\"trainingDatasetUri\":\"gs://my-bucket/train.jsonl\",\"hyperParameters\":{\"epochCount\":3,\"adapterSize\":\"ADAPTER_SIZE_FOUR\"}},\"tunedModelDisplayName\":\"my-tune\"}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/vertex-ai-tuning" }, "sameCompany": [ "gemini-api", "gemini-embedding", "google-model-armor", "google-imagen", "google-veo", "google-lyria", "google-speech-to-text", "gemini-live", "google-adk", "google-secret-manager", "google-weather-api", "chrome-devtools-mcp", "google-maps-platform", "google-cloud-translation", "google-calendar-api", "firebase-cloud-messaging", "google-drive-api", "gemini-cli", "google-search-console", "google-ads-api", "google-forms", "google-sheets-api", "gmail-api" ], "area": "models", "unitPrices": [ { "item": "Gemini 3.5 Flash, supervised tuning", "unit": "1m-tokens", "usd": 10 }, { "item": "Gemini 3.5 Flash, reinforcement learning fine-tuning", "unit": "1m-tokens", "usd": 10 }, { "item": "Gemini 3.1 Flash Lite, supervised tuning", "unit": "1m-tokens", "usd": 3 }, { "item": "Gemini 2.5 Pro, supervised tuning", "unit": "1m-tokens", "usd": 25 }, { "item": "Gemini 2.5 Flash, supervised or preference tuning", "unit": "1m-tokens", "usd": 5 }, { "item": "Gemini 2.5 Flash Lite, supervised or preference tuning", "unit": "1m-tokens", "usd": 1.5 }, { "item": "Gemma 3 27B IT, supervised tuning", "unit": "1m-tokens", "usd": 6.83 }, { "item": "Llama 3.3 70B, supervised tuning", "unit": "1m-tokens", "usd": 6.72 }, { "item": "Qwen 3 32B, supervised tuning", "unit": "1m-tokens", "usd": 6.57 } ], "provenance": { "legalEntity": "Google LLC", "domain": "google.com", "domainRegistered": "1997-09-15", "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.", "endpointOnVendorDomain": true, "terms": "https://cloud.google.com/terms", "privacy": "https://policies.google.com/privacy", "statusPage": "https://status.cloud.google.com", "changelog": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/release-notes", "securityTxt": "valid", "checked": "2026-09-30", "notes": [ "Entity, domain and security.txt are the same as the gemini-api listing, which uses the same Google privacy policy. The terms differ: this product runs under the Google Cloud Platform terms, whose contracting entity is set per billing country at cloud.google.com/terms/google-entity.", "The docs site serves navigation first and truncates the article body for a text fetcher, so the supported-model list, dataset limits and the checkpoint export page couldn't be read. Model and price facts come from the pricing page and the google-genai source.", "The old Vertex AI pricing page at cloud.google.com/vertex-ai/generative-ai/pricing still serves, but its tuning table stops at Gemini 2.5; the Gemini Enterprise Agent Platform pricing page has the Gemini 3 rows." ], "score": 97 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.json", "live": { "slug": "vertex-ai-tuning", "probe": { "target": "https://us-central1-aiplatform.googleapis.com/v1", "method": "get", "lastAt": "2026-10-09T12:15:40.757533137Z", "lastOk": true, "lastStatus": 404, "lastMs": 594, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 602, "p95ms24h": 675, "samples24h": 259, "samples30d": 2114, "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": 130, "ok": 130 } ] }, "versions": [ { "registry": "github", "name": "googleapis/python-genai", "version": "v2.29.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:34:14.536586178Z" }, { "registry": "pypi", "name": "google-genai", "version": "2.29.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:34:14.345198998Z" } ], "githubStars": 4009, "pypiWeekly": 34128301, "securityTxt": { "url": "https://google.com/.well-known/security.txt", "state": "valid", "expires": "2030-04-01T00:00:00z", "checkedAt": "2026-10-08T15:38:39.75078566Z" }, "domain": { "domain": "google.com", "registered": "1997-09-15", "source": "https://rdap.verisign.com/com/v1/domain/google.com", "checkedAt": "2026-10-04T13:05:50.737985829Z" }, "pages": [ { "url": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/release-notes", "kind": "changelog", "status": 200, "checkedAt": "2026-10-08T18:18:25.711138322Z", "changedAt": "2026-10-08T18:18:25.711138322Z", "fingerprint": "5ca95b459af7" }, { "url": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:16:15.480038421Z", "changedAt": "2026-10-08T18:16:15.480038421Z", "fingerprint": "9f07a469a718" } ], "updatedAt": "2026-10-09T12:15:40.757533137Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Amazon Web Services", "b": "Google Cloud", "name": "Vendor" }, { "a": "https://bedrock.