{
  "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": 61.4,
        "grade": "C",
        "agentReady": false,
        "rank": 228,
        "ranked": true,
        "rankOf": 452,
        "categoryRank": 2,
        "methodology": "0.3",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 47,
          "maintenance": 55,
          "payments": 20,
          "reliability": 65,
          "schema": 67,
          "security": 85,
          "transparency": 88
        },
        "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.",
        "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 $25 per 1M tokens globally, and no free tier without a card",
          "Deployment goes through management.azure.com with a separate credential and the Foundry Owner role",
          "The Azure OpenAI 'what's new' page hasn't had a dated section since May 2026"
        ],
        "agentNotes": [
          "Point the OpenAI SDK at https://\u003cresource\u003e.openai.azure.com/openai/v1 with the `api-key` header or an Entra token; job, file and checkpoint calls are the OpenAI shapes",
          "Read prices from the Azure Retail Prices API (meters named like 'gpt-4.1 FT Training global'), not the pricing page, which needs a browser",
          "Keep at most 3 jobs running and 20 queued per resource, and keep training files under 512 MB and 1 GB in total",
          "Create the deployment through the Resource Manager API with a Foundry Owner identity, then call it at least once a fortnight or it's deleted",
          "Query the Models API for `deprecationDate` before choosing a base model"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.3",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61.4
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        ],
        "editorialScores": {
          "ergonomics": 47,
          "maintenance": 55,
          "payments": 20,
          "reliability": 65,
          "schema": 67,
          "security": 85,
          "transparency": 81
        },
        "provenanceScore": 95
      },
      "connect": {
        "install": "pip install openai   # or: npm i openai",
        "http": "curl \"https://$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-learn-mcp",
        "playwright-mcp",
        "azure-mcp",
        "azure-translator",
        "microsoft-graph-calendar"
      ],
      "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": 95
      },
      "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": "2026-10-05T00:25:39.121157945Z",
          "lastOk": false,
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          "lastNote": "DNS lookup failed",
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          "samples24h": 272,
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              "date": "2026-10-01",
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            },
            {
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            },
            {
              "date": "2026-10-03",
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            },
            {
              "date": "2026-10-04",
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            },
            {
              "date": "2026-10-05",
              "probes": 5,
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            }
          ]
        },
        "vendorStatus": {
          "page": "https://azure.status.microsoft/en-us/status",
          "indicator": "unknown",
          "summary": "no machine-readable status found",
          "checkedAt": "2026-10-04T21:39:49.453465033Z"
        },
        "versions": [
          {
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        "securityTxt": {
          "url": "https://microsoft.com/.well-known/security.txt",
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          "expires": "2026-09-23T16:00:00.000Z",
          "checkedAt": "2026-10-04T15:16:01.36832038Z"
        },
        "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",
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            "status": 304,
            "checkedAt": "2026-10-04T15:45:24.699524258Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "237edf8365ef"
          },
          {
            "url": "https://prices.azure.com/api/retail/prices",
            "kind": "pricing",
            "status": 200,
            "checkedAt": "2026-10-04T15:46:58.974430601Z",
            "changedAt": "0001-01-01T00:00:00Z"
          },
          {
            "url": "https://www.microsoft.com/licensing/terms/product/ForOnlineServices/all",
            "kind": "terms",
            "status": 502,
            "checkedAt": "2026-10-04T15:51:24.287211028Z",
            "changedAt": "0001-01-01T00:00:00Z"
          }
        ],
        "updatedAt": "2026-10-05T00:25:39.121157945Z"
      }
    },
    "b": {
      "slug": "vertex-ai-tuning",
      "name": "Vertex AI Gemini tuning",
      "vendor": "Google Cloud",
      "vendorUrl": "https://cloud.google.com",
      "kind": "http-api",
      "category": "fine-tuning",
      "summary": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen, on Google Cloud's Gemini Enterprise Agent Platform (the platform formerly called Vertex AI).",
      "url": "https://www.anchorterminal.com/tools/vertex-ai-tuning",
      "markdownUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json",
      "repo": "https://github.com/googleapis/python-genai",
      "license": "Apache-2.0 (SDK)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://us-central1-aiplatform.googleapis.com/v1",
      "packages": [
        {
          "registry": "pypi",
          "name": "google-genai"
        }
      ],
      "auth": "oauth",
      "authNotes": "OAuth 2.0 bearer token from a service account or `gcloud auth print-access-token` on a project with billing and the platform API turned on. Training data comes from a Cloud Storage URI, so the caller also needs read access to the bucket. Tuning is a Vertex-only feature. The SDK says tuning is supported only on the enterprise platform, not the Gemini Developer API.",
