{
  "data": {
    "category": {
      "area": "models",
      "capabilities": [
        "finetune.sft",
        "finetune.preference",
        "finetune.rl",
        "finetune.lora",
        "finetune.export"
      ],
      "description": "Services that train a model on your examples, by supervised, preference or reinforcement fine-tuning, and serve the result. Compared on which base models you can tune, the methods, price per training token, whether you get the weights and what serving the result costs.",
      "json": "https://www.anchorterminal.com/categories/fine-tuning.json",
      "name": "Fine-tuning",
      "slug": "fine-tuning",
      "test": "The same small dataset used to tune a comparable open model on each service, then served. We check the job flow, how long training takes, whether the weights can leave, and the training and serving cost.",
      "title": "Fine-tuning services for AI models",
      "toolCount": 6,
      "tools": [
        "vertex-ai-tuning",
        "azure-foundry-fine-tuning",
        "fireworks-fine-tuning",
        "together-fine-tuning",
        "unsloth",
        "tinker"
      ],
      "url": "https://www.anchorterminal.com/categories/fine-tuning"
    },
    "tools": [
      {
        "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",
          "probe": {
            "target": "https://us-central1-aiplatform.googleapis.com/v1",
            "method": "get",
            "lastAt": "2026-10-05T01:43:47.71875277Z",
            "lastOk": true,
            "lastStatus": 404,
            "lastMs": 599,
            "authRequired": false,
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            "samples30d": 920,
            "days": [
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                "probes": 109,
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              {
                "date": "2026-10-02",
                "probes": 248,
                "ok": 248
              },
              {
                "date": "2026-10-03",
                "probes": 271,
                "ok": 271
              },
              {
                "date": "2026-10-04",
                "probes": 272,
                "ok": 272
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              {
                "date": "2026-10-05",
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            ]
          },
          "versions": [
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              "registry": "github",
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              "version": "v2.28.0",
              "released": "2026-10-02",
              "seenAt": "2026-10-04T16:43:23.256991186Z"
            },
            {
              "registry": "pypi",
              "name": "google-genai",
              "version": "2.28.0",
              "released": "2026-10-02",
              "seenAt": "2026-10-04T16:43:23.140253261Z"
            }
          ],
          "githubStars": 4002,
          "pypiWeekly": 34122162,
          "securityTxt": {
            "url": "https://google.com/.well-known/security.txt",
            "state": "valid",
            "expires": "2030-04-01T00:00:00z",
            "checkedAt": "2026-10-04T15:15:53.387118101Z"
          },
          "domain": {
            "domain": "google.com",
            "registered": "1997-09-15",
            "source": "https://rdap.verisign.com/com/v1/domain/google.com",
            "checkedAt": "2026-10-04T13:05:50.737985829Z"
          },
          "pages": [
            {
              "url": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/release-notes",
              "kind": "changelog",
              "status": 200,
              "checkedAt": "2026-10-04T15:43:22.79897489Z",
              "changedAt": "0001-01-01T00:00:00Z",
              "fingerprint": "410db995b736"
            },
            {
              "url": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing",
              "kind": "pricing",
              "status": 200,
              "checkedAt": "2026-10-04T15:41:53.106464837Z",
              "changedAt": "0001-01-01T00:00:00Z",
              "fingerprint": "793e43bfda77"
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          ],
          "updatedAt": "2026-10-05T01:43:47.71875277Z"
        }
      },
      {
        "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": "2026-10-05T01:43:33.569241833Z",
