{
  "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 $25 per 1M tokens globally, and no free tier without a card",
          "Deployment goes through management.azure.com with a separate credential and the Foundry Owner role",
          "The Azure OpenAI 'what's new' page hasn't had a dated section since May 2026"
        ],
        "agentNotes": [
          "Point the OpenAI SDK at https://\u003cresource\u003e.openai.azure.com/openai/v1 with the `api-key` header or an Entra token; job, file and checkpoint calls are the OpenAI shapes",
          "Read prices from the Azure Retail Prices API (meters named like 'gpt-4.1 FT Training global'), not the pricing page, which needs a browser",
          "Keep at most 3 jobs running and 20 queued per resource, and keep training files under 512 MB and 1 GB in total",
          "Create the deployment through the Resource Manager API with a Foundry Owner identity, then call it at least once a fortnight or it's deleted",
          "Query the Models API for `deprecationDate` before choosing a base model"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.3",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61.4
          }
        ],
        "editorialScores": {
          "ergonomics": 47,
          "maintenance": 55,
          "payments": 20,
          "reliability": 65,
          "schema": 67,
          "security": 85,
          "transparency": 81
        },
        "provenanceScore": 95
      },
      "connect": {
        "install": "pip install openai   # or: npm i openai",
        "http": "curl \"https://$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": "2026-10-04T23:48:04.611835303Z",
          "lastOk": false,
          "lastStatus": 0,
          "lastMs": 0,
          "lastNote": "DNS lookup failed",
          "authRequired": false,
          "uptime24h": 0,
          "uptime30d": 0,
          "p50ms24h": 0,
          "p95ms24h": 0,
          "samples24h": 272,
          "samples30d": 898,
          "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": 270,
              "ok": 0
            }
          ]
        },
        "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": [
          {
            "registry": "npm",
            "name": "openai",
            "version": "7.27.0",
            "seenAt": "2026-10-04T16:21:30.502857331Z"
          },
          {
            "registry": "pypi",
            "name": "openai",
            "version": "3.24.0",
            "released": "2026-10-02",
            "seenAt": "2026-10-04T16:21:30.317969684Z"
          }
        ],
        "npmWeekly": 50351921,
        "pypiWeekly": 72574929,
        "securityTxt": {
          "url": "https://microsoft.com/.well-known/security.txt",
          "state": "expired",
          "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",
            "kind": "changelog",
            "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": "2026-10-04T23:48:04.611835303Z"
      }
    },
    "b": {
      "slug": "together-fine-tuning",
      "name": "Together AI Fine-tuning",
      "vendor": "Together AI",
      "vendorUrl": "https://www.together.ai",
      "kind": "http-api",
      "category": "fine-tuning",
      "summary": "Managed LoRA and full fine-tuning, supervised or DPO, on about 30 open models from Qwen3.5 0.8B to Kimi K2.7, billed per training token with a $4 minimum.",
      "url": "https://www.anchorterminal.com/tools/together-fine-tuning",
      "markdownUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json",
      "repo": "https://github.com/togethercomputer/together-py",
      "license": "Apache-2.0 (SDKs)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.together.ai/v1",
      "packages": [
        {
          "registry": "pypi",
          "name": "together"
        },
        {
          "registry": "npm",
          "name": "together-ai"
        }
      ],
      "auth": "api-key",
      "authNotes": "`Authorization: Bearer` with the key from the console, read from `TOGETHER_API_KEY` by the SDKs and the `tg` CLI. One key covers files, fine-tuning jobs, downloads and endpoints.",
      "pricing": "usage",
      "pricingNotes": "Per training token, where tokens = epochs x training tokens + evaluations x validation tokens. LoRA SFT from $0.34 per 1M (Llama 3.1 8B, Qwen3.5 9B) through $1.05 (Qwen3.8 27B), $2.03 (Llama 3.3 70B), $2.50 (gpt-oss-120b), $7 (DeepSeek V3.1) and $15 (Kimi K2.6) to $40 (GLM-5.2). DPO is 2.5x the SFT rate ($0.84 for Llama 3.1 8B, $37.50 for Kimi K2.6). Full fine-tuning $0.38 (8B and 9B models) to $2.24 (Llama 3.3 70B). Minimum $4 a job, rising to $6 for gpt-oss-120b, $20 for DeepSeek V3.1 and $60 for Kimi K2.6. Hosting the result needs a dedicated endpoint; the pricing page lists dedicated endpoint GPUs at $5.49 an hour for an H100 and $8.99 for a B200, with H200 and B300 by quote. No free trial; access needs a $5 prepaid credit purchase (https://www.together.ai/pricing, https://docs.together.ai/docs/billing-credits).",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 10,
