{ "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 Unsloth's 51.5 (D), and leads in 5 of 7 scored categories. Unsloth leads on payments \u0026 pricing and maintenance \u0026 community.", "b": { "slug": "unsloth", "name": "Unsloth", "vendor": "Unsloth", "vendorUrl": "https://unsloth.ai", "kind": "framework", "category": "fine-tuning", "summary": "Open-source library, web UI (Studio) and desktop app for LoRA, QLoRA, full fine-tuning and RL (GRPO, DPO, ORPO) of open models on your own GPU, from 3 GB of VRAM.", "url": "https://www.anchorterminal.com/tools/unsloth", "markdownUrl": "https://www.anchorterminal.com/tools/unsloth.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/unsloth.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/unsloth.json", "repo": "https://github.com/unslothai/unsloth", "license": "Apache-2.0 (core), AGPL-3.0 (Studio UI)", "transports": [], "packages": [ { "registry": "pypi", "name": "unsloth" } ], "auth": "none", "authNotes": "No account. Studio asks for an admin password when exposed beyond loopback (`--secure`, `--cloudflare` or a non-loopback host), and hands out API keys for its OpenAI-compatible server under Settings.", "pricing": "free", "pricingNotes": "Free and open source. You pay for the GPU it runs on, whether a free Colab or Kaggle notebook, your own card or a rented one. Docker images `unsloth/unsloth` and `unsloth/unsloth-rocm` on Docker Hub. No hosted plan or price list appears on the site or in the docs index (https://unsloth.ai/docs).", "priceSummary": "Free · OSS", "where": "library", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 76900, "npmWeekly": null, "pypiWeekly": 230075, "asOf": "2026-09-30" }, "docsUrl": "https://unsloth.ai/docs", "llmsTxt": "https://unsloth.ai/docs/llms.txt", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.rl", "finetune.lora", "finetune.export" ], "tags": [ "open-source", "framework", "self-hosted", "local", "free", "python", "llms-txt", "open-weights" ], "lastRelease": "2026-09-28", "graded": true, "anchor": { "graded": true, "score": 51.5, "grade": "D", "agentReady": false, "rank": 668, "ranked": true, "rankOf": 842, "categoryRank": 7, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 53, "maintenance": 82, "payments": 60, "reliability": 43, "schema": 66, "security": 35, "transparency": 32 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.", "bestFor": "One person or a small team tuning an open model on their own GPU and keeping the weights.", "strengths": [ "Free and open source, Apache-2.0 core, with the weights staying on your hardware", "LoRA, QLoRA, full fine-tuning, GRPO, DPO and ORPO from one package", "Exports adapters, merged 16-bit weights and GGUF for vLLM, Ollama or llama.cpp", "Fifteen PyPI releases between 25 August and 28 September 2026", "llms.txt and over 100 model-specific notebooks" ], "weaknesses": [ "Not a hosted service; you bring and pay for the GPU", "Studio is AGPL-3.0, and its server-side tools are on by default when exposed", "792 open issues and 472 open pull requests", "No legal entity in the terms, no privacy page and no security.txt", "Calendar versions with no breaking-change notes and no deprecation policy" ], "agentNotes": [ "Install with `uv pip install unsloth --torch-backend=auto` on a CUDA machine; the desktop app is for people", "Start from the notebook for the model family in unslothai/notebooks; it sets LoRA targets and the chat template", "Save the LoRA adapter while iterating and merge to 16-bit or GGUF only when you ship", "If Studio must be reachable by other agents, pass --disable-tools and keep it on 127.0.0.1 behind a tunnel", "Pin the exact unsloth version; releases land several times a week and don't flag breaking changes" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.5 } ], "editorialScores": { "ergonomics": 53, "maintenance": 82, "payments": 60, "reliability": 43, "schema": 66, "security": 35, "transparency": 40 }, "provenanceScore": 23 }, "connect": { "install": "curl -fsSL https://unsloth.ai/install.sh | sh # or: uv pip install unsloth --torch-backend=auto" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/unsloth" }, "area": "models", "provenance": { "legalEntity": "", "domain": "unsloth.