{ "data": { "a": { "slug": "amazon-bedrock-customization", "name": "Amazon Bedrock model customisation", "vendor": "Amazon Web Services", "vendorUrl": "https://aws.amazon.com/bedrock/", "kind": "http-api", "category": "fine-tuning", "summary": "Managed supervised fine-tuning, reinforcement fine-tuning and distillation of Amazon Nova, Meta Llama and selected open-weight models on Amazon Bedrock, run as asynchronous jobs through the Bedrock control-plane API, the AWS SDKs and CLI, or OpenAI-compatible endpoints.", "url": "https://www.anchorterminal.com/tools/amazon-bedrock-customization", "markdownUrl": "https://www.anchorterminal.com/tools/amazon-bedrock-customization.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/amazon-bedrock-customization.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/amazon-bedrock-customization.json", "transports": [ "http" ], "remoteUrl": "https://bedrock.{region}.amazonaws.com/model-customization-jobs", "packages": [ { "registry": "pypi", "name": "boto3" }, { "registry": "npm", "name": "@aws-sdk/client-bedrock" } ], "auth": "mixed", "authNotes": "AWS Signature Version 4 with IAM credentials or a role allowed to call `bedrock:CreateModelCustomizationJob`, plus `iam:PassRole` for a service role that Bedrock assumes to read training data from S3 and write output. Access is self-serve inside an AWS account. The OpenAI-compatible fine-tuning endpoints on `bedrock-mantle` also accept a Bedrock API key as a bearer token, short-term (up to 12 hours) or long-term, and reinforcement jobs need `lambda:InvokeFunction` on the reward function.", "pricing": "usage", "pricingNotes": "Supervised fine-tuning is billed per training token (dataset tokens times epochs). Nova Micro $1, Nova Lite $2, Nova 2 Lite $3.78 and Nova Pro $8 per 1M tokens, and Llama from $0.50 (3.2 1B) to $7.99 (3.1 70B). Reinforcement fine-tuning is $80 per training hour. Each custom model costs $1.95 a month to store. Inference is at base-model token prices where on-demand deployment is supported, otherwise Provisioned Throughput by the hour. No free tier for customisation appears on the pricing page. New AWS accounts get up to $200 in Free Tier credits, and there is no sandbox (https://aws.amazon.com/bedrock/pricing/).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the user guide, the API reference or the pricing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": 2810365, "pypiWeekly": 573748207, "asOf": "2026-10-08" }, "docsUrl": "https://docs.aws.amazon.com/bedrock/latest/userguide/custom-models.html", "llmsTxt": "https://docs.aws.amazon.com/bedrock/latest/userguide/llms.txt", "capabilities": [ "finetune.sft", "finetune.rl" ], "tags": [ "hosted", "usage-priced", "closed-source", "enterprise", "python", "typescript", "async-jobs", "llms-txt", "status-page", "soc2" ], "lastRelease": "2026-05-28", "graded": true, "anchor": { "graded": true, "score": 75.8, "grade": "BB", "agentReady": true, "rank": 39, "ranked": true, "rankOf": 842, "categoryRank": 1, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 77, "maintenance": 45, "payments": 30, "reliability": 95, "schema": 87, "security": 87, "transparency": 83 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "Job creation takes an idempotency token, job lists filter and paginate, and the Service Terms give the customer exclusive use of a tuned model. Jobs run in two US Regions only, weights can't be exported, and the newest customisation API change found dates from 28 May 2026.", "bestFor": "Teams already on AWS that want to tune Amazon Nova or Llama models, or run reinforcement fine-tuning with a Lambda reward function, and serve the result inside Bedrock.", "strengths": [ "`CreateModelCustomizationJob` accepts a `clientRequestToken`, so a repeated create after a timeout doesn't start a second job", "`ListModelCustomizationJobs` filters by status, name and creation time, sorts, and pages with `maxResults` up to 1,000 and `nextToken`", "Service Terms section 50.12.4 gives the customer exclusive use of a customised model and bars third-party model providers from accessing it", "Training prices are public per 1M tokens, from $0.50 for Llama 3.2 1B to $8 for Nova Pro, with storage at $1.95 a model a month", "Supervised tunes of Nova and Llama 3.3 70B deploy for on-demand, per-token inference at base-model prices, without Provisioned Throughput" ], "weaknesses": [ "Fine-tuning runs in us-east-1 and us-west-2 only, and each base model in one