{ "data": { "a": { "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": "2026-10-09T11:00:16.942864776Z", "lastOk": false, "lastStatus": 0, "lastMs": 0, "lastNote": "DNS lookup failed", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 260, "samples30d": 2101, "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": 117, "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": "2026-10-09T11:00:16.942864776Z" } }, "answer": "Microsoft Foundry fine-tuning (Azure OpenAI) scores 61.1 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 4 of 7 scored categories. Nebius Token Factory fine-tuning leads on maintenance \u0026 community.", "b": { "slug": "nebius-token-factory-fine-tuning", "name": "Nebius Token Factory fine-tuning", "vendor": "Nebius", "vendorUrl": "https://nebius.com", "kind": "http-api", "category": "fine-tuning", "summary": "Nebius Token Factory runs supervised fine-tuning jobs on open models such as Llama, Qwen, gpt-oss, Gemma and DeepSeek through an OpenAI-compatible REST API, with LoRA or full weights and downloadable checkpoints.", "url": "https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nebius-token-factory-fine-tuning.json", "repo": "https://github.com/nebius/token-factory-cookbook", "license": "Proprietary service (cookbook examples MIT)", "transports": [ "http" ], "remoteUrl": "https://api.tokenfactory.nebius.com/v1", "packages": [], "auth": "api-key", "authNotes": "`Authorization: Bearer` with an API key created by a person in the console under API keys, shown once and read from `NEBIUS_API_KEY` in the docs. Keys belong to a project. Project Admins and Members both have full access to the Files and Fine-tuning APIs, and no per-key scopes or expiry were found in the reviewed documentation.", "pricing": "usage", "pricingNotes": "Pay as you go, with no monthly fee stated. No fine-tuning price was found in the docs or in the public catalogue at `/api/public/models_info`. The product page sends readers to the Token Factory console's prices page, a script-drawn console page that robots.txt disallows, so we did not read it. A bank card is mandatory at onboarding. New accounts get $1 of trial credit valid for 30 days (https://docs.tokenfactory.nebius.com/other-capabilities/billing-new.md). Dedicated endpoints bill while one or more replicas are ready.", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs index, the OpenAPI file or the billing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://docs.tokenfactory.nebius.com/post-training/overview", "llmsTxt": "https://docs.tokenfactory.nebius.com/llms.txt", "openapi": "https://api.tokenfactory.nebius.com/openapi.json", "capabilities": [ "finetune.sft", "finetune.lora", "finetune.export" ], "tags": [ "hosted", "usage-priced", "card-required", "open-weights", "llms-txt", "openapi", "async-jobs" ], "lastRelease": "2026-09-30", "graded": true, "anchor": { "graded": true, "score": 47.7, "grade": "D", "agentReady": false, "rank": 738, "ranked": true, "rankOf": 842, "categoryRank": 9, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 51, "maintenance": 61, "payments": 0, "reliability": 45, "schema": 68, "security": 52, "transparency": 79 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": -2, "negativeNotes": [ "-2: on 2026-10-08 the product page said a fine-tuned model goes live with one click on 'serverless endpoints, on-demand GPU, or dedicated enterprise clusters', while the docs say deployment is by dedicated endpoints only and custom model weights are in beta on request, and the guide's deployment links return 404 (https://nebius.com/services/token-factory/fine-tuning, https://docs.tokenfactory.nebius.com/post-training/models.md)" ], "verdict": "Supervised fine-tuning on 49 open base models through OpenAI-style `/v1/fine_tuning/jobs` calls, with LoRA or full weights and every checkpoint file downloadable. No fine-tuning price was found outside the script-drawn console, and the docs say tuned models deploy only to dedicated endpoints, with custom weights in beta on request.", "bestFor": "Teams that want supervised LoRA or full fine-tuning of a wide list of open models, up to Qwen3 Coder 480B and DeepSeek, through OpenAI-style calls, with EU storage and the weights to take away.", "strengths": [ "49 base models listed, 42 with LoRA and full fine-tuning and 7 with full fine-tuning only, at context lengths from 8,192 to 131,072 tokens", "Checkpoint files download through `GET /v1/files/{file_id}/content`, and an `hf` integration pushes the result to a Hugging Face repository", "A public OpenAPI 3.1 file covers the fine-tuning, files, datasets and operations paths, with