{ "data": { "a": { "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" }, "answer": "Axolotl scores 64.8 (B) on agent readiness against Microsoft Foundry fine-tuning (Azure OpenAI)'s 61.1 (C), and leads in 4 of 7 scored categories. Microsoft Foundry fine-tuning (Azure OpenAI) leads on security \u0026 auth and transparency \u0026 trust.", "b": { "slug": "azure-foundry-fine-tuning", "name": "Microsoft Foundry fine-tuning (Azure OpenAI)", "vendor": "Microsoft Azure", "vendorUrl": "https://azure.microsoft.com", "kind": "http-api", "category": "fine-tuning", "summary": "Azure's managed service for supervised, preference and reinforcement fine-tuning of supported OpenAI and open-weight models.", "url": "https://www.anchorterminal.com/tools/azure-foundry-fine-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/azure-foundry-fine-tuning.json", "transports": [ "http" ], "remoteUrl": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "packages": [ { "registry": "pypi", "name": "openai" }, { "registry": "npm", "name": "openai" } ], "auth": "mixed", "authNotes": "`api-key` header with a resource key, or a Microsoft Entra ID bearer token. Training a model needs the Foundry User role and deploying it needs Foundry Owner (renamed from Azure AI User and Azure AI Owner). Deployments are created through the Azure Resource Manager API at management.azure.com, a second credential.", "pricing": "usage", "pricingNotes": "SFT and DPO bill training tokens x epochs at a per-model rate. The Azure Retail Prices API lists, per 1M training tokens, gpt-4.1 at $25 global and $30.25 regional, gpt-4.1-mini at $5 and $6.05, and gpt-4.1-nano at $1.50 and $1.815 (regional is 21 per cent above global). RFT bills training hours plus grader tokens; the cost guide's example uses $100 an hour for o4-mini and jobs pause at $5,000. The developer tier is 50 per cent below global on pre-emptible capacity, without data residency. A fine-tuned model on a Standard or Global Standard deployment costs $1.70 an hour to host plus per-token inference (gpt-4.1-ft $2 input and $8 output per 1M, global); developer deployments have no hosting fee and are deleted after 24 hours (https://prices.azure.com/api/retail/prices, https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/fine-tuning-cost-management).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": 47155661, "pypiWeekly": 72103251, "asOf": "2026-09-30" }, "docsUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/fine-tuning", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.rl", "finetune.lora" ], "tags": [ "hosted", "usage-priced", "closed-source", "card-required", "enterprise", "eu", "python", "typescript", "async-jobs" ], "graded": true, "anchor": { "graded": true, "score": 61.1, "grade": "C", "agentReady": false, "rank": 434, "ranked": true, "rankOf": 842, "categoryRank": 4, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 47, "maintenance": 55, "payments": 20, "reliability": 65, "schema": 67, "security": 85, "transparency": 84 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "SFT, DPO and RFT on GPT-4.1 and o4-mini through the OpenAI-shaped /openai/v1 API. No weight export; checkpoints copy only between Azure resources.", "bestFor": "Teams that must tune an OpenAI model, need Azure's compliance and regional controls, and will serve the result on Azure.", "strengths": [ "SFT, DPO and RFT on GPT-4.1 and o4-mini through the OpenAI-shaped /openai/v1 API", "Retirement policy with 60 days' notice and published training and deployment retirement dates per tunable model", "Entra ID with RBAC, Azure Monitor logs and an activity log for every customer", "Training files and tuned models stay in the resource's geography, are deletable and exclusive to the customer", "Fine-tuning limits published with numbers, from 3 concurrent jobs to 2 billion tokens per job" ], "weaknesses": [ "No weight export; checkpoints copy only between Azure resources", "$1.70 an hour hosting on Standard deployments, and deletion after 15 idle days", "GPT-4.1 training at $25 per 1M tokens globally, and no free tier without a card", "Deployment goes through management.azure.com with a separate credential and the Foundry Owner role", "The Azure OpenAI 'what's new' page hasn't had a dated section since May 2026" ], "agentNotes": [ "Point the OpenAI SDK at https://\u003cresource\u003e.openai.azure.com/openai/v1 with the `api-key` header