of them (two for the Titan models)", "No weight export was found in the reviewed documentation, and Service Terms section 50.11 forbids extracting model weights", "Setup needs an IAM service role, `iam:PassRole` and S3 buckets for input and output before the first job", "Reinforcement fine-tuning is billed at $80 a training hour and covers three models. Distillation is not available for Anthropic models", "The Bedrock SLA is dated 4 October 2023 and doesn't mention customisation jobs. No customisation launch appears in the latest 100 Bedrock What's New posts" ], "agentNotes": [ "Send a `clientRequestToken` with every `CreateModelCustomizationJob` call, and poll `GetModelCustomizationJob`. Jobs are asynchronous and can take hours", "Create the job in the Region that hosts the base model (Nova in us-east-1, Llama and Claude 3 Haiku in us-west-2), with the S3 bucket in the same Region", "Pass an IAM service role that trusts `bedrock.amazonaws.com` and can read the training data and write the output location. The caller needs `iam:PassRole`", "After a job completes, call `CreateCustomModelDeployment` and use the deployment ARN as `modelId`. Models outside the on-demand list need Provisioned Throughput", "For gpt-oss-20b and Qwen3 32B, use `/v1/fine_tuning/jobs` on `bedrock-mantle.us-west-2.api.aws` with a Bedrock API key and a Lambda grader ARN" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 75.8 } ], "editorialScores": { "ergonomics": 77, "maintenance": 45, "payments": 30, "reliability": 95, "schema": 87, "security": 87, "transparency": 77 }, "provenanceScore": 88 }, "connect": { "install": "pip install boto3 # or: npm i @aws-sdk/client-bedrock", "http": "curl -X POST \"https://bedrock.us-east-1.amazonaws.com/model-customization-jobs\" \\\n --aws-sigv4 \"aws:amz:us-east-1:bedrock\" --user \"$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY\" \\\n -H \"content-type: application/json\" \\\n -d '{\"jobName\":\"my-tune\",\"customModelName\":\"my-nova-tune\",\"roleArn\":\"arn:aws:iam::123456789012:role/BedrockCustomisationRole\",\"baseModelIdentifier\":\"amazon.nova-2-lite-v1:0:256k\",\"clientRequestToken\":\"my-tune-001\",\"trainingDataConfig\":{\"s3Uri\":\"s3://my-bucket/train.jsonl\"},\"outputDataConfig\":{\"s3Uri\":\"s3://my-bucket/output/\"}}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/amazon-bedrock-customization" }, "sameCompany": [ "agentcore-code-interpreter", "amazon-textract", "aws-end-user-messaging", "amazon-sns", "amazon-s3" ], "area": "models", "unitPrices": [ { "item": "Nova 2 Lite, supervised fine-tuning", "unit": "1m-tokens", "usd": 3.78, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Nova Pro, supervised fine-tuning", "unit": "1m-tokens", "usd": 8, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Nova Lite, supervised fine-tuning", "unit": "1m-tokens", "usd": 2, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Nova Micro, supervised fine-tuning", "unit": "1m-tokens", "usd": 1, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Llama 3.1 70B Instruct, fine-tuning", "unit": "1m-tokens", "usd": 7.99, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Llama 3.1 8B Instruct, fine-tuning", "unit": "1m-tokens", "usd": 1.49, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Llama 3.2 90B Instruct, fine-tuning", "unit": "1m-tokens", "usd": 7.9, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Llama 3.2 11B Instruct, fine-tuning", "unit": "1m-tokens", "usd": 3.5, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Llama 3.2 3B Instruct, fine-tuning", "unit": "1m-tokens", "usd": 1.1, "note": "Training tokens, counted as dataset tokens times epochs" }, { "item": "Llama 3.2 1B Instruct, fine-tuning", "unit": "1m-tokens", "usd": 0.5, "note": "Training tokens, counted as dataset tokens times epochs" } ], "provenance": { "legalEntity": "Amazon Web Services, Inc.", "domain": "amazonaws.com", "domainRegistered": "2005-08-18", "domainNote": "The control-plane endpoints are `bedrock.\u003cregion\u003e.amazonaws.com`. The OpenAI-compatible fine-tuning endpoints are on `bedrock-mantle.