ranges on every hyperparameter", "The legal guide says content is not used to train models and that customers own the models they fine-tune", "A dated sub-processor list for Token Factory, with 15 days' notice of changes, and a valid security.txt on nebius.com" ], "weaknesses": [ "No fine-tuning price found in the docs or the public catalogue JSON. The price page is a script-drawn console page that robots.txt disallows", "The models page says deployment is by dedicated endpoints only, and custom model weights are in beta and available on request", "A bank card is mandatory at onboarding, so the $1 trial credit (30 days) is not a card-free trial", "Two major incidents tagged Token Factory in 90 days, 93 minutes on 27 July and about 21.5 hours in us-central1 from 19 August 2026", "No public changelog, no idempotency key on job creation and only a 422 response documented in the reference", "The Services Agreement (clause 4.1.10) forbids competitive analysis or benchmarking" ], "agentNotes": [ "Use the OpenAI client with `base_url` `https://api.tokenfactory.nebius.com/v1/` and `NEBIUS_API_KEY`. Upload JSONL with `purpose=fine-tune`, then create the job.", "Set `hyperparameters.lora` to true for an adapter. The default is false, which runs full fine-tuning.", "Poll `GET /v1/fine_tuning/jobs/{job_id}` no faster than every 15 seconds. There is no idempotency key, so list jobs before recreating one after a timeout.", "Download every ID in a checkpoint's `result_files` before relying on hosted copies. The terms allow deletion of tuned models at three days' notice.", "The spec requires `wandb.api_key` although the guide omits it, and it also accepts `mlflow` and `hf` integrations. Check the price in the console before starting a job." ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 47.7 } ], "editorialScores": { "ergonomics": 51, "maintenance": 61, "payments": 0, "reliability": 45, "schema": 68, "security": 52, "transparency": 70 }, "provenanceScore": 88 }, "connect": { "install": "pip3 install --upgrade openai", "http": "curl 'https://api.tokenfactory.nebius.com/v1/fine_tuning/jobs' \\\n -X POST \\\n -H 'Accept: application/json' \\\n -H 'Content-Type: application/json' \\\n -H \"Authorization: Bearer $NEBIUS_API_KEY\" \\\n -d '{\"model\":\"meta-llama/Llama-3.1-8B-Instruct\",\"suffix\":\"my-domain-adapter\",\"training_file\":\"\u003ctraining_file_ID\u003e\",\"hyperparameters\":{\"n_epochs\":3,\"lora\":true,\"lora_r\":16,\"lora_alpha\":16}}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/nebius-token-factory-fine-tuning" }, "sameCompany": [ "nebius-ai-cloud" ], "area": "models", "provenance": { "legalEntity": "Nebius B.V.", "domain": "nebius.com", "domainRegistered": "2004-06-26", "endpointOnVendorDomain": true, "terms": "https://docs.nebius.com/legal/agreement", "privacy": "https://docs.nebius.com/legal/privacy", "statusPage": "https://status.nebius.com", "changelog": "", "securityTxt": "valid", "checked": "2026-10-08", "notes": [ "The Nebius Services Agreement (published 15 September 2026, effective 28 September 2026) names Nebius B.V. as the contracting entity by default, Nebius Inc. for US customers who registered from 15 September 2026 and Nebius Israel Ltd for some customers in Israel. The parent is Nebius Group N.V.", "The Token Factory Supplemental Terms at https://docs.nebius.com/legal/token-factory are incorporated into the agreement and carry the fine-tuning clauses.", "The privacy policy (effective 23 September 2026) names Nebius Token Factory in its scope and covers data Nebius holds as controller. Customer content is covered by the DPA at https://docs.nebius.com/legal/dpa.", "security.txt at nebius.com gives security@nebius.com and expires on 31 December 2027. The same path on tokenfactory.nebius.com returns the console's HTML shell.", "status.nebius.com is an Atlassian Statuspage for the whole Nebius cloud, with a Token Factory component in each of nine regions and no separate fine-tuning component.", "No public changelog for Token Factory was found in the docs index. The OpenAPI file carries the version stamp 20260930-cfb76be12.", "nebius.com was registered on 26 June 2004 per Verisign RDAP." ], "score": 88 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.json", "live": { "slug": "nebius-token-factory-fine-tuning", "probe": { "target": "https://api.tokenfactory.nebius.com/v1", "method": "get", "lastAt": "2026-10-09T11:00:30.72469802Z", "lastOk": true, "lastStatus": 404, "lastMs": 87, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 87, "p95ms24h": 172, "samples24h": 36, "samples30d": 36, "days": [ { "date": "2026-10-09", "probes": 36, "ok": 36 } ] }, "vendorStatus": { "page": "https://status.nebius.