{region}.amazonaws.com/model-customization-jobs", "b": "https://us-central1-aiplatform.googleapis.com/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "OAuth", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "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": "no", "name": "llms.txt" }, { "a": "2026-05-28", "b": "2026-10-01", "name": "Last release" }, { "a": "2026-10-01", "b": "2026-09-02", "name": "Terms last updated" }, { "a": "2026-05-18", "b": "2026-10-01", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "yes", "name": "Customer content may train models" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "not found in the text", "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": "3.9k stars, 32.9M 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 Vertex AI Gemini tuning's 64.2 (B), and leads in 5 of 7 scored categories. Vertex AI Gemini tuning leads on maintenance \u0026 community and transparency \u0026 trust.", "question": "Which is better for AI agents, Amazon Bedrock model customisation or Vertex AI Gemini tuning?" }, { "answer": "Amazon Bedrock model customisation takes an API key or an OAuth sign-in. Vertex AI Gemini tuning uses an OAuth sign-in.", "question": "Do Amazon Bedrock model customisation and Vertex AI Gemini 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 Vertex AI Gemini tuning at https://us-central1-aiplatform.googleapis.com/v1.", "question": "Can an agent call Amazon Bedrock model customisation and Vertex AI Gemini tuning without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 95 against 67", "Schema \u0026 documentation, 87 against 82", "Agent ergonomics, 77 against 48", "Security \u0026 auth, 87 against 71", "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, 80 against 45", "Transparency \u0026 trust, 88 against 83" ], "also": null, "goodFor": "Teams already on Google Cloud who need to tune Gemini itself, especially with RL, and will serve it there.", "slug": "vertex-ai-tuning", "watchFor": "No weight export. The tuned model exists only as a Google Cloud endpoint" } ], "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-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/axolotl-vs-vertex-ai-tuning.json", "title": "Axolotl vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-tuning" }, { "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" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.json", "title": "Nebius Token Factory fine-tuning vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning" }, { "json": "https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning.json", "title": "Tinker vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning.json", "title": "Unsloth vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning" } ], "scores": [ { "amazon-bedrock-customization": 95, "by": 28, "edge": "amazon-bedrock-customization", "key": "reliability", "name": "Reliability", "vertex-ai-tuning": 67, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "amazon-bedrock-customization": 87, "by": 5, "edge": "amazon-bedrock-customization", "key": "schema", "name": "Schema \u0026 documentation", "vertex-ai-tuning": 82, "weight": 13 }, { "amazon-bedrock-customization": 77, "by": 29, "edge": "amazon-bedrock-customization", "key": "ergonomics", "name": "Agent ergonomics", "vertex-ai-tuning": 48, "weight": 13 }, { "amazon-bedrock-customization": 87, "by": 16, "edge": "amazon-bedrock-customization", "key": "security", "name": "Security \u0026 auth", "vertex-ai-tuning": 71, "weight": 14 }, { "amazon-bedrock-customization": 30, "by": 10, "edge": "amazon-bedrock-customization", "key": "payments", "name": "Payments \u0026 pricing", "vertex-ai-tuning": 20, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "amazon-bedrock-customization": 45, "by": 35, "edge": "vertex-ai-tuning", "key": "maintenance", "name": "Maintenance \u0026 community", "vertex-ai-tuning": 80, "weight": 7 }, { "amazon-bedrock-customization": 83, "by": 5, "edge": "vertex-ai-tuning", "key": "transparency", "name": "Transparency \u0026 trust", "vertex-ai-tuning": 88, "weight": 7 } ], "summary": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Vertex AI Gemini tuning's 64.2 (B), and leads in 5 of 7 scored categories. Vertex AI Gemini tuning leads on maintenance \u0026 community and transparency \u0026 trust. 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.", "vertex-ai-tuning": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning", "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning.md", "slim": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning.min.md" }, "markdown": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Vertex AI Gemini tuning's 64.2 (B), and leads in 5 of 7 scored categories. Vertex AI Gemini tuning leads on maintenance \u0026 community and transparency \u0026 trust. 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- Vertex AI Gemini tuning: grade B, 64.2/100, rank #325 of 842. Markdown https://www.anchorterminal.com/tools/vertex-ai-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/vertex-ai-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 67\n- Schema \u0026 documentation, 87 against 82\n- Agent ergonomics, 77 against 48\n- Security \u0026 auth, 87 against 71\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### Vertex AI Gemini tuning (B)\n\nGood for: Teams already on Google Cloud who need to tune Gemini itself, especially with RL, and will serve it there.