            "lastOk": false,
            "lastStatus": 0,
            "lastMs": 0,
            "lastNote": "DNS lookup failed",
            "authRequired": false,
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            "samples24h": 272,
            "samples30d": 920,
            "days": [
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                "date": "2026-10-01",
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                "ok": 0
              },
              {
                "date": "2026-10-02",
                "probes": 248,
                "ok": 0
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              {
                "date": "2026-10-03",
                "probes": 271,
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              {
                "date": "2026-10-04",
                "probes": 272,
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              {
                "date": "2026-10-05",
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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": [
            {
              "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,
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              "fingerprint": "237edf8365ef"
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            {
              "url": "https://prices.azure.com/api/retail/prices",
              "kind": "pricing",
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            },
            {
              "url": "https://www.microsoft.com/licensing/terms/product/ForOnlineServices/all",
              "kind": "terms",
              "status": 502,
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          ],
          "updatedAt": "2026-10-05T01:43:33.569241833Z"
        }
      },
      {
        "slug": "fireworks-fine-tuning",
        "name": "Fireworks AI Fine-tuning",
        "vendor": "Fireworks AI",
        "vendorUrl": "https://fireworks.ai",
        "kind": "http-api",
        "category": "fine-tuning",
        "summary": "Managed supervised, preference and reinforcement fine-tuning for open models, with a training API for custom workflows.",
        "url": "https://www.anchorterminal.com/tools/fireworks-fine-tuning",
        "markdownUrl": "https://www.anchorterminal.com/tools/fireworks-fine-tuning.md",
        "slimMarkdownUrl": "https://www.anchorterminal.com/tools/fireworks-fine-tuning.min.md",
        "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json",
        "repo": "https://github.com/fw-ai-external/python-sdk",
        "license": "Apache-2.0 (SDK)",
        "transports": [
          "http"
        ],
        "remoteUrl": "https://api.fireworks.ai",
        "packages": [
          {
            "registry": "pypi",
            "name": "fireworks-ai"
          }
        ],
        "auth": "api-key",
        "authNotes": "`Authorization: Bearer` with an account key, read from `FIREWORKS_API_KEY` by the SDK and `firectl`. The Training API wants a training-scoped key. Resources are addressed as accounts/\u003caccount\u003e/..., and the SDK resolves the account from the key.",
        "pricing": "usage",
        "pricingNotes": "Managed training per 1M training tokens by model size. LoRA SFT $0.50 up to 16B parameters, $3 from 16.1B to 80B, $6 from 80B to 300B, $10 above; DPO is double, and full-parameter training is double LoRA. Serving a fine-tuned model costs the same as the base model. The serverless Training API is priced per model (Qwen 3.8 27B at $4.103 per 1M training tokens); dedicated training is $8 a GPU-hour for an H100 or H200, $13 for a B200, $15 for a B300 and $20 for a GB300, effective 2026-09-01. On-demand inference deployments cost $8 an hour for an H100 or H200 and $13 for a B200. New accounts get $1 of credit (https://fireworks.ai/pricing).",
        "priceSummary": "Pay per use",
        "where": "hosted",
        "x402": {
          "level": "no",
          "endpoints": []
        },
        "toolCount": null,
        "popularity": {
          "githubStars": 7,
          "npmWeekly": null,
          "pypiWeekly": 290162,
          "asOf": "2026-09-30"
        },
        "docsUrl": "https://docs.fireworks.ai/fine-tuning/fine-tuning-models",
        "llmsTxt": "https://docs.fireworks.ai/llms.txt",