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://unsloth.ai/terms", "privacy": "", "statusPage": "", "changelog": "https://github.com/unslothai/unsloth/releases", "securityTxt": "none", "checked": "2026-09-30", "notes": [ "The terms page names no company, address or date, and unsloth.ai/privacy returns 404. Copyright notices in the source credit Daniel Han-Chen and the Unsloth team.", "A local library has no endpoint to check against the domain.", "unsloth.ai/.well-known/security.txt returns 404.", "The .ai registry's RDAP server refused our requests, so the registration date is blank.", "lastRelease is blank because releases are versioned by date (2026.9.12) and we didn't confirm the tag date; the last commit was 2026-09-30." ], "score": 23 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/unsloth.json", "live": { "slug": "unsloth", "versions": [ { "registry": "github", "name": "unslothai/unsloth", "version": "v0.1.905-beta", "released": "2026-10-08", "seenAt": "2026-10-08T16:33:23.101894009Z" }, { "registry": "pypi", "name": "unsloth", "version": "2026.10.3", "released": "2026-10-08", "seenAt": "2026-10-08T16:33:22.984821349Z" } ], "githubStars": 77487, "pypiWeekly": 177890, "securityTxt": { "url": "https://unsloth.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:38:59.33731935Z" }, "llmsTxt": { "url": "https://unsloth.ai/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:57.572131254Z" }, "domain": { "domain": "unsloth.ai", "registered": "2023-11-27", "source": "https://rdap.identitydigital.services/rdap/domain/unsloth.ai", "checkedAt": "2026-10-04T13:08:02.898044488Z" }, "pages": [ { "url": "https://unsloth.ai/terms", "kind": "terms", "status": 404, "checkedAt": "2026-10-08T18:25:27.328582164Z", "changedAt": "0001-01-01T00:00:00Z" } ], "updatedAt": "2026-10-08T18:25:27.328582164Z" } }, "facts": [ { "a": "HTTP API", "b": "Agent framework", "name": "Kind" }, { "a": "Amazon Web Services", "b": "Unsloth", "name": "Vendor" }, { "a": "https://bedrock.{region}.amazonaws.com/model-customization-jobs", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "", "name": "Transports" }, { "a": "OAuth or key", "b": "None", "name": "Auth" }, { "a": "Pay per use", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "none", "b": "Apache-2.0 (core), AGPL-3.0 (Studio UI)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-05-28", "b": "2026-09-28", "name": "Last release" }, { "a": "2026-10-01", "b": "couldn't be read", "name": "Terms last updated" }, { "a": "2026-05-18", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "couldn't be read", "name": "Customer content may train models" }, { "a": "yes", "b": "couldn't be read", "name": "Terms restrict automated access" }, { "a": "yes", "b": "couldn't be read", "name": "Terms restrict benchmarking" }, { "a": "yes", "b": "couldn't be read", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Arbitration or class-action waiver" }, { "a": "2.8M npm/wk, 573.7M PyPI/wk", "b": "77k stars, 230k 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 Unsloth's 51.5 (D), and leads in 5 of 7 scored categories. Unsloth leads on payments \u0026 pricing and maintenance \u0026 community.", "question": "Which is better for AI agents, Amazon Bedrock model customisation or Unsloth?" }, { "answer": "Amazon Bedrock model customisation has a hosted endpoint at https://bedrock.{region}.amazonaws.com/model-customization-jobs. No hosted endpoint is listed for Unsloth.", "question": "Can an agent call Amazon Bedrock model customisation and Unsloth without installing anything?" }, { "answer": "No open-source release is listed for Amazon Bedrock model customisation. Unsloth is open source (Apache-2.0 (core), AGPL-3.0 (Studio UI)).", "question": "Are Amazon Bedrock model customisation and Unsloth open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 95 against 43", "Schema \u0026 documentation, 87 against 66", "Agent ergonomics, 77 against 53", "Security \u0026 auth, 87 against 35", "Transparency \u0026 trust, 83 against 32" ], "also": [ "Agent-ready, a grade of BB or better", "A hosted endpoint, with nothing to install" ], "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": [ "Payments \u0026 pricing, 60 against 30", "Maintenance \u0026 community, 82 against 45" ], "also": [ "No key needed to call it", "Open source" ], "goodFor": "One person or a small team tuning an open model on their own GPU and keeping the weights.", "slug": "unsloth", "watchFor": "Not a hosted service; you bring and pay for the GPU" } ], "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-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-unsloth.json", "title": "Axolotl vs