or an Entra token; job, file and checkpoint calls are the OpenAI shapes", "Read prices from the Azure Retail Prices API (meters named like 'gpt-4.1 FT Training global'), not the pricing page, which needs a browser", "Keep at most 3 jobs running and 20 queued per resource, and keep training files under 512 MB and 1 GB in total", "Create the deployment through the Resource Manager API with a Foundry Owner identity, then call it at least once a fortnight or it's deleted", "Query the Models API for `deprecationDate` before choosing a base model" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.1 } ], "editorialScores": { "ergonomics": 47, "maintenance": 55, "payments": 20, "reliability": 65, "schema": 67, "security": 85, "transparency": 81 }, "provenanceScore": 86 }, "connect": { "install": "pip install openai # or: npm i openai", "http": "curl \"https://$AZURE_OPENAI_RESOURCE.openai.azure.com/openai/v1/fine_tuning/jobs\" \\\n -H \"api-key: $AZURE_OPENAI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"gpt-4.1-2025-04-14\",\"training_file\":\"file-abc123\",\"seed\":105}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/azure-foundry-fine-tuning" }, "sameCompany": [ "azure-ai-content-safety", "azure-speech-to-text", "azure-text-to-speech", "microsoft-agent-framework", "microsoft-execution-containers", "microsoft-entra-agent-id", "azure-key-vault", "azure-document-intelligence", "azure-devops-mcp", "microsoft-learn-mcp", "playwright-mcp", "azure-mcp", "azure-maps", "azure-translator", "microsoft-graph-calendar", "azure-blob-storage", "onedrive-sharepoint", "microsoft-teams", "dynamics-365-sales", "power-automate", "foundry-local", "microsoft-advertising-api", "microsoft-excel-graph", "outlook-mail-graph" ], "area": "models", "provenance": { "legalEntity": "Microsoft Corporation", "domain": "microsoft.com", "domainRegistered": "1991-05-02", "domainNote": "Endpoints are on openai.azure.com and management.azure.com. microsoft.com publishes a security.txt, but it passed its Expires date on 2026-09-23.", "endpointOnVendorDomain": true, "terms": "https://www.microsoft.com/licensing/terms/product/ForOnlineServices/all", "privacy": "https://privacy.microsoft.com/en-us/privacystatement", "statusPage": "https://azure.status.microsoft/en-us/status", "changelog": "https://learn.microsoft.com/en-us/azure/ai-foundry/whats-new-foundry", "securityTxt": "expired", "checked": "2026-09-30", "notes": [ "Entity, domain, privacy statement, status page and security.txt are the same as the azure-speech-to-text listing; the terms link here is the Product Terms for online services, which hold the generative AI clause.", "The Azure status page lists Azure OpenAI Service, Foundry Agent Service and Foundry Models as components.", "The npm and PyPI figures are for the openai package as a whole, which Azure customers share with OpenAI's own API; there's no Azure-only SDK to count.", "Docs facts were read from the MicrosoftDocs/azure-ai-docs repository (articles/foundry/openai, updated 2026-09-30) because Learn pages are long; the live how-to page confirms the model table and roles." ], "score": 86 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.json", "live": { "slug": "azure-foundry-fine-tuning", "probe": { "target": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "method": "get", "lastAt": "2026-10-09T11:46:22.713747879Z", "lastOk": false, "lastStatus": 0, "lastMs": 0, "lastNote": "DNS lookup failed", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 259, "samples30d": 2109, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 0 }, { "date": "2026-10-02", "probes": 248, "ok": 0 }, { "date": "2026-10-03", "probes": 271, "ok": 0 }, { "date": "2026-10-04", "probes": 272, "ok": 0 }, { "date": "2026-10-05", "probes": 272, "ok": 0 }, { "date": "2026-10-06", "probes": 272, "ok": 0 }, { "date": "2026-10-07", "probes": 272, "ok": 0 }, { "date": "2026-10-08", "probes": 268, "ok": 0 }, { "date": "2026-10-09", "probes": 125, "ok": 0 } ] }, "vendorStatus": { "page": "https://azure.status.microsoft/en-us/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-08T10:02:53.28378098Z" }, "versions": [ { "registry": "npm", "name": "openai", "version": "7.30.1", "seenAt": "2026-10-08T16:00:58.639202123Z" }, { "registry": "pypi", "name": "openai", "version": "3.26.1", "released": "2026-10-08", "seenAt": "2026-10-08T16:00:55.524523612Z" } ], "npmWeekly": 