        "npmWeekly": 117852,
        "pypiWeekly": 369054,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://docs.together.ai/docs/fine-tuning/overview",
      "llmsTxt": "https://docs.together.ai/llms.txt",
      "openapi": "https://docs.together.ai/openapi.yaml",
      "capabilities": [
        "finetune.sft",
        "finetune.preference",
        "finetune.lora",
        "finetune.export"
      ],
      "tags": [
        "hosted",
        "usage-priced",
        "card-required",
        "open-weights",
        "llms-txt",
        "python",
        "typescript",
        "async-jobs"
      ],
      "lastRelease": "2026-09-30",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 54.9,
        "grade": "C",
        "agentReady": false,
        "rank": 319,
        "ranked": true,
        "rankOf": 452,
        "categoryRank": 4,
        "methodology": "0.3",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 42,
          "maintenance": 80,
          "payments": 20,
          "reliability": 55,
          "schema": 78,
          "security": 50,
          "transparency": 70
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour.",
        "strengths": [
          "31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA",
          "GET /v1/finetune/download returns merged weights or the adapter, at any saved checkpoint",
          "POST /v1/fine-tunes/estimate-price quotes a job before it runs",
          "Project-scoped API keys with expiry dates from 1 hour",
          "Python and TypeScript SDKs, an OpenAPI file and llms.txt"
        ],
        "weaknesses": [
          "Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour",
          "No free trial, a $5 prepaid purchase before the first call, and job minimums up to $60",
          "The status page covers serverless models only, and no fine-tuning rate limits are published",
          "No pagination on the job list and no documented error responses for fine-tuning calls",
          "No read-only project role and no audit log found"
        ],
        "agentNotes": [
          "Call POST /v1/fine-tunes/estimate-price with the same body before creating the job, and check the model's minimum charge",
          "Read `lora_training.max_rank` from the model limits response before setting `lora_r`; most models went to 128 on 2026-09-29",
          "Don't retry a create call blindly after a timeout; there's no idempotency key, so list jobs and check first",
          "Download with checkpoint=adapter if you'll merge locally; merged weights for a 70B model are a large stream",
          "Tear down the dedicated endpoint once evaluation ends, since it bills while idle"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.3",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 54.9
          }
        ],
        "editorialScores": {
          "ergonomics": 42,
          "maintenance": 80,
          "payments": 20,
          "reliability": 55,
          "schema": 78,
          "security": 50,
          "transparency": 55
        },
        "provenanceScore": 85
      },
      "connect": {
        "install": "pip install together   # or: npm i together-ai",
        "http": "curl https://api.together.ai/v1/fine-tunes \\\n  -H \"Authorization: Bearer $TOGETHER_API_KEY\" -H \"content-type: application/json\" \\\n  -d '{\"model\":\"Qwen/Qwen3.5-9B\",\"training_file\":\"file-abc123\",\"n_epochs\":3,\"training_type\":{\"type\":\"Lora\",\"lora_r\":16,\"lora_alpha\":32},\"training_method\":{\"method\":\"sft\"},\"suffix\":\"my-run\"}'"
      },
      "letme": {
        "capability": "https://letme.dev/finetune.sft",
        "tool": "https://letme.dev/together-fine-tuning"
      },
      "area": "models",
      "unitPrices": [
        {
          "item": "LoRA SFT, Llama 3.1 8B",
          "unit": "1m-tokens",
          "usd": 0.34,
          "note": "Same rate for Qwen3.5 9B. $4 minimum"
        },
        {
          "item": "LoRA DPO, Llama 3.1 8B",
          "unit": "1m-tokens",
          "usd": 0.84
        },
        {
          "item": "Full SFT, Llama 3.1 8B",
          "unit": "1m-tokens",
          "usd": 0.38
        },
        {
          "item": "LoRA SFT, Qwen3.8 27B",
          "unit": "1m-tokens",
          "usd": 1.05
        },
        {
          "item": "LoRA SFT, Llama 3.3 70B",
          "unit": "1m-tokens",
          "usd": 2.03,
          "note": "Full SFT $2.24"
        },
        {
          "item": "LoRA SFT, gpt-oss-120b",
          "unit": "1m-tokens",
          "usd": 2.5,
          "note": "$6 minimum"
        },
        {
          "item": "LoRA SFT, DeepSeek V3.1",
          "unit": "1m-tokens",
          "usd": 7,
          "note": "$20 minimum"
        },
        {
          "item": "LoRA SFT, Kimi K2.6",
          "unit": "1m-tokens",
          "usd": 15,
          "note": "$60 minimum"
        },
        {
          "item": "H100 on demand",
          "unit": "gpu-hour",
          "usd": 3.99
        },
        {
          "item": "H200 on demand",
          "unit": "gpu-hour",
          "usd": 5.99