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-09T11:03:49.658305196Z" }, "updatedAt": "2026-10-09T11:03:49.658305196Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Microsoft Azure", "b": "Nebius", "name": "Vendor" }, { "a": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "b": "https://api.tokenfactory.nebius.com/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "none", "b": "Proprietary service (cookbook examples MIT)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "none", "b": "2026-09-30", "name": "Last release" }, { "a": "no date given", "b": "2026-09-28", "name": "Terms last updated" }, { "a": "2026-09-01", "b": "2026-09-23", "name": "Privacy policy last updated" }, { "a": "yes", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "47.2M npm/wk, 72.1M PyPI/wk", "b": "none", "name": "Popularity" }, { "a": "3.5/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Microsoft Foundry fine-tuning (Azure OpenAI) scores 61.1 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 4 of 7 scored categories. Nebius Token Factory fine-tuning leads on maintenance \u0026 community.", "question": "Which is better for AI agents, Microsoft Foundry fine-tuning (Azure OpenAI) or Nebius Token Factory fine-tuning?" }, { "answer": "Microsoft Foundry fine-tuning (Azure OpenAI) takes an API key or an OAuth sign-in. Nebius Token Factory fine-tuning needs an API key.", "question": "Do Microsoft Foundry fine-tuning (Azure OpenAI) and Nebius Token Factory fine-tuning need an API key?" }, { "answer": "Yes. Microsoft Foundry fine-tuning (Azure OpenAI) has a hosted endpoint at https://\u003cresource\u003e.openai.azure.com/openai/v1 and Nebius Token Factory fine-tuning at https://api.tokenfactory.nebius.com/v1.", "question": "Can an agent call Microsoft Foundry fine-tuning (Azure OpenAI) and Nebius Token Factory fine-tuning without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 65 against 45", "Security \u0026 auth, 85 against 52", "Payments \u0026 pricing, 20 against 0", "Transparency \u0026 trust, 84 against 79" ], "also": null, "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" }, { "aheadOn": [ "Maintenance \u0026 community, 61 against 55" ], "also": null, "goodFor": "Teams that want supervised LoRA or full fine-tuning of a wide list of open models, up to Qwen3 Coder 480B and DeepSeek, through OpenAI-style calls, with EU storage and the weights to take away.", "slug": "nebius-token-factory-fine-tuning", "watchFor": "No fine-tuning price found in the docs or the public catalogue JSON. The price page is a script-drawn console page that robots.txt disallows" } ], "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-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/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-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/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-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" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.json", "title": "Fireworks AI Fine-tuning vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-tinker.json", "title": "Nebius Token Factory fine-tuning vs Tinker", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-tinker" }, { "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning.json", "title": "Nebius Token Factory fine-tuning vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/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/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.json", "title": "Nebius Token Factory fine-tuning vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning" } ], "scores": [ { "azure-foundry-fine-tuning": 65, "by": 20, "edge": "azure-foundry-fine-tuning", "key": "reliability", "name": "Reliability", "nebius-token-factory-fine-tuning": 45, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "azure-foundry-fine-tuning": 67, "by": 1, "edge": "nebius-token-factory-fine-tuning", "key": "schema", "name": "Schema \u0026 documentation", "nebius-token-factory-fine-tuning": 68, "weight": 13 }, { "azure-foundry-fine-tuning": 47, "by": 4, "edge": "nebius-token-factory-fine-tuning", "key": "ergonomics", "name": "Agent ergonomics", "nebius-token-factory-fine-tuning": 51, "weight": 13 }, { "azure-foundry-fine-tuning": 85, "by": 33, "edge": "azure-foundry-fine-tuning", "key": "security", "name": "Security \u0026 auth", "nebius-token-factory-fine-tuning": 52, "weight": 14 }, { "azure-foundry-fine-tuning": 20, "by": 20, "edge": "azure-foundry-fine-tuning", "key": "payments", "name": "Payments \u0026 