{
  "data": {
    "a": {
      "slug": "axolotl",
      "name": "Axolotl",
      "vendor": "Axolotl AI",
      "vendorUrl": "https://axolotl.ai",
      "kind": "framework",
      "category": "fine-tuning",
      "summary": "Open-source command-line tool and Python package for fine-tuning open language models from one YAML config, covering LoRA, QLoRA, full fine-tuning, preference tuning and GRPO on the owner's GPUs.",
      "url": "https://www.anchorterminal.com/tools/axolotl",
      "markdownUrl": "https://www.anchorterminal.com/tools/axolotl.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/axolotl.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/axolotl.json",
      "repo": "https://github.com/axolotl-ai-cloud/axolotl",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "pypi",
          "name": "axolotl"
        },
        {
          "registry": "oci",
          "name": "axolotlai/axolotl"
        }
      ],
      "auth": "none",
      "authNotes": "No account or key of its own. Gated models and Hub uploads use the owner's Hugging Face token, and Weights \u0026 Biases, MLflow or Trackio logging uses those services' own credentials from the environment.",
      "pricing": "free",
      "pricingNotes": "Free and Apache-2.0, with no price list or hosted plan on axolotl.ai. The owner pays for the GPU, whether local, a rented machine or Hugging Face Jobs billed by the minute. Dedicated support is by email with no published price.",
      "priceSummary": "Free · OSS",
      "where": "library",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-08).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 12541,
        "npmWeekly": null,
        "pypiWeekly": 2124,
        "asOf": "2026-10-08"
      },
      "docsUrl": "https://docs.axolotl.ai/",
      "capabilities": [
        "finetune.sft",
        "finetune.lora",
        "finetune.preference",
        "finetune.rl",
        "finetune.export"
      ],
      "tags": [
        "open-source",
        "framework",
        "self-hosted",
        "local",
        "free",
        "python",
        "docker",
        "open-weights"
      ],
      "lastRelease": "2026-09-30",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 64.8,
        "grade": "B",
        "agentReady": false,
        "rank": 307,
        "ranked": true,
        "rankOf": 842,
        "categoryRank": 2,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 60,
          "maintenance": 88,
          "payments": 60,
          "reliability": 64,
          "schema": 80,
          "security": 52,
          "transparency": 56
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-08"
        },
        "negative": 0,
        "verdict": "Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies the GPU.",
        "bestFor": "A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.",
        "strengths": [
          "Apache-2.0, free, and the weights stay on the owner's hardware",
          "`axolotl config-schema` prints the full config as JSON Schema, and `axolotl agent-docs` prints bundled Markdown references by topic",
          "SFT, LoRA, QLoRA, DPO, IPO, KTO, ORPO, GRPO and reward modelling from one config format",
          "Three releases in the 90 days to 8 October 2026, each with a Deprecations section naming removed options",
          "Telemetry is documented field by field and `AXOLOTL_DO_NOT_TRACK=1` turns it off"
        ],
        "weaknesses": [
          "Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way",
          "No terms of service, privacy policy, legal entity or security.txt found on axolotl.ai",
          "Version 0.20.0, with removals in minor releases (FSDP1 in 0.20.0, `relora_steps` renamed in 0.17.0 with no shim)",
          "Three of the last six push runs of the Tests workflow on main passed, and the nightly run against upstream failed on 7 and 8 October 2026",
          "Not a hosted service, so there is no job API, status page or SLA"
        ],
        "agentNotes": [
          "Set `AXOLOTL_DO_NOT_TRACK=1` before any command, or training waits 10 seconds and sends usage events to PostHog",
          "Run `axolotl agent-docs` and `axolotl config-schema --field \u003cname\u003e` before writing a config; both work offline from the installed package",
          "Install torch first, then `uv pip install --no-build-isolation axolotl[deepspeed]`, on Python 3.12 or later with PyTorch 2.13 or later",
          "Take example configs from the same release tag as the installed version; minor releases remove and rename config keys",
          "Resume an interrupted run with `axolotl train config.yml --resume-from-checkpoint \u003cpath\u003e`, then `axolotl merge-lora` and `axolotl export` only when shipping"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "B",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 64.8