        },
        {
          "item": "B200 on demand",
          "unit": "gpu-hour",
          "usd": 8.19
        }
      ],
      "provenance": {
        "legalEntity": "Together Computer, Inc.",
        "domain": "together.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": true,
        "terms": "https://www.together.ai/terms-of-service",
        "privacy": "https://www.together.ai/privacy",
        "statusPage": "https://status.together.ai",
        "changelog": "https://docs.together.ai/docs/changelog",
        "securityTxt": "valid",
        "checked": "2026-09-30",
        "notes": [
          "The terms (2026-05-19) name Together Computer, Inc., a Delaware corporation. The privacy policy (2025-12-17) says data isn't used to train models without opt-in.",
          "security.txt points Contact and Policy at hackerone.com/together_ai and has no Expires field.",
          "The status page monitors serverless inference models only; there's no fine-tuning component.",
          "The .ai registry's RDAP server refused our requests, so the registration date is blank.",
          "The MCP registry has a third-party io.usefulapi/together-ai server that wraps fine-tunes; Together doesn't publish one."
        ],
        "score": 85
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.json",
      "live": {
        "slug": "together-fine-tuning",
        "probe": {
          "target": "https://api.together.ai/v1",
          "method": "get",
          "lastAt": "2026-10-04T23:48:17.142790986Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 200,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 223,
          "p95ms24h": 550,
          "samples24h": 272,
          "samples30d": 898,
          "days": [
            {
              "date": "2026-10-01",
              "probes": 109,
              "ok": 109
            },
            {
              "date": "2026-10-02",
              "probes": 248,
              "ok": 248
            },
            {
              "date": "2026-10-03",
              "probes": 271,
              "ok": 271
            },
            {
              "date": "2026-10-04",
              "probes": 270,
              "ok": 270
            }
          ]
        },
        "vendorStatus": {
          "page": "https://status.together.ai",
          "indicator": "unknown",
          "summary": "no machine-readable status found",
          "checkedAt": "2026-10-04T21:40:31.633872227Z"
        },
        "versions": [
          {
            "registry": "github",
            "name": "togethercomputer/together-py",
            "version": "v2.39.0",
            "released": "2026-10-01",
            "seenAt": "2026-10-04T16:42:06.500144656Z"
          },
          {
            "registry": "npm",
            "name": "together-ai",
            "version": "0.57.0",
            "seenAt": "2026-10-04T16:42:05.689430855Z"
          },
          {
            "registry": "pypi",
            "name": "together",
            "version": "2.39.0",
            "released": "2026-10-01",
            "seenAt": "2026-10-04T16:42:05.492937716Z"
          }
        ],
        "githubStars": 10,
        "npmWeekly": 120148,
        "pypiWeekly": 377345,
        "securityTxt": {
          "url": "https://together.ai/.well-known/security.txt",
          "state": "valid",
          "checkedAt": "2026-10-04T15:16:01.636231106Z"
        },
        "llmsTxt": {
          "url": "https://docs.together.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-04T15:18:18.165758207Z"
        },
        "domain": {
          "domain": "together.ai",
          "registered": "2017-12-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/together.ai",
          "checkedAt": "2026-10-04T13:04:15.476837291Z"
        },
        "pages": [
          {
            "url": "https://docs.together.ai/docs/changelog",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-04T15:44:08.24409663Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "3820028b3e12"
          },
          {
            "url": "https://www.together.ai/pricing",
            "kind": "pricing",
            "status": 304,
            "checkedAt": "2026-10-04T15:52:28.018092824Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "6569e636eb45"
          },
          {
            "url": "https://www.together.ai/privacy",
            "kind": "privacy",
            "status": 304,
            "checkedAt": "2026-10-04T15:52:30.046079919Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "1ae1e2a8ef61"
          },
          {
            "url": "https://www.together.ai/terms-of-service",
            "kind": "terms",
            "status": 304,
            "checkedAt": "2026-10-04T15:52:32.055910677Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "c30ddbd90e92"
          }
        ],
        "updatedAt": "2026-10-04T23:48:17.142790986Z"
      }
    },
    "summary": "Microsoft Foundry fine-tuning (Azure OpenAI) has a score of 61.4 (C) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is security \u0026 auth, 35 points."