\u003cregion\u003e.api.aws`, a second AWS domain. The security.txt on aws.amazon.com passed its Expires date on 2026-09-24.", "endpointOnVendorDomain": true, "terms": "https://aws.amazon.com/service-terms/", "privacy": "https://aws.amazon.com/privacy/", "statusPage": "https://health.aws.amazon.com/health/status", "changelog": "https://docs.aws.amazon.com/bedrock/latest/userguide/doc-history.html", "securityTxt": "expired", "checked": "2026-10-08", "notes": [ "The AWS Service Terms (last updated 1 October 2026) hold the Bedrock clauses in section 50.12, including 50.12.4 on customised models. They sit under the AWS Customer Agreement at aws.amazon.com/agreement (last updated 14 August 2026), where the contracting party depends on the account country.", "The AWS Privacy Notice was last updated on 18 May 2026 and names Amazon Web Services, Inc., 410 Terry Avenue North, Seattle.", "RDAP for amazonaws.com gives a registration date of 2005-08-18, read on 8 October 2026.", "aws.amazon.com/.well-known/security.txt carries Expires 2026-09-24T16:25:03Z and was still expired on 8 October 2026. It points to vdp.aws.security and a HackerOne disclosure programme.", "The pricing page's customisation tables are filled by script. Token prices were read from the price file the page loads from b0.p.awsstatic.com." ], "score": 88 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/amazon-bedrock-customization.json", "live": { "slug": "amazon-bedrock-customization", "probe": { "target": "https://bedrock.{region}.amazonaws.com/model-customization-jobs", "method": "get", "lastAt": "2026-10-09T10:42:35.125049822Z", "lastOk": false, "lastStatus": 0, "lastMs": 0, "lastNote": "invalid character \"{\" in host name", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 33, "samples30d": 33, "days": [ { "date": "2026-10-09", "probes": 33, "ok": 0 } ], "outages": [ { "start": "2026-10-09T07:40:18.733946397Z", "end": "0001-01-01T00:00:00Z", "note": "invalid character \"{\" in host name" } ] }, "updatedAt": "2026-10-09T10:42:35.125049822Z" } }, "answer": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Axolotl's 64.8 (B), and leads in 5 of 7 scored categories. Axolotl leads on payments \u0026 pricing and maintenance \u0026 community.", "b": { "slug": "axolotl", "name": "Axolotl", "vendor": "Axolotl AI", "vendorUrl": "https://axolotl.ai", "kind": "framework", "category": "fine-tuning", "summary": "Open-source command-line tool and Python package for fine-tuning open language models from one YAML config, covering LoRA, QLoRA, full fine-tuning, preference tuning and GRPO on the owner's GPUs.", "url": "https://www.anchorterminal.com/tools/axolotl", "markdownUrl": "https://www.anchorterminal.com/tools/axolotl.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/axolotl.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/axolotl.json", "repo": "https://github.com/axolotl-ai-cloud/axolotl", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "pypi", "name": "axolotl" }, { "registry": "oci", "name": "axolotlai/axolotl" } ], "auth": "none", "authNotes": "No account or key of its own. Gated models and Hub uploads use the owner's Hugging Face token, and Weights \u0026 Biases, MLflow or Trackio logging uses those services' own credentials from the environment.", "pricing": "free", "pricingNotes": "Free and Apache-2.0, with no price list or hosted plan on axolotl.ai. The owner pays for the GPU, whether local, a rented machine or Hugging Face Jobs billed by the minute. Dedicated support is by email with no published price.", "priceSummary": "Free · OSS", "where": "library", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 12541, "npmWeekly": null, "pypiWeekly": 2124, "asOf": "2026-10-08" }, "docsUrl": "https://docs.axolotl.ai/", "capabilities": [ "finetune.sft", "finetune.lora", "finetune.preference", "finetune.rl", "finetune.export" ], "tags": [ "open-source", "framework", "self-hosted", "local", "free", "python", "docker", "open-weights" ], "lastRelease": "2026-09-30", "graded": true, "anchor": { "graded": true, "score": 64.8, "grade": "B", "agentReady": false, "rank": 307, "ranked": true, "rankOf": 842, "categoryRank": 2, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 60, "maintenance": 88, "payments": 60, "reliability": 64, "schema": 80, "security": 52, "transparency": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies the GPU.", "bestFor": "A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.", "strengths": [ "Apache-2.0, free, and the weights stay on the owner's hardware", "`axolotl config-schema` prints the full config as JSON Schema, and `axolotl agent-docs` prints bundled Markdown references by topic", "SFT, LoRA, QLoRA, DPO, IPO, KTO, ORPO, GRPO and reward modelling from one config format", "Three releases in the 90 days to 8 October 2026, each with a Deprecations section