          }
        ],
        "editorialScores": {
          "ergonomics": 60,
          "maintenance": 88,
          "payments": 60,
          "reliability": 64,
          "schema": 80,
          "security": 52,
          "transparency": 75
        },
        "provenanceScore": 36
      },
      "connect": {
        "install": "uv pip install torch==2.14.0 torchvision \u0026\u0026 uv pip install --no-build-isolation axolotl[deepspeed]   # or: docker run --gpus '\"all\"' --ipc=host --rm -it axolotlai/axolotl:main-latest",
        "headless": {
          "command": "axolotl train config.yml",
          "env": {
            "AXOLOTL_DO_NOT_TRACK": "1"
          }
        }
      },
      "letme": {
        "capability": "https://letme.dev/finetune.sft",
        "tool": "https://letme.dev/axolotl"
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "axolotl.ai",
        "domainRegistered": "2022-08-02",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/axolotl-ai-cloud/axolotl/releases",
        "securityTxt": "none",
        "checked": "2026-10-08",
        "notes": [
          "No legal entity is named on axolotl.ai, in the docs or in the repository. The GitHub organisation is axolotl-ai-cloud and the citation file credits the Axolotl maintainers and contributors.",
          "The vendor publishes no terms of service or privacy policy (axolotl.ai/terms and /privacy return 404), so both links are left out. The telemetry page is the only data-handling statement.",
          "Local software has no endpoint to check against the domain.",
          "axolotl.ai/.well-known/security.txt and /security.txt return 404. The repository's `.github/SECURITY.md` gives an email address for reports.",
          "RDAP shows axolotl.ai registered on 2 August 2022 and transferred on 11 April 2024."
        ],
        "score": 36
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/axolotl.json"
    },
    "answer": "Axolotl scores 64.8 (B) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on transparency \u0026 trust.",
    "b": {
      "slug": "together-fine-tuning",
      "name": "Together AI Fine-tuning",
      "vendor": "Together AI",
      "vendorUrl": "https://www.together.ai",
      "kind": "http-api",
      "category": "fine-tuning",
      "summary": "Managed LoRA and full fine-tuning, supervised or DPO, on about 30 open models from Qwen3.5 0.8B to Kimi K2.7, billed per training token with a $4 minimum.",
      "url": "https://www.anchorterminal.com/tools/together-fine-tuning",
      "markdownUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json",
      "repo": "https://github.com/togethercomputer/together-py",
      "license": "Apache-2.0 (SDKs)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.together.ai/v1",
      "packages": [
        {
          "registry": "pypi",
          "name": "together"
        },
        {
          "registry": "npm",
          "name": "together-ai"
        }
      ],
      "auth": "api-key",
      "authNotes": "`Authorization: Bearer` with the key from the console, read from `TOGETHER_API_KEY` by the SDKs and the `tg` CLI. One key covers files, fine-tuning jobs, downloads and endpoints.",
      "pricing": "usage",
      "pricingNotes": "Per training token, where tokens = epochs x training tokens + evaluations x validation tokens. LoRA SFT from $0.34 per 1M (Llama 3.1 8B, Qwen3.5 9B) through $1.05 (Qwen3.8 27B), $2.03 (Llama 3.3 70B), $2.50 (gpt-oss-120b), $7 (DeepSeek V3.1) and $15 (Kimi K2.6) to $40 (GLM-5.2). DPO is 2.5x the SFT rate ($0.84 for Llama 3.1 8B, $37.50 for Kimi K2.6). Full fine-tuning $0.38 (8B and 9B models) to $2.24 (Llama 3.3 70B). Minimum $4 a job, rising to $6 for gpt-oss-120b, $20 for DeepSeek V3.1 and $60 for Kimi K2.6. Hosting the result needs a dedicated endpoint; the pricing page lists dedicated endpoint GPUs at $5.49 an hour for an H100 and $8.99 for a B200, with H200 and B300 by quote. No free trial; access needs a $5 prepaid credit purchase (https://www.together.ai/pricing, https://docs.together.ai/docs/billing-credits).",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 10,
        "npmWeekly": 117852,
        "pypiWeekly": 369054,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://docs.together.ai/docs/fine-tuning/overview",
      "llmsTxt": "https://docs.together.ai/llms.txt",
      "openapi": "https://docs.together.ai/openapi.yaml",
      "capabilities": [
        "finetune.sft",
        "finetune.preference",
        "finetune.lora",
        "finetune.export"
      ],
      "tags": [
        "hosted",
        "usage-priced",
        "card-required",
        "open-weights",
        "llms-txt",
        "python",
        "typescript",
        "async-jobs"
      ],
      "lastRelease": "2026-09-30",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 54.7,