        "openapi": "https://docs.fireworks.ai/merged.openapi.yaml",
        "capabilities": [
          "finetune.sft",
          "finetune.preference",
          "finetune.rl",
          "finetune.lora",
          "finetune.export"
        ],
        "tags": [
          "hosted",
          "usage-priced",
          "card-required",
          "open-weights",
          "llms-txt",
          "python",
          "async-jobs"
        ],
        "lastRelease": "2026-10-01",
        "graded": true,
        "anchor": {
          "graded": true,
          "score": 59.2,
          "grade": "C",
          "agentReady": false,
          "rank": 269,
          "ranked": true,
          "rankOf": 452,
          "categoryRank": 3,
          "methodology": "0.3",
          "run": "2026-10-01",
          "scores": {
            "ergonomics": 75,
            "maintenance": 82,
            "payments": 25,
            "reliability": 55,
            "schema": 77,
            "security": 65,
            "transparency": 66
          },
          "pending": [
            "performance",
            "tasks"
          ],
          "assessment": {
            "confidence": "medium",
            "date": "2026-10-01"
          },
          "negative": -4,
          "negativeNotes": [
            "-4: on 2026-08-26 the changelog deprecated Qwen 3.5 9B and Qwen 3.6 27B from Serverless Training 'effective August 26, 2026', with no earlier entry announcing it, and told users to move existing workloads to Qwen 3.8 27B (https://docs.fireworks.ai/updates/changelog)"
          ],
          "verdict": "SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless.",
          "strengths": [
            "SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available",
            "LoRA SFT from $0.50 per 1M training tokens up to 16B parameters, with serving at base-model prices",
            "List endpoints take readMask, pageSize up to 200, AIP-160 filters and orderBy",
            "An Inference User role and revocable keys with an expiry date",
            "A public control-plane OpenAPI file, llms.txt and Markdown twins of every docs page"
          ],
          "weaknesses": [
            "Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless",
            "No training without a payment method; the $1 sign-up credit buys inference only",
            "The status page has no training component; a 46-hour cloud provider incident in August hit dedicated deployments",
            "Two Serverless Training base models were deprecated with same-day effect on 2026-08-26",
            "Audit logs are Enterprise only and security.txt returns 404"
          ],
          "agentNotes": [
            "Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute",
            "Check `firectl model get -a fireworks \u003cMODEL-ID\u003e` for Tunable: true before uploading a dataset",
            "Pass your own `supervisedFineTuningJobId` on create, so after a timeout you can GET the job by that name instead of guessing whether it started",
            "Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends",
            "Download with `firectl model download` and keep the exact base model; the adapter alone won't run"
          ],
          "metrics": {
            "kind": "remote",
            "measured": false
          },
          "reviewCount": 2,
          "avgRating": 2.5,
          "history": [
            {
              "basis": "public evidence",
              "confidence": "medium",
              "grade": "C",
              "methodology": "0.3",
              "pending": [
                "performance",
                "tasks"
              ],
              "run": "2026-10-01",
              "runLabel": "October 2026 research run",
              "score": 59.2
            }
          ],
          "editorialScores": {
            "ergonomics": 75,
            "maintenance": 82,
            "payments": 25,
            "reliability": 55,
            "schema": 77,
            "security": 65,
            "transparency": 57
          },
          "provenanceScore": 75
        },
        "connect": {
          "install": "pip install fireworks-ai   # add [training] for the Training API",