naming removed options", "Telemetry is documented field by field and `AXOLOTL_DO_NOT_TRACK=1` turns it off" ], "weaknesses": [ "Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way", "No terms of service, privacy policy, legal entity or security.txt found on axolotl.ai", "Version 0.20.0, with removals in minor releases (FSDP1 in 0.20.0, `relora_steps` renamed in 0.17.0 with no shim)", "Three of the last six push runs of the Tests workflow on main passed, and the nightly run against upstream failed on 7 and 8 October 2026", "Not a hosted service, so there is no job API, status page or SLA" ], "agentNotes": [ "Set `AXOLOTL_DO_NOT_TRACK=1` before any command, or training waits 10 seconds and sends usage events to PostHog", "Run `axolotl agent-docs` and `axolotl config-schema --field \u003cname\u003e` before writing a config; both work offline from the installed package", "Install torch first, then `uv pip install --no-build-isolation axolotl[deepspeed]`, on Python 3.12 or later with PyTorch 2.13 or later", "Take example configs from the same release tag as the installed version; minor releases remove and rename config keys", "Resume an interrupted run with `axolotl train config.yml --resume-from-checkpoint \u003cpath\u003e`, then `axolotl merge-lora` and `axolotl export` only when shipping" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.8 } ], "editorialScores": { "ergonomics": 60, "maintenance": 88, "payments": 60, "reliability": 64, "schema": 80, "security": 52, "transparency": 75 }, "provenanceScore": 36 }, "connect": { "install": "uv pip install torch==2.14.0 torchvision \u0026\u0026 uv pip install --no-build-isolation axolotl[deepspeed] # or: docker run --gpus '\"all\"' --ipc=host --rm -it axolotlai/axolotl:main-latest", "headless": { "command": "axolotl train config.yml", "env": { "AXOLOTL_DO_NOT_TRACK": "1" } } }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/axolotl" }, "area": "models", "provenance": { "legalEntity": "", "domain": "axolotl.ai", "domainRegistered": "2022-08-02", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/axolotl-ai-cloud/axolotl/releases", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "No legal entity is named on axolotl.ai, in the docs or in the repository. The GitHub organisation is axolotl-ai-cloud and the citation file credits the Axolotl maintainers and contributors.", "The vendor publishes no terms of service or privacy policy (axolotl.ai/terms and /privacy return 404), so both links are left out. The telemetry page is the only data-handling statement.", "Local software has no endpoint to check against the domain.", "axolotl.ai/.well-known/security.txt and /security.txt return 404. The repository's `.github/SECURITY.md` gives an email address for reports.", "RDAP shows axolotl.ai registered on 2 August 2022 and transferred on 11 April 2024." ], "score": 36 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/axolotl.json" }, "facts": [ { "a": "HTTP API", "b": "Agent framework", "name": "Kind" }, { "a": "Amazon Web Services", "b": "Axolotl AI", "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-05-28", "b": "2026-09-30", "name": "Last release" }, { "a": "2026-10-01", "b": "no document linked", "name": "Terms last updated" }, { "a": "2026-05-18", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "", "name": "Customer content may train models" }, { "a": "yes", "b": "", "name": "Terms restrict automated access" }, { "a": "yes", "b": "", "name": "Terms restrict benchmarking" }, { "a": "yes", "b": "", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.8M npm/wk, 573.7M PyPI/wk", "b": "13k stars, 2.1k PyPI/wk", "name": "Popularity" } ], "faq": [ { "answer": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Axolotl's 64.8 (B), and leads in 5 of 7 scored categories. Axolotl leads on payments \u0026 pricing and maintenance \u0026 community.", "question": "Which is better for AI agents, Amazon Bedrock model customisation or Axolotl?" }, { "answer": "Amazon Bedrock model customisation has a hosted endpoint at https://bedrock.