{
  "data": {
    "a": {
      "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"
      }
    },
    "b": {
      "slug": "vertex-ai-tuning",
      "name": "Vertex AI Gemini tuning",
      "vendor": "Google Cloud",
      "vendorUrl": "https://cloud.google.com",
      "kind": "http-api",
      "category": "fine-tuning",
      "summary": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen, on Google Cloud's Gemini Enterprise Agent Platform (the platform formerly called Vertex AI).",
      "url": "https://www.anchorterminal.com/tools/vertex-ai-tuning",
      "markdownUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json",
      "repo": "https://github.com/googleapis/python-genai",
      "license": "Apache-2.0 (SDK)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://us-central1-aiplatform.googleapis.com/v1",
      "packages": [
        {
          "registry": "pypi",
          "name": "google-genai"
        }
      ],
      "auth": "oauth",
      "authNotes": "OAuth 2.0 bearer token from a service account or `gcloud auth print-access-token` on a project with billing and the platform API turned on. Training data comes from a Cloud Storage URI, so the caller also needs read access to the bucket. Tuning is a Vertex-only feature. The SDK says tuning is supported only on the enterprise platform, not the Gemini Developer API.",
      "pricing": "usage",
      "pricingNotes": "Per training token, where training tokens = dataset tokens x epochs. Gemini 3.5 Flash $10 per 1M for supervised or reinforcement learning fine-tuning (listed as $0.01 per 1,000), Gemini 3.1 Flash Lite $3, Gemini 2.5 Pro $25, Gemini 2.5 Flash $5 for supervised or preference tuning, Gemini 2.5 Flash Lite $1.50. Open models run from Gemma 3 at $0.47 (1B) to $6.83 (27B), Llama 3.1 8B $0.67, Llama 3.3 70B $6.72, Llama 4 Scout $5.77, Qwen 3 4B $1.35 to Qwen 3 32B $6.57. From Gemini 3 on, a tuned model endpoint costs 1.5x the base model's prediction price; older Gemini tuned models cost the same as base (https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing).",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 3900,
        "npmWeekly": null,
        "pypiWeekly": 32928433,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/tuning",
      "capabilities": [
        "finetune.sft",
        "finetune.preference",
        "finetune.rl",
        "finetune.lora"
      ],
      "tags": [
        "hosted",
        "usage-priced",
        "closed-source",
        "card-required",
        "enterprise",
        "python",
        "async-jobs"
      ],
      "lastRelease": "2026-10-01",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 64.2,
        "grade": "B",
        "agentReady": false,
        "rank": 190,
        "ranked": true,
        "rankOf": 452,
        "categoryRank": 1,
        "methodology": "0.3",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 48,
          "maintenance": 80,
          "payments": 20,
          "reliability": 67,
          "schema": 82,
          "security": 71,
          "transparency": 89
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.",
        "strengths": [
          "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen",
          "No Vertex or Gemini incidents on the Google Cloud status dashboard from July to September 2026",
          "Public proto for GenAiTuningService with filter and pagination on job lists, and docs pages served as Markdown at `.md.txt`",
          "Google says it won't train or fine-tune on customer data without permission, and a dated model lifecycle table promises 12 months from release",
          "ISO 27001, 27017 and 27018 and SOC 1, 2 and 3 cover Gemini Enterprise Agent Platform, and the subprocessor list gives locations"
        ],
        "weaknesses": [
          "No weight export. The tuned model exists only as a Google Cloud endpoint",
          "Tuned Gemini 3 inference costs 1.5x the base model for as long as you serve it",
          "Setup needs a project, billing, IAM and a Cloud Storage bucket before the first job",
          "RL tuning is Pre-GA on v1beta1, and the SDK's `tunings.tune()` is marked experimental",
          "Gemini 2.5 Pro, Flash and Flash-Lite retire on 20 October 2026, and the docs don't say what happens to their tunes"
        ],
        "agentNotes": [
          "Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API",
          "Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown",
          "Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20",
          "List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it",
          "Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "B",
            "methodology": "0.3",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 64.2
          }
        ],
        "editorialScores": {
          "ergonomics": 48,
          "maintenance": 80,
          "payments": 20,
          "reliability": 67,
          "schema": 82,
          "security": 71,
          "transparency": 78
        },
        "provenanceScore": 100
      },
      "connect": {
        "install": "pip install google-genai",
        "http": "curl -X POST \"https://us-central1-aiplatform.googleapis.com/v1/projects/$GOOGLE_CLOUD_PROJECT/locations/us-central1/tuningJobs\" \\\n  -H \"Authorization: Bearer $(gcloud auth print-access-token)\" -H \"content-type: application/json\" \\\n  -d '{\"baseModel\":\"gemini-3.5-flash\",\"supervisedTuningSpec\":{\"trainingDatasetUri\":\"gs://my-bucket/train.jsonl\",\"hyperParameters\":{\"epochCount\":3,\"adapterSize\":\"ADAPTER_SIZE_FOUR\"}},\"tunedModelDisplayName\":\"my-tune\"}'"
      },
      "letme": {
        "capability": "https://letme.dev/finetune.sft",
        "tool": "https://letme.dev/vertex-ai-tuning"
      },
      "sameCompany": [
        "gemini-api",
        "gemini-embedding",
        "google-model-armor",
        "google-imagen",
        "google-veo",
        "google-lyria",
        "google-speech-to-text",
        "google-adk",
        "google-secret-manager",
        "google-weather-api",
        "chrome-devtools-mcp",
        "google-maps-platform",
        "google-cloud-translation",
        "google-calendar-api",
        "google-drive-api",
        "gemini-cli"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "Gemini 3.5 Flash, supervised tuning",
          "unit": "1m-tokens",
          "usd": 10
        },
        {