pricing", "nebius-token-factory-fine-tuning": 0, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "azure-foundry-fine-tuning": 55, "by": 6, "edge": "nebius-token-factory-fine-tuning", "key": "maintenance", "name": "Maintenance \u0026 community", "nebius-token-factory-fine-tuning": 61, "weight": 7 }, { "azure-foundry-fine-tuning": 84, "by": 5, "edge": "azure-foundry-fine-tuning", "key": "transparency", "name": "Transparency \u0026 trust", "nebius-token-factory-fine-tuning": 79, "weight": 7 } ], "summary": "Microsoft Foundry fine-tuning (Azure OpenAI) scores 61.1 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 4 of 7 scored categories. Nebius Token Factory fine-tuning leads on maintenance \u0026 community. Both do finetune sft.", "verdicts": { "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.", "nebius-token-factory-fine-tuning": "Supervised fine-tuning on 49 open base models through OpenAI-style `/v1/fine_tuning/jobs` calls, with LoRA or full weights and every checkpoint file downloadable. No fine-tuning price was found outside the script-drawn console, and the docs say tuned models deploy only to dedicated endpoints, with custom weights in beta on request." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "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-nebius-token-factory-fine-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning.md", "slim": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning.min.md" }, "markdown": "Microsoft Foundry fine-tuning (Azure OpenAI) scores 61.1 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 4 of 7 scored categories. Nebius Token Factory fine-tuning leads on maintenance \u0026 community. Both do finetune sft.\n\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- Nebius Token Factory fine-tuning: grade D, 47.7/100, rank #738 of 842. Markdown https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/nebius-token-factory-fine-tuning.json\n\n## Which one, for what\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- Reliability, 65 against 45\n- Security \u0026 auth, 85 against 52\n- Payments \u0026 pricing, 20 against 0\n- Transparency \u0026 trust, 84 against 79\n\nWatch for: No weight export; checkpoints copy only between Azure resources\n\n### Nebius Token Factory fine-tuning (D)\n\nGood for: Teams that want supervised LoRA or full fine-tuning of a wide list of open models, up to Qwen3 Coder 480B and DeepSeek, through OpenAI-style calls, with EU storage and the weights to take away.\n\nAhead on:\n- Maintenance \u0026 community, 61 against 55\n\nWatch for: No fine-tuning price found in the docs or the public catalogue JSON. The price page is a script-drawn console page that robots.txt disallows\n\n\n## Score by category\n\n| Category | Weight | Microsoft Foundry fine-tuning (Azure OpenAI) | Nebius Token Factory fine-tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 65 | 45 | Microsoft Foundry fine-tuning (Azure OpenAI) +20 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 67 | 68 | Nebius Token Factory fine-tuning +1 |\n| Agent ergonomics | 13% (16.2 this run) | 47 | 51 | Nebius Token Factory fine-tuning +4 |\n| Security \u0026 auth | 14% (17.5 this run) | 85 | 52 | Microsoft Foundry fine-tuning (Azure OpenAI) +33 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 0 | Microsoft Foundry fine-tuning (Azure OpenAI) +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 55 | 61 | Nebius Token Factory fine-tuning +6 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 84 | 79 | Microsoft Foundry fine-tuning (Azure OpenAI) +5 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **61.1 · C** | **47.7 · D** | |\n\n## Facts side by side\n\n| Fact | Microsoft Foundry fine-tuning (Azure OpenAI) | Nebius Token Factory fine-tuning |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Microsoft Azure | Nebius |\n| Hosted endpoint | `https://\u003cresource\u003e.openai.azure.com/openai/v1` | `https://api.tokenfactory.nebius.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | OAuth or key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | none | Proprietary service (cookbook examples MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | none | 2026-09-30 |\n| Terms last updated | no date given | 2026-09-28 |\n| Privacy policy last updated | 2026-09-01 | 2026-09-23 |\n| Customer content may train models | yes | not found in the text |\n| Terms restrict automated access | yes | not found in the text |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | not found in the text | yes |\n| Popularity | 47.2M npm/wk, 72.1M PyPI/wk | none |\n| Agent reviews | 3.5/5 (2) | none |\n\n## Verdicts\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**Nebius Token Factory fine-tuning.