{region}.amazonaws.com/model-customization-jobs. No hosted endpoint is listed for Axolotl.", "question": "Can an agent call Amazon Bedrock model customisation and Axolotl without installing anything?" }, { "answer": "No open-source release is listed for Amazon Bedrock model customisation. Axolotl is open source (Apache-2.0).", "question": "Are Amazon Bedrock model customisation and Axolotl open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 95 against 64", "Schema \u0026 documentation, 87 against 80", "Agent ergonomics, 77 against 60", "Security \u0026 auth, 87 against 52", "Transparency \u0026 trust, 83 against 56" ], "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, 88 against 45" ], "also": [ "No key needed to call it", "Open source" ], "goodFor": "A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.", "slug": "axolotl", "watchFor": "Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way" } ], "job": { "capability": "finetune.sft", "name": "Finetune sft" }, "others": [ { "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/amazon-bedrock-customization-vs-vertex-ai-tuning.json", "title": "Amazon Bedrock model customisation vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.json", "title": "Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI)", "url": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.json", "title": "Axolotl vs Fireworks AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.json", "title": "Axolotl vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-tinker.json", "title": "Axolotl vs Tinker", "url": "https://www.anchorterminal.com/compare/axolotl-vs-tinker" }, { "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/axolotl-vs-unsloth.json", "title": "Axolotl vs Unsloth", "url": "https://www.anchorterminal.com/compare/axolotl-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" } ], "scores": [ { "amazon-bedrock-customization": 95, "axolotl": 64, "by": 31, "edge": "amazon-bedrock-customization", "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "amazon-bedrock-customization": 87, "axolotl": 80, "by": 7, "edge": "amazon-bedrock-customization", "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "amazon-bedrock-customization": 77, "axolotl": 60, "by": 17, "edge": "amazon-bedrock-customization", "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "amazon-bedrock-customization": 87, "axolotl": 52, "by": 35, "edge": "amazon-bedrock-customization", "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "amazon-bedrock-customization": 30, "axolotl": 60, "by": 30, "edge": "axolotl", "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "amazon-bedrock-customization": 45, "axolotl": 88, "by": 43, "edge": "axolotl", "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "amazon-bedrock-customization": 83, "axolotl": 56, "by": 27, "edge": "amazon-bedrock-customization", "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Axolotl's 64.8 (B), and leads in 5 of 7 scored categories. Axolotl 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.", "axolotl": "Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies 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-axolotl", "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.md", "slim": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.min.md" }, "markdown": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Axolotl's 64.8 (B), and leads in 5 of 7 scored categories. Axolotl 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- Axolotl: grade B, 64.8/100, rank #307 of 842. Markdown https://www.anchorterminal.com/tools/axolotl.md · JSON https://www.anchorterminal.com/api/v1/tools/axolotl.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 64\n- Schema \u0026 documentation, 87 against 80\n- Agent ergonomics, 77 against 60\n- Security \u0026 auth, 87 against 52\n- Transparency \u0026 trust, 83 against 56\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### Axolotl (B)\n\nGood for: A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.\n\nAhead on:\n- Payments \u0026 pricing, 60 against 30\n- Maintenance \u0026 community, 88 against 45\n\nAlso in its favour:\n- No key needed to call it\n- Open source\n\nWatch for: Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way\n\n\n## Score by category\n\n| Category | Weight | Amazon Bedrock model customisation | Axolotl | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 95 | 64 | Amazon Bedrock model customisation +31 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 87 | 80 | Amazon Bedrock model customisation +7 |\n| Agent ergonomics | 13% (16.2 this run) | 77 | 60 | Amazon Bedrock model customisation +17 |\n| Security \u0026 auth | 14% (17.5 this run) | 87 | 52 | Amazon Bedrock model customisation +35 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 60 | Axolotl +30 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 45 | 88 | Axolotl +43 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 83 | 56 | Amazon Bedrock model customisation +27 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **75.8 · BB** | **64.8 · B** | |\n\n## Facts side by side\n\n| Fact | Amazon Bedrock model customisation | Axolotl |\n| --- | --- | --- |\n| Kind | HTTP API | Agent framework |\n| Vendor | Amazon Web Services | Axolotl AI |\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 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-05-28 | 2026-09-30 |\n| Terms last updated | 2026-10-01 | no document linked |\n| Privacy policy last updated | 2026-05-18 | no document linked |\n| Customer content may train models | yes, with an opt-out | |\n| Terms restrict automated access | yes | |\n| Terms restrict benchmarking | yes | |\n| Terms or service can change without notice | yes | |\n| Arbitration or class-action waiver | not found in the text | |\n| Popularity | 2.8M npm/wk, 573.7M PyPI/wk | 13k stars, 2.1k PyPI/wk |\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**Axolotl.** Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies 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### Axolotl\n\n1. Set `AXOLOTL_DO_NOT_TRACK=1` before any command, or training waits 10 seconds and sends usage events to PostHog\n2. Run `axolotl agent-docs` and `axolotl config-schema --field \u003cname\u003e` before writing a config; both work offline from the installed package\n3. Install torch first, then `uv pip install --no-build-isolation axolotl[deepspeed]`, on Python 3.12 or later with PyTorch 2.13 or later\n4. Take example configs from the same release tag as the installed version; minor releases remove and rename config keys\n5. Resume an interrupted run with `axolotl train config.yml --resume-from-checkpoint \u003cpath\u003e`, then `axolotl merge-lora` and `axolotl export` only when shipping\n\n## Questions\n\n### Which is better for AI agents, Amazon Bedrock model customisation or Axolotl?\n\nAmazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Axolotl's 64.8 (B), and leads in 5 of 7 scored categories. Axolotl leads on payments \u0026 pricing and maintenance \u0026 community.\n\n### Can an agent call Amazon Bedrock model customisation and Axolotl 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 Axolotl.\n\n### Are Amazon Bedrock model customisation and Axolotl open source?\n\nNo open-source release is listed for Amazon Bedrock model customisation. Axolotl is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.json, and with the fewest tokens: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"amazon-bedrock-customization\", \"b\": \"axolotl\"}`. From a terminal: `anchor compare amazon-bedrock-customization axolotl`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/amazon-bedrock-customization.json and https://www.anchorterminal.com/api/v1/tools/axolotl.json\n\n## Other comparisons with Amazon Bedrock model customisation or Axolotl\n\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- [Amazon Bedrock model customisation vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning.md)\n- [Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI)](https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.md)\n- [Axolotl vs Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.md)\n- [Axolotl vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.md)\n- [Axolotl vs Tinker](https://www.anchorterminal.com/compare/axolotl-vs-tinker.md)\n- [Axolotl vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-together-fine-tuning.md)\n- [Axolotl vs Unsloth](https://www.anchorterminal.com/compare/axolotl-vs-unsloth.md)\n- [Axolotl vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/axolotl-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 Axolotl", "url": "" } ], "description": "Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Axolotl's 64.8 (B), and leads in 5 of 7 scored categories. Axolotl 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", "Axolotl B 64.8", "scores" ], "h1": "Amazon Bedrock model customisation vs Axolotl", "image": "https://www.anchorterminal.com/assets/og/compare-amazon-bedrock-customization-vs-axolotl.png", "path": "/compare/amazon-bedrock-customization-vs-axolotl", "published": "2026-10-01", "section": "tools", "title": "Amazon Bedrock model customisation vs Axolotl for AI agents", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl" }, "tokens": { "markdown": 2450, "slim": 780 }, "version": 1 }