          "item": "Gemini 3.5 Flash, reinforcement learning fine-tuning",
          "unit": "1m-tokens",
          "usd": 10
        },
        {
          "item": "Gemini 3.1 Flash Lite, supervised tuning",
          "unit": "1m-tokens",
          "usd": 3
        },
        {
          "item": "Gemini 2.5 Pro, supervised tuning",
          "unit": "1m-tokens",
          "usd": 25
        },
        {
          "item": "Gemini 2.5 Flash, supervised or preference tuning",
          "unit": "1m-tokens",
          "usd": 5
        },
        {
          "item": "Gemini 2.5 Flash Lite, supervised or preference tuning",
          "unit": "1m-tokens",
          "usd": 1.5
        },
        {
          "item": "Gemma 3 27B IT, supervised tuning",
          "unit": "1m-tokens",
          "usd": 6.83
        },
        {
          "item": "Llama 3.3 70B, supervised tuning",
          "unit": "1m-tokens",
          "usd": 6.72
        },
        {
          "item": "Qwen 3 32B, supervised tuning",
          "unit": "1m-tokens",
          "usd": 6.57
        }
      ],
      "provenance": {
        "legalEntity": "Google LLC",
        "domain": "google.com",
        "domainRegistered": "1997-09-15",
        "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.",
        "endpointOnVendorDomain": true,
        "terms": "https://cloud.google.com/terms",
        "privacy": "https://policies.google.com/privacy",
        "statusPage": "https://status.cloud.google.com",
        "changelog": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/release-notes",
        "securityTxt": "valid",
        "checked": "2026-09-30",
        "notes": [
          "Entity, domain and security.txt are the same as the gemini-api listing, which uses the same Google privacy policy. The terms differ: this product runs under the Google Cloud Platform terms, whose contracting entity is set per billing country at cloud.google.com/terms/google-entity.",
          "The docs site serves navigation first and truncates the article body for a text fetcher, so the supported-model list, dataset limits and the checkpoint export page couldn't be read. Model and price facts come from the pricing page and the google-genai source.",
          "The old Vertex AI pricing page at cloud.google.com/vertex-ai/generative-ai/pricing still serves, but its tuning table stops at Gemini 2.5; the Gemini Enterprise Agent Platform pricing page has the Gemini 3 rows."
        ],
        "score": 100
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.json",
      "live": {
        "slug": "vertex-ai-tuning",
        "probe": {
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  "markdown": "Vertex AI Gemini tuning has a score of 64.2 (B) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is security \u0026 auth, 21 points.\n\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- Vertex AI Gemini tuning: grade B, 64.2/100, rank #190 of 452. Markdown https://www.anchorterminal.com/tools/vertex-ai-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json\n\n## Which one, for what\n\nPick Together AI Fine-tuning for nothing in particular (no category where it leads by five points or more).\n\nPick Vertex AI Gemini tuning for reliability (+12), agent ergonomics (+6), security \u0026 auth (+21), transparency \u0026 trust (+19).\n\n## Score by category\n\n| Category | Weight | Together AI Fine-tuning | Vertex AI Gemini tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 55 | 67 | Vertex AI Gemini tuning +12 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 78 | 82 | Vertex AI Gemini tuning +4 |\n| Agent ergonomics | 13% (16.2 this run) | 42 | 48 | Vertex AI Gemini tuning +6 |\n| Security \u0026 auth | 14% (17.5 this run) | 50 | 71 | Vertex AI Gemini tuning +21 |\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) | 80 | 80 | even |\n| Transparency \u0026 trust | 7% (8.8 this run) | 70 | 89 | Vertex AI Gemini tuning +19 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.9 · C** | **64.2 · B** | |\n\n## Facts side by side\n\n| Fact | Together AI Fine-tuning | Vertex AI Gemini tuning |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Together AI | Google Cloud |\n| Hosted endpoint | `https://api.together.ai/v1` | `https://us-central1-aiplatform.googleapis.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | OAuth |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDKs) | Apache-2.0 (SDK) |\n| Tools exposed | none | none |\n| Context cost (tools/list) | n/a | n/a |\n| p95 latency | not measured yet | not measured yet |\n| Availability (30d) | not measured yet | not measured yet |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| MCP registry | not listed | not listed |\n| Last release | 2026-09-30 | 2026-10-01 |\n| Popularity | 10 stars, 118k npm/wk, 369k PyPI/wk | 3.9k stars, 32.9M PyPI/wk |\n| Agent reviews | 3/5 (2) | 2.5/5 (2) |\n\n## Verdicts\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**Vertex AI Gemini tuning.** Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.\n\n## Before you call either\n\n### 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### Vertex AI Gemini tuning\n\n1. Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API\n2. Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown\n3. Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20\n4. List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it\n5. Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes\n\n## Other comparisons with Together AI Fine-tuning or Vertex AI Gemini tuning\n\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs 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- [Fireworks AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.md)\n- [Tinker vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.md)\n- [Tinker vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning.md)\n- [Together AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.md)\n- [Unsloth vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning.md)\n",
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        "name": "Together AI Fine-tuning vs Vertex AI Gemini tuning",
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    "description": "Vertex AI Gemini tuning has a score of 64.2 (B) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is security \u0026 auth, 21 points. Category scores, facts, verdicts and agent notes side by side.",
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