{
  "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": "unsloth",
      "name": "Unsloth",
      "vendor": "Unsloth",
      "vendorUrl": "https://unsloth.ai",
      "kind": "framework",
      "category": "fine-tuning",
      "summary": "Open-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.",
      "url": "https://www.anchorterminal.com/tools/unsloth",
      "markdownUrl": "https://www.anchorterminal.com/tools/unsloth.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/unsloth.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/unsloth.json",
      "repo": "https://github.com/unslothai/unsloth",
      "license": "Apache-2.0 (core), AGPL-3.0 (Studio UI)",
      "transports": [],
      "packages": [
        {
          "registry": "pypi",
          "name": "unsloth"
        }
      ],
      "auth": "none",
      "authNotes": "No account. Studio asks for an admin password when exposed beyond loopback (`--secure`, `--cloudflare` or a non-loopback host), and hands out API keys for its OpenAI-compatible server under Settings.",
      "pricing": "free",
      "pricingNotes": "Free and open source. You pay for the GPU it runs on, whether a free Colab or Kaggle notebook, your own card or a rented one. Docker images `unsloth/unsloth` and `unsloth/unsloth-rocm` on Docker Hub. No hosted plan or price list appears on the site or in the docs index (https://unsloth.ai/docs).",
      "priceSummary": "Free · OSS",
      "where": "library",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 76900,
        "npmWeekly": null,
        "pypiWeekly": 230075,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://unsloth.ai/docs",
      "llmsTxt": "https://unsloth.ai/docs/llms.txt",
      "capabilities": [
        "finetune.sft",
        "finetune.preference",
        "finetune.rl",
        "finetune.lora",
        "finetune.export"
      ],
      "tags": [
        "open-source",
        "framework",
        "self-hosted",
        "local",
        "free",
        "python",
        "llms-txt",
        "open-weights"
      ],
      "lastRelease": "2026-09-28",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 51.7,
        "grade": "D",
        "agentReady": false,
        "rank": 347,
        "ranked": true,
        "rankOf": 452,
        "categoryRank": 5,
        "methodology": "0.3",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 53,
          "maintenance": 82,
          "payments": 60,
          "reliability": 43,
          "schema": 66,
          "security": 35,
          "transparency": 34
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.",
        "strengths": [
          "Free and open source, Apache-2.0 core, with the weights staying on your hardware",
          "LoRA, QLoRA, full fine-tuning, GRPO, DPO and ORPO from one package",
          "Exports adapters, merged 16-bit weights and GGUF for vLLM, Ollama or llama.cpp",
          "Fifteen PyPI releases between 25 August and 28 September 2026",
          "llms.txt and over 100 model-specific notebooks"
        ],
        "weaknesses": [
          "Not a hosted service; you bring and pay for the GPU",
          "Studio is AGPL-3.0, and its server-side tools are on by default when exposed",
          "792 open issues and 472 open pull requests",
          "No legal entity in the terms, no privacy page and no security.txt",
          "Calendar versions with no breaking-change notes and no deprecation policy"
        ],
        "agentNotes": [
          "Install with `uv pip install unsloth --torch-backend=auto` on a CUDA machine; the desktop app is for people",
          "Start from the notebook for the model family in unslothai/notebooks; it sets LoRA targets and the chat template",
          "Save the LoRA adapter while iterating and merge to 16-bit or GGUF only when you ship",
          "If Studio must be reachable by other agents, pass --disable-tools and keep it on 127.0.0.1 behind a tunnel",
          "Pin the exact unsloth version; releases land several times a week and don't flag breaking changes"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "D",
            "methodology": "0.3",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 51.7
          }
        ],
        "editorialScores": {
          "ergonomics": 53,
          "maintenance": 82,
          "payments": 60,
          "reliability": 43,
          "schema": 66,
          "security": 35,
          "transparency": 40
        },
        "provenanceScore": 27
      },
      "connect": {
        "install": "curl -fsSL https://unsloth.ai/install.sh | sh   # or: uv pip install unsloth --torch-backend=auto"
      },
      "letme": {
        "capability": "https://letme.dev/finetune.sft",
        "tool": "https://letme.dev/unsloth"
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "unsloth.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://unsloth.ai/terms",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/unslothai/unsloth/releases",
        "securityTxt": "none",
        "checked": "2026-09-30",
        "notes": [
          "The terms page names no company, address or date, and unsloth.ai/privacy returns 404. Copyright notices in the source credit Daniel Han-Chen and the Unsloth team.",
          "A local library has no endpoint to check against the domain.",
          "unsloth.ai/.well-known/security.txt returns 404.",
          "The .ai registry's RDAP server refused our requests, so the registration date is blank.",
          "lastRelease is blank because releases are versioned by date (2026.9.12) and we didn't confirm the tag date; the last commit was 2026-09-30."