** Supervised fine-tuning on 49 open base models through OpenAI-style `/v1/fine_tuning/jobs` calls, with LoRA or full weights and every checkpoint file downloadable. No fine-tuning price was found outside the script-drawn console, and the docs say tuned models deploy only to dedicated endpoints, with custom weights in beta on request.\n\n## Before you call either\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### Nebius Token Factory fine-tuning\n\n1. Use the OpenAI client with `base_url` `https://api.tokenfactory.nebius.com/v1/` and `NEBIUS_API_KEY`. Upload JSONL with `purpose=fine-tune`, then create the job.\n2. Set `hyperparameters.lora` to true for an adapter. The default is false, which runs full fine-tuning.\n3. Poll `GET /v1/fine_tuning/jobs/{job_id}` no faster than every 15 seconds. There is no idempotency key, so list jobs before recreating one after a timeout.\n4. Download every ID in a checkpoint's `result_files` before relying on hosted copies. The terms allow deletion of tuned models at three days' notice.\n5. The spec requires `wandb.api_key` although the guide omits it, and it also accepts `mlflow` and `hf` integrations. Check the price in the console before starting a job.\n\n## Questions\n\n### Which is better for AI agents, Microsoft Foundry fine-tuning (Azure OpenAI) or Nebius Token Factory fine-tuning?\n\nMicrosoft Foundry fine-tuning (Azure OpenAI) scores 61.1 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 4 of 7 scored categories. Nebius Token Factory fine-tuning leads on maintenance \u0026 community.\n\n### Do Microsoft Foundry fine-tuning (Azure OpenAI) and Nebius Token Factory fine-tuning need an API key?\n\nMicrosoft Foundry fine-tuning (Azure OpenAI) takes an API key or an OAuth sign-in. Nebius Token Factory fine-tuning needs an API key.\n\n### Can an agent call Microsoft Foundry fine-tuning (Azure OpenAI) and Nebius Token Factory fine-tuning without installing anything?\n\nYes. Microsoft Foundry fine-tuning (Azure OpenAI) has a hosted endpoint at https://\u003cresource\u003e.openai.azure.com/openai/v1 and Nebius Token Factory fine-tuning at https://api.tokenfactory.nebius.com/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"azure-foundry-fine-tuning\", \"b\": \"nebius-token-factory-fine-tuning\"}`. From a terminal: `anchor compare azure-foundry-fine-tuning nebius-token-factory-fine-tuning`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/azure-foundry-fine-tuning.json and https://www.anchorterminal.com/api/v1/tools/nebius-token-factory-fine-tuning.json\n\n## Other comparisons with Microsoft Foundry fine-tuning (Azure OpenAI) or Nebius Token Factory fine-tuning\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 Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-nebius-token-factory-fine-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 Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-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 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- [Fireworks AI Fine-tuning vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.md)\n- [Nebius Token Factory fine-tuning vs Tinker](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-tinker.md)\n- [Nebius Token Factory fine-tuning vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning.md)\n- [Nebius Token Factory fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-unsloth.md)\n- [Nebius Token Factory fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.md)\n", "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": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Nebius Token Factory fine-tuning", "url": "" } ], "description": "Microsoft Foundry fine-tuning (Azure OpenAI) scores 61.1 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 4 of 7 scored categories. Nebius Token Factory fine-tuning leads on maintenance \u0026 community. Both do finetune sft. Category scores…", "facts": [ "Microsoft Foundry fine-tuning (Azure OpenAI) C 61.1", "Nebius Token Factory fine-tuning D 47.7", "scores" ], "h1": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Nebius Token Factory fine-tuning", "image": "https://www.anchorterminal.com/assets/og/compare-azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning.png", "path": "/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning", "published": "2026-10-01", "section": "tools", "title": "Microsoft Foundry fine-tuning vs Nebius Token Factory fine-tuning", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning" }, "tokens": { "markdown": 2650, "slim": 730 }, "version": 1 }