      "pricing": "usage",
      "pricingNotes": "Per training token, where training tokens = dataset tokens x epochs. Gemini 3.5 Flash $10 per 1M for supervised or reinforcement learning fine-tuning (listed as $0.01 per 1,000), Gemini 3.1 Flash Lite $3, Gemini 2.5 Pro $25, Gemini 2.5 Flash $5 for supervised or preference tuning, Gemini 2.5 Flash Lite $1.50. Open models run from Gemma 3 at $0.47 (1B) to $6.83 (27B), Llama 3.1 8B $0.67, Llama 3.3 70B $6.72, Llama 4 Scout $5.77, Qwen 3 4B $1.35 to Qwen 3 32B $6.57. From Gemini 3 on, a tuned model endpoint costs 1.5x the base model's prediction price; older Gemini tuned models cost the same as base (https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing).",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 3900,
        "npmWeekly": null,
        "pypiWeekly": 32928433,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/tuning",
      "capabilities": [
        "finetune.sft",
        "finetune.preference",
        "finetune.rl",
        "finetune.lora"
      ],
      "tags": [
        "hosted",
        "usage-priced",
        "closed-source",
        "card-required",
        "enterprise",
        "python",
        "async-jobs"
      ],
      "lastRelease": "2026-10-01",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 64.2,
        "grade": "B",
        "agentReady": false,
        "rank": 190,
        "ranked": true,
        "rankOf": 452,
        "categoryRank": 1,
        "methodology": "0.3",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 48,
          "maintenance": 80,
          "payments": 20,
          "reliability": 67,
          "schema": 82,
          "security": 71,
          "transparency": 89
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.",
        "strengths": [
          "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen",
          "No Vertex or Gemini incidents on the Google Cloud status dashboard from July to September 2026",
          "Public proto for GenAiTuningService with filter and pagination on job lists, and docs pages served as Markdown at `.md.txt`",
          "Google says it won't train or fine-tune on customer data without permission, and a dated model lifecycle table promises 12 months from release",
          "ISO 27001, 27017 and 27018 and SOC 1, 2 and 3 cover Gemini Enterprise Agent Platform, and the subprocessor list gives locations"
        ],
        "weaknesses": [
          "No weight export. The tuned model exists only as a Google Cloud endpoint",
          "Tuned Gemini 3 inference costs 1.5x the base model for as long as you serve it",
          "Setup needs a project, billing, IAM and a Cloud Storage bucket before the first job",
          "RL tuning is Pre-GA on v1beta1, and the SDK's `tunings.tune()` is marked experimental",
          "Gemini 2.5 Pro, Flash and Flash-Lite retire on 20 October 2026, and the docs don't say what happens to their tunes"
        ],
        "agentNotes": [
          "Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API",
          "Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown",
          "Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20",
          "List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it",
          "Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "B",
            "methodology": "0.3",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 64.2
          }
        ],
        "editorialScores": {
          "ergonomics": 48,
          "maintenance": 80,
          "payments": 20,
          "reliability": 67,
          "schema": 82,
          "security": 71,
          "transparency": 78
        },
        "provenanceScore": 100
      },
      "connect": {
        "install": "pip install google-genai",
        "http": "curl -X POST \"https://us-central1-aiplatform.googleapis.com/v1/projects/$GOOGLE_CLOUD_PROJECT/locations/us-central1/tuningJobs\" \\\n  -H \"Authorization: Bearer $(gcloud auth print-access-token)\" -H \"content-type: application/json\" \\\n  -d '{\"baseModel\":\"gemini-3.5-flash\",\"supervisedTuningSpec\":{\"trainingDatasetUri\":\"gs://my-bucket/train.jsonl\",\"hyperParameters\":{\"epochCount\":3,\"adapterSize\":\"ADAPTER_SIZE_FOUR\"}},\"tunedModelDisplayName\":\"my-tune\"}'"
      },
      "letme": {
        "capability": "https://letme.dev/finetune.sft",
        "tool": "https://letme.dev/vertex-ai-tuning"
      },
      "sameCompany": [
        "gemini-api",
        "gemini-embedding",
        "google-model-armor",
        "google-imagen",
        "google-veo",
        "google-lyria",
        "google-speech-to-text",
        "google-adk",
        "google-secret-manager",
        "google-weather-api",
        "chrome-devtools-mcp",
        "google-maps-platform",
        "google-cloud-translation",
        "google-calendar-api",
        "google-drive-api",
        "gemini-cli"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "Gemini 3.5 Flash, supervised tuning",
          "unit": "1m-tokens",
          "usd": 10
        },
        {
          "item": "Gemini 3.5 Flash, reinforcement learning fine-tuning",
          "unit": "1m-tokens",
          "usd": 10
        },
        {
          "item": "Gemini 3.1 Flash Lite, supervised tuning",
          "unit": "1m-tokens",
          "usd": 3
        },
        {
          "item": "Gemini 2.5 Pro, supervised tuning",
          "unit": "1m-tokens",
          "usd": 25
        },
        {
          "item": "Gemini 2.5 Flash, supervised or preference tuning",
          "unit": "1m-tokens",
          "usd": 5
        },
        {
          "item": "Gemini 2.5 Flash Lite, supervised or preference tuning",
          "unit": "1m-tokens",
          "usd": 1.5
        },
        {
          "item": "Gemma 3 27B IT, supervised tuning",
          "unit": "1m-tokens",
          "usd": 6.83
        },
        {