50858207, "pypiWeekly": 74231726, "securityTxt": { "url": "https://microsoft.com/.well-known/security.txt", "state": "expired", "expires": "2026-09-23T16:00:00.000Z", "checkedAt": "2026-10-08T15:39:08.216544687Z" }, "domain": { "domain": "microsoft.com", "registered": "1991-05-02", "source": "https://rdap.verisign.com/com/v1/domain/microsoft.com", "checkedAt": "2026-10-04T13:04:13.488857536Z" }, "pages": [ { "url": "https://learn.microsoft.com/en-us/azure/ai-foundry/whats-new-foundry", "kind": "changelog", "status": 304, "checkedAt": "2026-10-08T18:21:22.172079422Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "237edf8365ef" }, { "url": "https://prices.azure.com/api/retail/prices", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:23:18.650260122Z", "changedAt": "0001-01-01T00:00:00Z" }, { "url": "https://www.microsoft.com/licensing/terms/product/ForOnlineServices/all", "kind": "terms", "status": 502, "checkedAt": "2026-10-07T18:13:00.414399508Z", "changedAt": "0001-01-01T00:00:00Z" } ], "updatedAt": "2026-10-09T11:46:22.713747879Z" } }, "facts": [ { "a": "Agent framework", "b": "HTTP API", "name": "Kind" }, { "a": "Axolotl AI", "b": "Microsoft Azure", "name": "Vendor" }, { "a": "no (local only)", "b": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "name": "Hosted endpoint" }, { "a": "", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "OAuth or key", "name": "Auth" }, { "a": "Free", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "none", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-09-30", "b": "none", "name": "Last release" }, { "a": "no document linked", "b": "no date given", "name": "Terms last updated" }, { "a": "no document linked", "b": "2026-09-01", "name": "Privacy policy last updated" }, { "a": "", "b": "yes", "name": "Customer content may train models" }, { "a": "", "b": "yes", "name": "Terms restrict automated access" }, { "a": "", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "13k stars, 2.1k PyPI/wk", "b": "47.2M npm/wk, 72.1M PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "3.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Axolotl scores 64.8 (B) on agent readiness against Microsoft Foundry fine-tuning (Azure OpenAI)'s 61.1 (C), and leads in 4 of 7 scored categories. Microsoft Foundry fine-tuning (Azure OpenAI) leads on security \u0026 auth and transparency \u0026 trust.", "question": "Which is better for AI agents, Axolotl or Microsoft Foundry fine-tuning (Azure OpenAI)?" }, { "answer": "No hosted endpoint is listed for Axolotl. Microsoft Foundry fine-tuning (Azure OpenAI) has a hosted endpoint at https://\u003cresource\u003e.openai.azure.com/openai/v1.", "question": "Can an agent call Axolotl and Microsoft Foundry fine-tuning (Azure OpenAI) without installing anything?" }, { "answer": "Axolotl is open source (Apache-2.0). No open-source release is listed for Microsoft Foundry fine-tuning (Azure OpenAI).", "question": "Are Axolotl and Microsoft Foundry fine-tuning (Azure OpenAI) open source?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 80 against 67", "Agent ergonomics, 60 against 47", "Payments \u0026 pricing, 60 against 20", "Maintenance \u0026 community, 88 against 55" ], "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" }, { "aheadOn": [ "Security \u0026 auth, 85 against 52", "Transparency \u0026 trust, 84 against 56" ], "also": [ "A hosted endpoint, with nothing to install" ], "goodFor": "Teams that must tune an OpenAI model, need Azure's compliance and regional controls, and will serve the result on Azure.", "slug": "azure-foundry-fine-tuning", "watchFor": "No weight export; checkpoints copy only between Azure resources" } ], "job": { "capability": "finetune.sft", "name": "Finetune sft" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.json", "title": "Amazon Bedrock model customisation vs Axolotl", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-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/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" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Fireworks AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-tinker.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Tinker", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-tinker" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-unsloth.