          "http": "curl https://api.fireworks.ai/v1/accounts/$FIREWORKS_ACCOUNT_ID/supervisedFineTuningJobs \\\n  -H \"Authorization: Bearer $FIREWORKS_API_KEY\" -H \"content-type: application/json\" \\\n  -d '{\"baseModel\":\"accounts/fireworks/models/gemma-4-31b-it\",\"dataset\":\"accounts/'$FIREWORKS_ACCOUNT_ID'/datasets/my-dataset\",\"outputModel\":\"accounts/'$FIREWORKS_ACCOUNT_ID'/models/my-tune\",\"loraRank\":16}'"
        },
        "letme": {
          "capability": "https://letme.dev/finetune.sft",
          "tool": "https://letme.dev/fireworks-fine-tuning"
        },
        "area": "models",
        "unitPrices": [
          {
            "item": "LoRA SFT, models up to 16B",
            "unit": "1m-tokens",
            "usd": 0.5
          },
          {
            "item": "LoRA DPO, models up to 16B",
            "unit": "1m-tokens",
            "usd": 1
          },
          {
            "item": "Full-parameter SFT, models up to 16B",
            "unit": "1m-tokens",
            "usd": 1
          },
          {
            "item": "LoRA SFT, 16.1B to 80B",
            "unit": "1m-tokens",
            "usd": 3
          },
          {
            "item": "LoRA SFT, 80B to 300B",
            "unit": "1m-tokens",
            "usd": 6
          },
          {
            "item": "LoRA SFT, over 300B",
            "unit": "1m-tokens",
            "usd": 10
          },
          {
            "item": "Serverless Training API, Qwen 3.8 27B",
            "unit": "1m-tokens",
            "usd": 4.103
          },
          {
            "item": "Dedicated training, H100 or H200",
            "unit": "gpu-hour",
            "usd": 8,
            "note": "Effective 2026-09-01"
          },
          {
            "item": "Dedicated training, B200",
            "unit": "gpu-hour",
            "usd": 13
          },
          {
            "item": "Dedicated training, B300",
            "unit": "gpu-hour",
            "usd": 15
          },
          {
            "item": "Dedicated training, GB300",
            "unit": "gpu-hour",
            "usd": 20
          }
        ],
        "provenance": {
          "legalEntity": "Fireworks.ai, Inc.",
          "domain": "fireworks.ai",
          "domainRegistered": "",
          "endpointOnVendorDomain": true,
          "terms": "https://fireworks.ai/terms-of-service",
          "privacy": "https://fireworks.ai/privacy-policy",
          "statusPage": "https://status.fireworks.ai",
          "changelog": "https://docs.fireworks.ai/updates/changelog",
          "securityTxt": "none",
          "checked": "2026-09-30",
          "notes": [
            "The privacy policy, last updated 8/11/2026, names Fireworks.ai, Inc. and gives no address.",
            "fireworks.ai/robots.txt disallows the terms of service page to crawlers, so we read the entity from the privacy policy instead.",
            "fireworks.ai/.well-known/security.txt returns 404.",
            "The status page lists 16 serverless model components and no training component.",
            "The .ai registry's RDAP server refused our requests, so the registration date is blank.",
            "The MCP registry holds a third-party io.usefulapi/fireworks server; Fireworks doesn't publish one."
          ],
          "score": 75
        },
        "pageJsonUrl": "https://www.anchorterminal.com/tools/fireworks-fine-tuning.json",
        "live": {
          "slug": "fireworks-fine-tuning",
          "probe": {
            "target": "https://api.fireworks.ai",
            "method": "get",
            "lastAt": "2026-10-05T01:43:37.694460289Z",
            "lastOk": true,
            "lastStatus": 404,
            "lastMs": 47,
            "authRequired": false,
            "uptime24h": 100,
            "uptime30d": 100,
            "p50ms24h": 29,
            "p95ms24h": 65,
            "samples24h": 272,
            "samples30d": 920,
            "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": 272,
                "ok": 272
              },
              {
                "date": "2026-10-05",
                "probes": 20,
                "ok": 20
              }
            ]
          },
          "vendorStatus": {
            "page": "https://status.fireworks.ai",
            "indicator": "none",
            "summary": "All Systems Operational",
            "checkedAt": "2026-10-05T01:46:32.519185764Z"
          },
          "versions": [
            {
              "registry": "github",
              "name": "fw-ai-external/python-sdk",