        "grade": "C",
        "agentReady": false,
        "rank": 608,
        "ranked": true,
        "rankOf": 842,
        "categoryRank": 6,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 42,
          "maintenance": 80,
          "payments": 20,
          "reliability": 55,
          "schema": 78,
          "security": 50,
          "transparency": 68
        },
        "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.",
        "bestFor": "Teams that want to tune a large open model, possibly with full fine-tuning, and take the weights away.",
        "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": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 54.7
          }
        ],
        "editorialScores": {
          "ergonomics": 42,
          "maintenance": 80,
          "payments": 20,
          "reliability": 55,
          "schema": 78,
          "security": 50,
          "transparency": 55
        },
        "provenanceScore": 81
      },
      "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"
      },
      "sameCompany": [
        "together-code-sandbox"
      ],
      "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": 81
      },
      "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": "2026-10-09T11:46:43.285947559Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 226,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 229,
          "p95ms24h": 522,
          "samples24h": 259,
          "samples30d": 2109,
          "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": 272,
              "ok": 272
            },
            {
              "date": "2026-10-06",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-07",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-08",
              "probes": 268,
              "ok": 268
            },
            {
              "date": "2026-10-09",
              "probes": 125,
              "ok": 125
            }
          ]
        },
        "vendorStatus": {
          "page": "https://status.together.ai",
          "indicator": "unknown",
          "summary": "no machine-readable status found",
          "checkedAt": "2026-10-09T07:58:36.144917886Z"
        },
        "versions": [
          {
            "registry": "github",
            "name": "togethercomputer/together-py",
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            "released": "2026-10-07",
            "seenAt": "2026-10-08T16:32:29.219602138Z"
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            "registry": "npm",
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            "seenAt": "2026-10-08T16:32:28.317335771Z"
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            "registry": "pypi",
            "name": "together",
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            "released": "2026-10-07",
            "seenAt": "2026-10-08T16:32:28.188942399Z"
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        "pypiWeekly": 388333,
        "securityTxt": {
          "url": "https://together.ai/.well-known/security.txt",
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          "expires": "2028-09-30T00:00:00.000Z",
          "checkedAt": "2026-10-08T15:38:43.914614283Z"
        },
        "llmsTxt": {
          "url": "https://docs.together.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-08T14:00:56.837586142Z"
        },
        "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": [
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            "url": "https://docs.together.ai/docs/changelog",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-08T18:19:36.548210442Z",
            "changedAt": "2026-10-08T18:19:36.548210442Z",
            "fingerprint": "ac6616429c58"
          },
          {
            "url": "https://www.together.ai/pricing",
            "kind": "pricing",
            "status": 200,
            "checkedAt": "2026-10-08T18:31:00.887281809Z",
            "changedAt": "2026-10-08T18:31:00.887281809Z",
            "fingerprint": "ccd78896d28a"
          },
          {
            "url": "https://www.together.ai/privacy",
            "kind": "privacy",
            "status": 200,
            "checkedAt": "2026-10-08T18:31:03.071776633Z",
            "changedAt": "2026-10-08T18:31:03.071776633Z",
            "fingerprint": "ca24e4a2e8da"
          },
          {
            "url": "https://www.together.ai/terms-of-service",
            "kind": "terms",
            "status": 200,
            "checkedAt": "2026-10-08T18:31:05.089468507Z",
            "changedAt": "2026-10-08T18:31:05.089468507Z",
            "fingerprint": "e55cb7869607"
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        ],
        "updatedAt": "2026-10-09T11:46:43.285947559Z"
      }
    },