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning",
    "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning.md",
    "slim": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning.min.md"
  },
  "markdown": "Microsoft Foundry fine-tuning (Azure OpenAI) has a score of 61.4 (C) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is security \u0026 auth, 35 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- Together AI Fine-tuning: grade C, 54.9/100, rank #319 of 452. Markdown https://www.anchorterminal.com/tools/together-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json\n\n## Which one, for what\n\nPick Microsoft Foundry fine-tuning (Azure OpenAI) for reliability (+10), agent ergonomics (+5), security \u0026 auth (+35), transparency \u0026 trust (+18).\n\nPick Together AI Fine-tuning for schema \u0026 documentation (+11), maintenance \u0026 community (+25).\n\n## Score by category\n\n| Category | Weight | Microsoft Foundry fine-tuning (Azure OpenAI) | Together AI Fine-tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 65 | 55 | Microsoft Foundry fine-tuning (Azure OpenAI) +10 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 67 | 78 | Together AI Fine-tuning +11 |\n| Agent ergonomics | 13% (16.2 this run) | 47 | 42 | Microsoft Foundry fine-tuning (Azure OpenAI) +5 |\n| Security \u0026 auth | 14% (17.5 this run) | 85 | 50 | Microsoft Foundry fine-tuning (Azure OpenAI) +35 |\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 | Together AI Fine-tuning +25 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 88 | 70 | Microsoft Foundry fine-tuning (Azure OpenAI) +18 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61.4 · C** | **54.9 · C** | |\n\n## Facts side by side\n\n| Fact | Microsoft Foundry fine-tuning (Azure OpenAI) | Together AI Fine-tuning |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Microsoft Azure | Together AI |\n| Hosted endpoint | `https://\u003cresource\u003e.openai.azure.com/openai/v1` | `https://api.together.ai/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 | Apache-2.0 (SDKs) |\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 | yes |\n| MCP registry | not listed | not listed |\n| Last release | none | 2026-09-30 |\n| Popularity | 47.2M npm/wk, 72.1M PyPI/wk | 10 stars, 118k npm/wk, 369k PyPI/wk |\n| Agent reviews | 3.5/5 (2) | 3/5 (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**Together AI Fine-tuning.** 31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour.\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### Together AI Fine-tuning\n\n1. Call POST /v1/fine-tunes/estimate-price with the same body before creating the job, and check the model's minimum charge\n2. Read `lora_training.max_rank` from the model limits response before setting `lora_r`; most models went to 128 on 2026-09-29\n3. Don't retry a create call blindly after a timeout; there's no idempotency key, so list jobs and check first\n4. Download with checkpoint=adapter if you'll merge locally; merged weights for a 70B model are a large stream\n5. Tear down the dedicated endpoint once evaluation ends, since it bills while idle\n\n## Other comparisons with Microsoft Foundry fine-tuning (Azure OpenAI) or Together AI Fine-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 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 Together AI Fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning.md)\n- [Tinker vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.md)\n- [Together AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.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",
  "meta": {
    "attribution": "Anchor Terminal (https://www.anchorterminal.com)",
    "docs": "https://www.anchorterminal.com/docs/",
    "generatedAt": "2026-10-04",
    "license": "CC-BY-4.0",
    "method": "https://www.anchorterminal.com/benchmark/",
    "methodology": "0.3",
    "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 Together AI Fine-tuning",
        "url": ""
      }
    ],
    "description": "Microsoft Foundry fine-tuning (Azure OpenAI) has a score of 61.4 (C) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is security \u0026 auth, 35 points. Category scores, facts, verdicts and agent notes side by side.",
    "facts": [
      "Microsoft Foundry fine-tuning (Azure OpenAI) C 61.4",
      "Together AI Fine-tuning C 54.9",
      "scores"
    ],
    "h1": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI Fine-tuning",
    "image": "https://www.anchorterminal.com/assets/og/compare-azure-foundry-fine-tuning-vs-together-fine-tuning.png",
    "path": "/compare/azure-foundry-fine-tuning-vs-together-fine-tuning",
    "published": "2026-10-01",
    "section": "tools",
    "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI…",
    "toc": null,
    "updated": "2026-10-04",
    "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning"
  },
  "tokens": {
    "markdown": 1650,
    "slim": 380
  },
  "version": 1
}