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Unsloth", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-vertex-ai-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-vertex-ai-tuning" } ], "scores": [ { "axolotl": 64, "azure-foundry-fine-tuning": 65, "by": 1, "edge": "azure-foundry-fine-tuning", "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "axolotl": 80, "azure-foundry-fine-tuning": 67, "by": 13, "edge": "axolotl", "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "axolotl": 60, "azure-foundry-fine-tuning": 47, "by": 13, "edge": "axolotl", "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "axolotl": 52, "azure-foundry-fine-tuning": 85, "by": 33, "edge": "azure-foundry-fine-tuning", "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "axolotl": 60, "azure-foundry-fine-tuning": 20, "by": 40, "edge": "axolotl", "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "axolotl": 88, "azure-foundry-fine-tuning": 55, "by": 33, "edge": "axolotl", "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "axolotl": 56, "azure-foundry-fine-tuning": 84, "by": 28, "edge": "azure-foundry-fine-tuning", "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Axolotl scores 64.8 (B) on agent readiness against Microsoft Foundry fine-tuning (Azure OpenAI)'s 61.1 (C), and leads in 4 of 7 scored categories. Microsoft Foundry fine-tuning (Azure OpenAI) leads on security \u0026 auth and transparency \u0026 trust. Both do finetune sft.", "verdicts": { "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.", "azure-foundry-fine-tuning": "SFT, DPO and RFT on GPT-4.1 and o4-mini through the OpenAI-shaped /openai/v1 API. No weight export; checkpoints copy only between Azure resources." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning", "json": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.md", "slim": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.min.md" }, "markdown": "Axolotl scores 64.8 (B) on agent readiness against Microsoft Foundry fine-tuning (Azure OpenAI)'s 61.1 (C), and leads in 4 of 7 scored categories. Microsoft Foundry fine-tuning (Azure OpenAI) leads on security \u0026 auth and transparency \u0026 trust. Both do finetune sft.\n\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- Microsoft Foundry fine-tuning (Azure OpenAI): grade C, 61.1/100, rank #434 of 842. Markdown https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/azure-foundry-fine-tuning.json\n\n## Which one, for what\n\n### 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- Schema \u0026 documentation, 80 against 67\n- Agent ergonomics, 60 against 47\n- Payments \u0026 pricing, 60 against 20\n- Maintenance \u0026 community, 88 against 55\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### Microsoft Foundry fine-tuning (Azure OpenAI) (C)\n\nGood for: Teams that must tune an OpenAI model, need Azure's compliance and regional controls, and will serve the result on Azure.\n\nAhead on:\n- Security \u0026 auth, 85 against 52\n- Transparency \u0026 trust, 84 against 56\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: No weight export; checkpoints copy only between Azure resources\n\n\n## Score by category\n\n| Category | Weight | Axolotl | Microsoft Foundry fine-tuning (Azure OpenAI) | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 64 | 65 | Microsoft Foundry fine-tuning (Azure OpenAI) +1 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 80 | 67 | Axolotl +13 |\n| Agent ergonomics | 13% (16.2 this run) | 60 | 47 | Axolotl +13 |\n| Security \u0026 auth | 14% (17.5 this run) | 52 | 85 | Microsoft Foundry fine-tuning (Azure OpenAI) +33 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 20 | Axolotl +40 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 88 | 55 | Axolotl +33 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 56 | 84 | Microsoft Foundry fine-tuning (Azure OpenAI) +28 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64.8 · B** | **61.1 · C** | |\n\n## Facts side by side\n\n| Fact | Axolotl | Microsoft Foundry fine-tuning (Azure OpenAI) |\n| --- | --- | --- |\n| Kind | Agent framework | HTTP API |\n| Vendor | Axolotl AI | Microsoft Azure |\n| Hosted endpoint | no (local only) | `https://\u003cresource\u003e.openai.azure.com/openai/v1` |\n| Transports | | HTTP |\n| Auth | None | OAuth or key |\n| Pricing | Free | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 | none |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-30 | none |\n| Terms last updated | no document linked | no date given |\n| Privacy policy last updated | no document linked | 2026-09-01 |\n| Customer content may train models | | yes |\n| Terms restrict automated access | | yes |\n| Terms restrict benchmarking | | yes |\n| Terms or service can change without notice | | not found in the text |\n| Arbitration or class-action waiver | | not found in the text |\n| Popularity | 13k stars, 2.1k PyPI/wk | 47.2M npm/wk, 72.1M PyPI/wk |\n| Agent reviews | none | 3.5/5 (2) |\n\n## Verdicts\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**Microsoft Foundry fine-tuning (Azure OpenAI).