        ],
        "score": 27
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/unsloth.json",
      "live": {
        "slug": "unsloth",
        "versions": [
          {
            "registry": "github",
            "name": "unslothai/unsloth",
            "version": "v0.1.902-beta",
            "released": "2026-10-01",
            "seenAt": "2026-10-04T16:42:47.22948531Z"
          },
          {
            "registry": "pypi",
            "name": "unsloth",
            "version": "2026.9.14",
            "released": "2026-10-01",
            "seenAt": "2026-10-04T16:42:47.034258052Z"
          }
        ],
        "githubStars": 77198,
        "pypiWeekly": 198310,
        "securityTxt": {
          "url": "https://unsloth.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-04T15:15:40.917718435Z"
        },
        "llmsTxt": {
          "url": "https://unsloth.ai/docs/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-04T15:18:19.343297803Z"
        },
        "domain": {
          "domain": "unsloth.ai",
          "registered": "2023-11-27",
          "source": "https://rdap.identitydigital.services/rdap/domain/unsloth.ai",
          "checkedAt": "2026-10-04T13:08:02.898044488Z"
        },
        "pages": [
          {
            "url": "https://unsloth.ai/terms",
            "kind": "terms",
            "status": 404,
            "checkedAt": "2026-10-04T15:48:37.812526194Z",
            "changedAt": "0001-01-01T00:00:00Z"
          }
        ],
        "updatedAt": "2026-10-04T16:42:47.22948531Z"
      }
    },
    "summary": "Together AI Fine-tuning has a score of 54.9 (C) against Unsloth's 51.7 (D). Both do finetune sft. The largest gap is payments \u0026 pricing, 40 points."
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth",
    "json": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.md",
    "slim": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.min.md"
  },
  "markdown": "Together AI Fine-tuning has a score of 54.9 (C) against Unsloth's 51.7 (D). Both do finetune sft. The largest gap is payments \u0026 pricing, 40 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- Unsloth: grade D, 51.7/100, rank #347 of 452. Markdown https://www.anchorterminal.com/tools/unsloth.md · JSON https://www.anchorterminal.com/api/v1/tools/unsloth.json\n\n## Which one, for what\n\nPick Together AI Fine-tuning for reliability (+12), schema \u0026 documentation (+12), security \u0026 auth (+15), transparency \u0026 trust (+36).\n\nPick Unsloth for agent ergonomics (+11), payments \u0026 pricing (+40).\n\n## Score by category\n\n| Category | Weight | Together AI Fine-tuning | Unsloth | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 55 | 43 | Together AI Fine-tuning +12 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 78 | 66 | Together AI Fine-tuning +12 |\n| Agent ergonomics | 13% (16.2 this run) | 42 | 53 | Unsloth +11 |\n| Security \u0026 auth | 14% (17.5 this run) | 50 | 35 | Together AI Fine-tuning +15 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 60 | Unsloth +40 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 80 | 82 | Unsloth +2 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 70 | 34 | Together AI Fine-tuning +36 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.9 · C** | **51.7 · D** | |\n\n## Facts side by side\n\n| Fact | Together AI Fine-tuning | Unsloth |\n| --- | --- | --- |\n| Kind | HTTP API | Agent framework |\n| Vendor | Together AI | Unsloth |\n| Hosted endpoint | `https://api.together.ai/v1` | no (local only) |\n| Transports | HTTP |  |\n| Auth | API key | None |\n| Pricing | Pay per use | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDKs) | Apache-2.0 (core), AGPL-3.0 (Studio UI) |\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 | yes |\n| MCP registry | not listed | not listed |\n| Last release | 2026-09-30 | 2026-09-28 |\n| Popularity | 10 stars, 118k npm/wk, 369k PyPI/wk | 77k stars, 230k PyPI/wk |\n| Agent reviews | 3/5 (2) | 3.5/5 (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**Unsloth.** The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.\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### Unsloth\n\n1. Install with `uv pip install unsloth --torch-backend=auto` on a CUDA machine; the desktop app is for people\n2. Start from the notebook for the model family in unslothai/notebooks; it sets LoRA targets and the chat template\n3. Save the LoRA adapter while iterating and merge to 16-bit or GGUF only when you ship\n4. If Studio must be reachable by other agents, pass --disable-tools and keep it on 127.0.0.1 behind a tunnel\n5. Pin the exact unsloth version; releases land several times a week and don't flag breaking changes\n\n## Other comparisons with Together AI Fine-tuning or Unsloth\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 Unsloth](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-unsloth.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 Unsloth](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.md)\n- [Tinker vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.md)\n- [Tinker vs Unsloth](https://www.anchorterminal.com/compare/tinker-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- [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 Unsloth",
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    "description": "Together AI Fine-tuning has a score of 54.9 (C) against Unsloth's 51.7 (D). Both do finetune sft. The largest gap is payments \u0026 pricing, 40 points. Category scores, facts, verdicts and agent notes side by side.",
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    "title": "Together AI Fine-tuning vs Unsloth for AI agents, C 54.9 vs D 51.7",
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