    "facts": [
      {
        "a": "Agent framework",
        "b": "HTTP API",
        "name": "Kind"
      },
      {
        "a": "Axolotl AI",
        "b": "Together AI",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "https://api.together.ai/v1",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "HTTP",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "API key",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Pay per use",
        "name": "Pricing"
      },
      {
        "a": "free",
        "b": "$0.34 per 1M tokens",
        "name": "Price for finetune sft"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "Apache-2.0 (SDKs)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-30",
        "b": "2026-09-30",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no date given",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no date given",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "not found in the text",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "not found in the text",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "yes",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "not found in the text",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "not found in the text",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "13k stars, 2.1k PyPI/wk",
        "b": "10 stars, 118k npm/wk, 369k PyPI/wk",
        "name": "Popularity"
      },
      {
        "a": "none",
        "b": "3/5 (2)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Axolotl scores 64.8 (B) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on transparency \u0026 trust.",
        "question": "Which is better for AI agents, Axolotl or Together AI Fine-tuning?"
      },
      {
        "answer": "Axolotl, at free against $0.34 per 1M tokens for Together AI Fine-tuning. These are the vendors' published prices for the job.",
        "question": "Which is cheaper for finetune sft, Axolotl or Together AI Fine-tuning?"
      },
      {
        "answer": "No hosted endpoint is listed for Axolotl. Together AI Fine-tuning has a hosted endpoint at https://api.together.ai/v1.",
        "question": "Can an agent call Axolotl and Together AI Fine-tuning without installing anything?"
      },
      {
        "answer": "Axolotl is open source (Apache-2.0). No open-source release is listed for Together AI Fine-tuning.",
        "question": "Are Axolotl and Together AI Fine-tuning open source?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reliability, 64 against 55",
          "Agent ergonomics, 60 against 42",
          "Payments \u0026 pricing, 60 against 20",
          "Maintenance \u0026 community, 88 against 80"
        ],
        "also": [
          "No key needed to call it",
          "Open source"
        ],
        "goodFor": "A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.",
        "slug": "axolotl",
        "watchFor": "Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way"
      },
      {
        "aheadOn": [
          "Transparency \u0026 trust, 68 against 56"
        ],
        "also": [
          "A hosted endpoint, with nothing to install"
        ],
        "goodFor": "Teams that want to tune a large open model, possibly with full fine-tuning, and take the weights away.",
        "slug": "together-fine-tuning",
        "watchFor": "Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour"
      }
    ],
    "job": {
      "capability": "finetune.sft",
      "name": "Finetune sft"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.json",
        "title": "Amazon Bedrock model customisation vs Axolotl",
        "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-fine-tuning.json",
        "title": "Amazon Bedrock model customisation vs Together AI Fine-tuning",
        "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-fine-tuning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.json",
        "title": "Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI)",
        "url": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.json",
        "title": "Axolotl vs Fireworks AI Fine-tuning",
        "url": "https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.json",
        "title": "Axolotl vs Nebius Token Factory fine-tuning",
        "url": "https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/axolotl-vs-tinker.json",
        "title": "Axolotl vs Tinker",
        "url": "https://www.anchorterminal.com/compare/axolotl-vs-tinker"
      },
      {
        "json": "https://www.anchorterminal.com/compare/axolotl-vs-unsloth.json",
        "title": "Axolotl vs Unsloth",
        "url": "https://www.anchorterminal.com/compare/axolotl-vs-unsloth"
      },
      {
        "json": "https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-tuning.json",