              "version": "v1.2.19",
              "released": "2026-10-02",
              "seenAt": "2026-10-04T16:27:15.503949556Z"
            },
            {
              "registry": "pypi",
              "name": "fireworks-ai",
              "version": "1.2.19",
              "released": "2026-10-02",
              "seenAt": "2026-10-04T16:27:11.794181655Z"
            }
          ],
          "githubStars": 10,
          "pypiWeekly": 274409,
          "securityTxt": {
            "url": "https://fireworks.ai/.well-known/security.txt",
            "state": "none",
            "checkedAt": "2026-10-04T15:16:04.762370371Z"
          },
          "llmsTxt": {
            "url": "https://docs.fireworks.ai/llms.txt",
            "ok": true,
            "status": 200,
            "checkedAt": "2026-10-04T15:17:46.770278727Z"
          },
          "domain": {
            "domain": "fireworks.ai",
            "registered": "2020-03-11",
            "source": "https://rdap.identitydigital.services/rdap/domain/fireworks.ai",
            "checkedAt": "2026-10-04T13:05:45.131398465Z"
          },
          "pages": [
            {
              "url": "https://docs.fireworks.ai/updates/changelog",
              "kind": "changelog",
              "status": 200,
              "checkedAt": "2026-10-04T15:43:37.188332196Z",
              "changedAt": "2026-10-03T15:31:45.792871079Z",
              "fingerprint": "5ee4d578feb4"
            },
            {
              "url": "https://fireworks.ai/pricing",
              "kind": "pricing",
              "status": 200,
              "checkedAt": "2026-10-04T15:44:43.473872205Z",
              "changedAt": "0001-01-01T00:00:00Z",
              "fingerprint": "9f6b298d1e67"
            },
            {
              "url": "https://fireworks.ai/privacy-policy",
              "kind": "privacy",
              "status": 200,
              "checkedAt": "2026-10-04T15:44:45.619814106Z",
              "changedAt": "0001-01-01T00:00:00Z",
              "fingerprint": "7220d287a92b"
            },
            {
              "url": "https://fireworks.ai/terms-of-service",
              "kind": "terms",
              "status": 200,
              "checkedAt": "2026-10-04T15:44:47.623501485Z",
              "changedAt": "0001-01-01T00:00:00Z"
            }
          ],
          "updatedAt": "2026-10-05T01:46:32.519185764Z"
        }
      },
      {
        "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"
          },
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  "markdown": "Services that train a model on your examples, by supervised, preference or reinforcement fine-tuning, and serve the result. Compared on which base models you can tune, the methods, price per training token, whether you get the weights and what serving the result costs.\n\n- Tools ranked: 6 · agent-ready (BB or better): 0 · accept x402: 0 · hosted endpoints: 4 · desk reviews by the panel: 12\n- JSON: https://www.anchorterminal.com/api/v1/tools.json (list) · https://www.anchorterminal.com/api/v1/rankings.json (ranked) · https://www.anchorterminal.com/api/v1/x402.json (payable) · https://www.anchorterminal.com/api/v1/capabilities.json (by capability)\n- Grades run AA, A, BB, B, C, D, E, F · methodology: https://www.anchorterminal.com/benchmark/\n\n- Capabilities in this category: finetune.sft, finetune.preference, finetune.rl, finetune.lora, finetune.export\n- https://letme.dev/finetune.sft picks the top-graded tool in this list and says how to call it direct; calling through letme comes later (https://www.anchorterminal.com/letme/index.md)\n\n## Ranking\n\n| # | Tool | Vendor | Kind | Category | Grade | Score | Confidence | x402 | Auth | Where | Reviews | Page |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 190 | Vertex AI Gemini tuning | Google Cloud | HTTP API | Fine-tuning | B | 64.2 | medium | no | OAuth | hosted | 2.5/5 (2) | https://www.anchorterminal.com/tools/vertex-ai-tuning.md |\n| 228 | Microsoft Foundry fine-tuning (Azure OpenAI) | Microsoft Azure | HTTP API | Fine-tuning | C | 61.4 | medium | no | OAuth or key | hosted | 3.5/5 (2) | https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.md |\n| 269 | Fireworks AI Fine-tuning | Fireworks AI | HTTP API | Fine-tuning | C | 59.2 | medium | no | API key | hosted | 2.5/5 (2) | https://www.anchorterminal.com/tools/fireworks-fine-tuning.md |\n| 319 | Together AI Fine-tuning | Together AI | HTTP API | Fine-tuning | C | 54.9 | medium | no | API key | hosted | 3/5 (2) | https://www.anchorterminal.com/tools/together-fine-tuning.md |\n| 347 | Unsloth | Unsloth | Agent framework | Fine-tuning | D | 51.7 | medium | no | None | library | 3.5/5 (2) | https://www.anchorterminal.com/tools/unsloth.md |\n| 354 | Tinker | Thinking Machines Lab | SDK + MCP | Fine-tuning | D | 51.2 | medium | no | API key | local | 3.5/5 (2) | https://www.anchorterminal.com/tools/tinker.md |\n\nScores are from public evidence against the published checklist (https://www.anchorterminal.com/benchmark/), with Performance and Task success pending. p95 latency and context cost come from our probes, which haven't run yet.\n\n## Summaries\n\n### 190. Vertex AI Gemini tuning, B (64.2)\n\nSupervised, 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). 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- Page: https://www.anchorterminal.com/tools/vertex-ai-tuning · Markdown: https://www.anchorterminal.com/tools/vertex-ai-tuning.md · JSON: https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json\n- Capabilities: finetune.sft, finetune.preference, finetune.rl, finetune.lora · endpoint: `https://us-central1-aiplatform.googleapis.com/v1`\n\n### 228. Microsoft Foundry fine-tuning (Azure OpenAI), C (61.4)\n\nAzure's managed service for supervised, preference and reinforcement fine-tuning of supported OpenAI and open-weight models. 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- Page: https://www.anchorterminal.com/tools/azure-foundry-fine-tuning · 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- Capabilities: finetune.sft, finetune.preference, finetune.rl, finetune.lora · endpoint: `https://\u003cresource\u003e.openai.azure.com/openai/v1`\n\n### 269. Fireworks AI Fine-tuning, C (59.2)\n\nManaged supervised, preference and reinforcement fine-tuning for open models, with a training API for custom workflows. SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless.\n\n- Page: https://www.anchorterminal.com/tools/fireworks-fine-tuning · Markdown: https://www.anchorterminal.com/tools/fireworks-fine-tuning.md · JSON: https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json\n- Capabilities: finetune.sft, finetune.preference, finetune.rl, finetune.lora, finetune.export · endpoint: `https://api.fireworks.ai`\n\n### 319. Together AI Fine-tuning, C (54.9)\n\nManaged 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. 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- Page: https://www.anchorterminal.com/tools/together-fine-tuning · Markdown: https://www.anchorterminal.com/tools/together-fine-tuning.md · JSON: https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json\n- Capabilities: finetune.sft, finetune.preference, finetune.lora, finetune.export · endpoint: `https://api.together.ai/v1`\n\n### 347. Unsloth, D (51.7)\n\nOpen-source library, web UI (Studio) and desktop app for LoRA, QLoRA, full fine-tuning and RL (GRPO, DPO, ORPO) of open models on your own GPU, from 3 GB of VRAM. The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.\n\n- Page: https://www.anchorterminal.com/tools/unsloth · Markdown: https://www.anchorterminal.com/tools/unsloth.md · JSON: https://www.anchorterminal.com/api/v1/tools/unsloth.json\n- Capabilities: finetune.sft, finetune.preference, finetune.rl, finetune.lora, finetune.export\n\n### 354. Tinker, D (51.2)\n\nThinking Machines Lab's API for model training. Full control of the training loop with the GPUs abstracted away, plus recipes for SFT, DPO, RL and distillation. LoRA only; no full-parameter training.\n\n- Page: https://www.anchorterminal.com/tools/tinker · Markdown: https://www.anchorterminal.com/tools/tinker.md · JSON: https://www.anchorterminal.com/api/v1/tools/tinker.json\n- Capabilities: finetune.sft, finetune.preference, finetune.rl, finetune.lora, finetune.export\n\n## How we test this category\n\nThe same small dataset used to tune a comparable open model on each service, then served. We check the job flow, how long training takes, whether the weights can leave, and the training and serving cost. This test hasn't run yet, so Task success is pending and the grades here come from the categories assessed from public evidence.\n\n",
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