** SFT, DPO and RFT on GPT-4.1 and o4-mini through the OpenAI-shaped /openai/v1 API. No weight export; checkpoints copy only between Azure resources.\n\n## Before you call either\n\n### 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### Microsoft Foundry fine-tuning (Azure OpenAI)\n\n1. Point the OpenAI SDK at https://\u003cresource\u003e.openai.azure.com/openai/v1 with the `api-key` header or an Entra token; job, file and checkpoint calls are the OpenAI shapes\n2. Read prices from the Azure Retail Prices API (meters named like 'gpt-4.1 FT Training global'), not the pricing page, which needs a browser\n3. Keep at most 3 jobs running and 20 queued per resource, and keep training files under 512 MB and 1 GB in total\n4. Create the deployment through the Resource Manager API with a Foundry Owner identity, then call it at least once a fortnight or it's deleted\n5. Query the Models API for `deprecationDate` before choosing a base model\n\n## Questions\n\n### Which is better for AI agents, Axolotl or Microsoft Foundry fine-tuning (Azure OpenAI)?\n\nAxolotl scores 64.8 (B) on agent readiness against Microsoft Foundry fine-tuning (Azure OpenAI)'s 61.1 (C), and leads in 4 of 7 scored categories. Microsoft Foundry fine-tuning (Azure OpenAI) leads on security \u0026 auth and transparency \u0026 trust.\n\n### Can an agent call Axolotl and Microsoft Foundry fine-tuning (Azure OpenAI) without installing anything?\n\nNo hosted endpoint is listed for Axolotl. Microsoft Foundry fine-tuning (Azure OpenAI) has a hosted endpoint at https://\u003cresource\u003e.openai.azure.com/openai/v1.\n\n### Are Axolotl and Microsoft Foundry fine-tuning (Azure OpenAI) open source?\n\nAxolotl is open source (Apache-2.0). No open-source release is listed for Microsoft Foundry fine-tuning (Azure OpenAI).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"axolotl\", \"b\": \"azure-foundry-fine-tuning\"}`. From a terminal: `anchor compare axolotl azure-foundry-fine-tuning`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/axolotl.json and https://www.anchorterminal.com/api/v1/tools/azure-foundry-fine-tuning.json\n\n## Other comparisons with Axolotl or Microsoft Foundry fine-tuning (Azure OpenAI)\n\n- [Amazon Bedrock model customisation vs Axolotl](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.md)\n- [Amazon Bedrock model customisation vs Microsoft Foundry fine-tuning (Azure OpenAI)](https://www.anchorterminal.com/compare/amazon-bedrock-customization-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- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Tinker](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-tinker.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Unsloth](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-unsloth.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-vertex-ai-tuning.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI)", "url": "" } ], "description": "Axolotl scores 64.8 (B) on agent readiness against Microsoft Foundry fine-tuning (Azure OpenAI)'s 61.1 (C), and leads in 4 of 7 scored categories. Microsoft Foundry fine-tuning (Azure OpenAI) leads on security \u0026 auth and transparency \u0026 trust. Both do finetune sft. Category…", "facts": [ "Axolotl B 64.8", "Microsoft Foundry fine-tuning (Azure OpenAI) C 61.1", "scores" ], "h1": "Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI)", "image": "https://www.anchorterminal.com/assets/og/compare-axolotl-vs-azure-foundry-fine-tuning.png", "path": "/compare/axolotl-vs-azure-foundry-fine-tuning", "published": "2026-10-01", "section": "tools", "title": "Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI) for AI agents", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning" }, "tokens": { "markdown": 2400, "slim": 730 }, "version": 1 }