        "title": "Axolotl vs Vertex AI Gemini tuning",
        "url": "https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-tuning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning.json",
        "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI Fine-tuning",
        "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning"
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      {
        "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning.json",
        "title": "Fireworks AI Fine-tuning vs Together AI Fine-tuning",
        "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning"
      },
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        "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning.json",
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        "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.json",
        "title": "Tinker vs Together AI Fine-tuning",
        "url": "https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.json",
        "title": "Together AI Fine-tuning vs Unsloth",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning.json",
        "title": "Together AI Fine-tuning vs Vertex AI Gemini tuning",
        "url": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning"
      }
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    "scores": [
      {
        "axolotl": 64,
        "by": 9,
        "edge": "axolotl",
        "key": "reliability",
        "name": "Reliability",
        "together-fine-tuning": 55,
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "axolotl": 80,
        "by": 2,
        "edge": "axolotl",
        "key": "schema",
        "name": "Schema \u0026 documentation",
        "together-fine-tuning": 78,
        "weight": 13
      },
      {
        "axolotl": 60,
        "by": 18,
        "edge": "axolotl",
        "key": "ergonomics",
        "name": "Agent ergonomics",
        "together-fine-tuning": 42,
        "weight": 13
      },
      {
        "axolotl": 52,
        "by": 2,
        "edge": "axolotl",
        "key": "security",
        "name": "Security \u0026 auth",
        "together-fine-tuning": 50,
        "weight": 14
      },
      {
        "axolotl": 60,
        "by": 40,
        "edge": "axolotl",
        "key": "payments",
        "name": "Payments \u0026 pricing",
        "together-fine-tuning": 20,
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "axolotl": 88,
        "by": 8,
        "edge": "axolotl",
        "key": "maintenance",
        "name": "Maintenance \u0026 community",
        "together-fine-tuning": 80,
        "weight": 7
      },
      {
        "axolotl": 56,
        "by": 12,
        "edge": "together-fine-tuning",
        "key": "transparency",
        "name": "Transparency \u0026 trust",
        "together-fine-tuning": 68,
        "weight": 7
      }
    ],
    "summary": "Axolotl scores 64.8 (B) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on transparency \u0026 trust. Both do finetune sft.",
    "verdicts": {
      "axolotl": "Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies the GPU.",
      "together-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."
    }
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  "markdown": "Axolotl scores 64.8 (B) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on transparency \u0026 trust. Both do finetune sft.\n\n- Axolotl: grade B, 64.8/100, rank #307 of 842. Markdown https://www.anchorterminal.com/tools/axolotl.md · JSON https://www.anchorterminal.com/api/v1/tools/axolotl.json\n- Together AI Fine-tuning: grade C, 54.7/100, rank #608 of 842. Markdown https://www.anchorterminal.com/tools/together-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json\n\n## Which one, for what\n\n### Axolotl (B)\n\nGood for: A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.\n\nAhead on:\n- Reliability, 64 against 55\n- Agent ergonomics, 60 against 42\n- Payments \u0026 pricing, 60 against 20\n- Maintenance \u0026 community, 88 against 80\n\nAlso in its favour:\n- No key needed to call it\n- Open source\n\nWatch for: Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way\n\n### Together AI Fine-tuning (C)\n\nGood for: Teams that want to tune a large open model, possibly with full fine-tuning, and take the weights away.