          "item": "Llama 3.3 70B, supervised tuning",
          "unit": "1m-tokens",
          "usd": 6.72
        },
        {
          "item": "Qwen 3 32B, supervised tuning",
          "unit": "1m-tokens",
          "usd": 6.57
        }
      ],
      "provenance": {
        "legalEntity": "Google LLC",
        "domain": "google.com",
        "domainRegistered": "1997-09-15",
        "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.",
        "endpointOnVendorDomain": true,
        "terms": "https://cloud.google.com/terms",
        "privacy": "https://policies.google.com/privacy",
        "statusPage": "https://status.cloud.google.com",
        "changelog": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/release-notes",
        "securityTxt": "valid",
        "checked": "2026-09-30",
        "notes": [
          "Entity, domain and security.txt are the same as the gemini-api listing, which uses the same Google privacy policy. The terms differ: this product runs under the Google Cloud Platform terms, whose contracting entity is set per billing country at cloud.google.com/terms/google-entity.",
          "The docs site serves navigation first and truncates the article body for a text fetcher, so the supported-model list, dataset limits and the checkpoint export page couldn't be read. Model and price facts come from the pricing page and the google-genai source.",
          "The old Vertex AI pricing page at cloud.google.com/vertex-ai/generative-ai/pricing still serves, but its tuning table stops at Gemini 2.5; the Gemini Enterprise Agent Platform pricing page has the Gemini 3 rows."
        ],
        "score": 100
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.json",
      "live": {
        "slug": "vertex-ai-tuning",
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          "lastOk": true,
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            {
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        "versions": [
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            "seenAt": "2026-10-04T16:43:23.256991186Z"
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  "markdown": "Vertex AI Gemini tuning has a score of 64.2 (B) against Microsoft Foundry fine-tuning (Azure OpenAI)'s 61.4 (C). Both do finetune sft. The largest gap is maintenance \u0026 community, 25 points.\n\n- Microsoft Foundry fine-tuning (Azure OpenAI): grade C, 61.4/100, rank #228 of 452. 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- Vertex AI Gemini tuning: grade B, 64.2/100, rank #190 of 452. Markdown https://www.anchorterminal.com/tools/vertex-ai-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json\n\n## Which one, for what\n\nPick Microsoft Foundry fine-tuning (Azure OpenAI) for security \u0026 auth (+14).\n\nPick Vertex AI Gemini tuning for schema \u0026 documentation (+15), maintenance \u0026 community (+25).\n\n## Score by category\n\n| Category | Weight | Microsoft Foundry fine-tuning (Azure OpenAI) | Vertex AI Gemini tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 65 | 67 | Vertex AI Gemini tuning +2 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 67 | 82 | Vertex AI Gemini tuning +15 |\n| Agent ergonomics | 13% (16.2 this run) | 47 | 48 | Vertex AI Gemini tuning +1 |\n| Security \u0026 auth | 14% (17.5 this run) | 85 | 71 | Microsoft Foundry fine-tuning (Azure OpenAI) +14 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 20 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 55 | 80 | Vertex AI Gemini tuning +25 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 88 | 89 | Vertex AI Gemini tuning +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61.4 · C** | **64.2 · B** | |\n\n## Facts side by side\n\n| Fact | Microsoft Foundry fine-tuning (Azure OpenAI) | Vertex AI Gemini tuning |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Microsoft Azure | Google Cloud |\n| Hosted endpoint | `https://\u003cresource\u003e.openai.azure.com/openai/v1` | `https://us-central1-aiplatform.googleapis.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | OAuth or key | OAuth |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | none | Apache-2.0 (SDK) |\n| Tools exposed | none | none |\n| Context cost (tools/list) | n/a | n/a |\n| p95 latency | not measured yet | not measured yet |\n| Availability (30d) | not measured yet | not measured yet |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| MCP registry | not listed | not listed |\n| Last release | none | 2026-10-01 |\n| Popularity | 47.2M npm/wk, 72.1M PyPI/wk | 3.9k stars, 32.9M PyPI/wk |\n| Agent reviews | 3.5/5 (2) | 2.5/5 (2) |\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**Vertex AI Gemini tuning.** Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.\n\n## Before you call either\n\n### 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### Vertex AI Gemini tuning\n\n1. Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API\n2. Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown\n3. Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20\n4. List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it\n5. Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes\n\n## Other comparisons with Microsoft Foundry fine-tuning (Azure OpenAI) or Vertex AI Gemini tuning\n\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- [Fireworks AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.md)\n- [Tinker vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning.md)\n- [Together AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning.md)\n- [Unsloth vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning.md)\n",
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    "description": "Vertex AI Gemini tuning has a score of 64.2 (B) against Microsoft Foundry fine-tuning (Azure OpenAI)'s 61.4 (C). Both do finetune sft. The largest gap is maintenance \u0026 community, 25 points. Category scores, facts, verdicts and agent notes side by side.",
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