Unsloth", "url": "https://www.anchorterminal.com/compare/axolotl-vs-unsloth" }, { "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/fireworks-fine-tuning-vs-unsloth.json", "title": "Fireworks AI Fine-tuning vs Unsloth", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-unsloth.json", "title": "Nebius Token Factory fine-tuning vs Unsloth", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/tinker-vs-unsloth.json", "title": "Tinker vs Unsloth", "url": "https://www.anchorterminal.com/compare/tinker-vs-unsloth" }, { "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/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": 52, "edge": "amazon-bedrock-customization", "key": "reliability", "name": "Reliability", "unsloth": 43, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "amazon-bedrock-customization": 87, "by": 21, "edge": "amazon-bedrock-customization", "key": "schema", "name": "Schema \u0026 documentation", "unsloth": 66, "weight": 13 }, { "amazon-bedrock-customization": 77, "by": 24, "edge": "amazon-bedrock-customization", "key": "ergonomics", "name": "Agent ergonomics", "unsloth": 53, "weight": 13 }, { "amazon-bedrock-customization": 87, "by": 52, "edge": "amazon-bedrock-customization", "key": "security", "name": "Security \u0026 auth", "unsloth": 35, "weight": 14 }, { "amazon-bedrock-customization": 30, "by": 30, "edge": "unsloth", "key": "payments", "name": "Payments \u0026 pricing", "unsloth": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "amazon-bedrock-customization": 45, "by": 37, "edge": "unsloth", "key": "maintenance", "name": "Maintenance \u0026 community", "unsloth": 82, "weight": 7 }, { "amazon-bedrock-customization": 83, "by": 51, "edge": "amazon-bedrock-customization", "key": "transparency", "name": "Transparency \u0026 trust", "unsloth": 32, "weight": 7 } ], "summary": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Unsloth's 51.5 (D), and leads in 5 of 7 scored categories. Unsloth leads on payments \u0026 pricing and 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.", "unsloth": "The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-unsloth", "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-unsloth.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-unsloth.md", "slim": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-unsloth.min.md" }, "markdown": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Unsloth's 51.5 (D), and leads in 5 of 7 scored categories. Unsloth leads on payments \u0026 pricing and 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- Unsloth: grade D, 51.5/100, rank #668 of 842. Markdown https://www.anchorterminal.com/tools/unsloth.md · JSON https://www.anchorterminal.com/api/v1/tools/unsloth.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 43\n- Schema \u0026 documentation, 87 against 66\n- Agent ergonomics, 77 against 53\n- Security \u0026 auth, 87 against 35\n- Transparency \u0026 trust, 83 against 32\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- A hosted endpoint, with nothing to install\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### Unsloth (D)\n\nGood for: One person or a small team tuning an open model on their own GPU and keeping the weights.\n\nAhead on:\n- Payments \u0026 pricing, 60 against 30\n- Maintenance \u0026 community, 82 against 45\n\nAlso in its favour:\n- No key needed to call it\n- Open source\n\nWatch for: Not a hosted service; you bring and pay for the GPU\n\n\n## Score by category\n\n| Category | Weight | Amazon Bedrock model customisation | Unsloth | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 95 | 43 | Amazon Bedrock model customisation +52 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 87 | 66 | Amazon Bedrock model customisation +21 |\n| Agent ergonomics | 13% (16.2 this run) | 77 | 53 | Amazon Bedrock model customisation +24 |\n| Security \u0026 auth | 14% (17.5 this run) | 87 | 35 | Amazon Bedrock model customisation +52 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 60 | Unsloth +30 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 45 | 82 | Unsloth +37 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 83 | 32 | Amazon Bedrock model customisation +51 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **75.8 · BB** | **51.5 · D** | |\n\n## Facts side by side\n\n| Fact | Amazon Bedrock model customisation | Unsloth |\n| --- | --- | --- |\n| Kind | HTTP API | Agent framework |\n| Vendor | Amazon Web Services | Unsloth |\n| Hosted endpoint | `https://bedrock.