\n\nAhead on:\n- Maintenance \u0026 community, 80 against 45\n- Transparency \u0026 trust, 88 against 83\n\nWatch for: No weight export. The tuned model exists only as a Google Cloud endpoint\n\n\n## Score by category\n\n| Category | Weight | Amazon Bedrock model customisation | Vertex AI Gemini tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 95 | 67 | Amazon Bedrock model customisation +28 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 87 | 82 | Amazon Bedrock model customisation +5 |\n| Agent ergonomics | 13% (16.2 this run) | 77 | 48 | Amazon Bedrock model customisation +29 |\n| Security \u0026 auth | 14% (17.5 this run) | 87 | 71 | Amazon Bedrock model customisation +16 |\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 | Vertex AI Gemini tuning +35 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 83 | 88 | Vertex AI Gemini tuning +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **75.8 · BB** | **64.2 · B** | |\n\n## Facts side by side\n\n| Fact | Amazon Bedrock model customisation | Vertex AI Gemini tuning |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Amazon Web Services | Google Cloud |\n| Hosted endpoint | `https://bedrock.{region}.amazonaws.com/model-customization-jobs` | `https://us-central1-aiplatform.googleapis.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | OAuth or key | OAuth |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | none | Apache-2.0 (SDK) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-05-28 | 2026-10-01 |\n| Terms last updated | 2026-10-01 | 2026-09-02 |\n| Privacy policy last updated | 2026-05-18 | 2026-10-01 |\n| Customer content may train models | yes, with an opt-out | yes |\n| Terms restrict automated access | yes | not found in the text |\n| Terms restrict benchmarking | yes | not found in the text |\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 | 3.9k stars, 32.9M 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**Vertex AI Gemini tuning.** Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.\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### Vertex AI Gemini tuning\n\n1. Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API\n2. Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown\n3. Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20\n4. List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it\n5. Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes\n\n## Questions\n\n### Which is better for AI agents, Amazon Bedrock model customisation or Vertex AI Gemini tuning?\n\nAmazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Vertex AI Gemini tuning's 64.2 (B), and leads in 5 of 7 scored categories. Vertex AI Gemini tuning leads on maintenance \u0026 community and transparency \u0026 trust.\n\n### Do Amazon Bedrock model customisation and Vertex AI Gemini tuning need an API key?\n\nAmazon Bedrock model customisation takes an API key or an OAuth sign-in. Vertex AI Gemini tuning uses an OAuth sign-in.\n\n### Can an agent call Amazon Bedrock model customisation and Vertex AI Gemini tuning without installing anything?\n\nYes. Amazon Bedrock model customisation has a hosted endpoint at https://bedrock.{region}.amazonaws.com/model-customization-jobs and Vertex AI Gemini tuning at https://us-central1-aiplatform.googleapis.com/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"amazon-bedrock-customization\", \"b\": \"vertex-ai-tuning\"}`. From a terminal: `anchor compare amazon-bedrock-customization vertex-ai-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/vertex-ai-tuning.json\n\n## Other comparisons with Amazon Bedrock model customisation or Vertex AI Gemini 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 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- [Axolotl vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-tuning.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- [Fireworks AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.md)\n- [Nebius Token Factory fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.md)\n- [Tinker vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning.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- [Unsloth vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/unsloth-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 Vertex AI Gemini tuning", "url": "" } ], "description": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Vertex AI Gemini tuning's 64.2 (B), and leads in 5 of 7 scored categories. Vertex AI Gemini tuning leads on maintenance \u0026 community and transparency \u0026 trust. Both do finetune sft. Category scores…", "facts": [ "Amazon Bedrock model customisation BB 75.8", "Vertex AI Gemini tuning B 64.2", "scores" ], "h1": "Amazon Bedrock model customisation vs Vertex AI Gemini tuning", "image": "https://www.anchorterminal.com/assets/og/compare-amazon-bedrock-customization-vs-vertex-ai-tuning.png", "path": "/compare/amazon-bedrock-customization-vs-vertex-ai-tuning", "published": "2026-10-01", "section": "tools", "title": "Amazon Bedrock model customisation vs Vertex AI Gemini tuning", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning" }, "tokens": { "markdown": 2550, "slim": 730 }, "version": 1 }