{region}.amazonaws.com/model-customization-jobs` | no (local only) |\n| Transports | HTTP | |\n| Auth | OAuth or key | None |\n| Pricing | Pay per use | Free |\n| x402 | no | no |\n| Licence | none | Apache-2.0 (core), AGPL-3.0 (Studio UI) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-05-28 | 2026-09-28 |\n| Terms last updated | 2026-10-01 | couldn't be read |\n| Privacy policy last updated | 2026-05-18 | no document linked |\n| Customer content may train models | yes, with an opt-out | couldn't be read |\n| Terms restrict automated access | yes | couldn't be read |\n| Terms restrict benchmarking | yes | couldn't be read |\n| Terms or service can change without notice | yes | couldn't be read |\n| Arbitration or class-action waiver | not found in the text | couldn't be read |\n| Popularity | 2.8M npm/wk, 573.7M PyPI/wk | 77k stars, 230k 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**Unsloth.** The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.\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### Unsloth\n\n1. Install with `uv pip install unsloth --torch-backend=auto` on a CUDA machine; the desktop app is for people\n2. Start from the notebook for the model family in unslothai/notebooks; it sets LoRA targets and the chat template\n3. Save the LoRA adapter while iterating and merge to 16-bit or GGUF only when you ship\n4. If Studio must be reachable by other agents, pass --disable-tools and keep it on 127.0.0.1 behind a tunnel\n5. Pin the exact unsloth version; releases land several times a week and don't flag breaking changes\n\n## Questions\n\n### Which is better for AI agents, Amazon Bedrock model customisation or Unsloth?\n\nAmazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Unsloth's 51.5 (D), and leads in 5 of 7 scored categories. Unsloth leads on payments \u0026 pricing and maintenance \u0026 community.\n\n### Can an agent call Amazon Bedrock model customisation and Unsloth without installing anything?\n\nAmazon Bedrock model customisation has a hosted endpoint at https://bedrock.{region}.amazonaws.com/model-customization-jobs. No hosted endpoint is listed for Unsloth.\n\n### Are Amazon Bedrock model customisation and Unsloth open source?\n\nNo open-source release is listed for Amazon Bedrock model customisation. Unsloth is open source (Apache-2.0 (core), AGPL-3.0 (Studio UI)).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-unsloth.json, and with the fewest tokens: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-unsloth.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"amazon-bedrock-customization\", \"b\": \"unsloth\"}`. From a terminal: `anchor compare amazon-bedrock-customization unsloth`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/amazon-bedrock-customization.json and https://www.anchorterminal.com/api/v1/tools/unsloth.json\n\n## Other comparisons with Amazon Bedrock model customisation or Unsloth\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 Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning.md)\n- [Axolotl vs Unsloth](https://www.anchorterminal.com/compare/axolotl-vs-unsloth.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Unsloth](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-unsloth.md)\n- [Fireworks AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.md)\n- [Nebius Token Factory fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-unsloth.md)\n- [Tinker vs Unsloth](https://www.anchorterminal.com/compare/tinker-vs-unsloth.md)\n- [Together AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.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 Unsloth", "url": "" } ], "description": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Unsloth's 51.5 (D), and leads in 5 of 7 scored categories. Unsloth leads on payments \u0026 pricing and maintenance \u0026 community. Both do finetune sft. Category scores, facts, verdicts and agent notes side…", "facts": [ "Amazon Bedrock model customisation BB 75.8", "Unsloth D 51.5", "scores" ], "h1": "Amazon Bedrock model customisation vs Unsloth", "image": "https://www.anchorterminal.com/assets/og/compare-amazon-bedrock-customization-vs-unsloth.png", "path": "/compare/amazon-bedrock-customization-vs-unsloth", "published": "2026-10-01", "section": "tools", "title": "Amazon Bedrock model customisation vs Unsloth for AI agents", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-unsloth" }, "tokens": { "markdown": 2400, "slim": 780 }, "version": 1 }