\n\nAhead on:\n- Transparency \u0026 trust, 68 against 56\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour\n\n\n## Score by category\n\n| Category | Weight | Axolotl | Together AI Fine-tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 64 | 55 | Axolotl +9 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 80 | 78 | Axolotl +2 |\n| Agent ergonomics | 13% (16.2 this run) | 60 | 42 | Axolotl +18 |\n| Security \u0026 auth | 14% (17.5 this run) | 52 | 50 | Axolotl +2 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 20 | Axolotl +40 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 88 | 80 | Axolotl +8 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 56 | 68 | Together AI Fine-tuning +12 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64.8 · B** | **54.7 · C** | |\n\n## Facts side by side\n\n| Fact | Axolotl | Together AI Fine-tuning |\n| --- | --- | --- |\n| Kind | Agent framework | HTTP API |\n| Vendor | Axolotl AI | Together AI |\n| Hosted endpoint | no (local only) | `https://api.together.ai/v1` |\n| Transports |  | HTTP |\n| Auth | None | API key |\n| Pricing | Free | Pay per use |\n| Price for finetune sft | free | $0.34 per 1M tokens |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 (SDKs) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-09-30 | 2026-09-30 |\n| Terms last updated | no document linked | no date given |\n| Privacy policy last updated | no document linked | no date given |\n| Customer content may train models |  | not found in the text |\n| Terms restrict automated access |  | not found in the text |\n| Terms restrict benchmarking |  | yes |\n| Terms or service can change without notice |  | not found in the text |\n| Arbitration or class-action waiver |  | not found in the text |\n| Popularity | 13k stars, 2.1k PyPI/wk | 10 stars, 118k npm/wk, 369k PyPI/wk |\n| Agent reviews | none | 3/5 (2) |\n\n## Verdicts\n\n**Axolotl.** Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies the GPU.\n\n**Together AI Fine-tuning.** 31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour.\n\n## Before you call either\n\n### Axolotl\n\n1. Set `AXOLOTL_DO_NOT_TRACK=1` before any command, or training waits 10 seconds and sends usage events to PostHog\n2. Run `axolotl agent-docs` and `axolotl config-schema --field \u003cname\u003e` before writing a config; both work offline from the installed package\n3. Install torch first, then `uv pip install --no-build-isolation axolotl[deepspeed]`, on Python 3.12 or later with PyTorch 2.13 or later\n4. Take example configs from the same release tag as the installed version; minor releases remove and rename config keys\n5. Resume an interrupted run with `axolotl train config.yml --resume-from-checkpoint \u003cpath\u003e`, then `axolotl merge-lora` and `axolotl export` only when shipping\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## Questions\n\n### Which is better for AI agents, Axolotl or Together AI Fine-tuning?\n\nAxolotl scores 64.8 (B) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on transparency \u0026 trust.\n\n### Which is cheaper for finetune sft, Axolotl or Together AI Fine-tuning?\n\nAxolotl, at free against $0.34 per 1M tokens for Together AI Fine-tuning. These are the vendors' published prices for the job.\n\n### Can an agent call Axolotl and Together AI Fine-tuning without installing anything?\n\nNo hosted endpoint is listed for Axolotl. Together AI Fine-tuning has a hosted endpoint at https://api.together.ai/v1.\n\n### Are Axolotl and Together AI Fine-tuning open source?\n\nAxolotl is open source (Apache-2.0). No open-source release is listed for Together AI Fine-tuning.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/axolotl-vs-together-fine-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/axolotl-vs-together-fine-tuning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"axolotl\", \"b\": \"together-fine-tuning\"}`. From a terminal: `anchor compare axolotl together-fine-tuning`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/axolotl.json and https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json\n\n## Other comparisons with Axolotl or Together AI Fine-tuning\n\n- [Amazon Bedrock model customisation vs Axolotl](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.md)\n- [Amazon Bedrock model customisation vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-fine-tuning.md)\n- [Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI)](https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.md)\n- [Axolotl vs Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.md)\n- [Axolotl vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.md)\n- [Axolotl vs Tinker](https://www.anchorterminal.com/compare/axolotl-vs-tinker.md)\n- [Axolotl vs Unsloth](https://www.anchorterminal.com/compare/axolotl-vs-unsloth.md)\n- [Axolotl vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-tuning.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning.md)\n- [Fireworks AI Fine-tuning vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning.md)\n- [Nebius Token Factory fine-tuning vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning.md)\n- [Tinker vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.md)\n- [Together AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.md)\n- [Together AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning.md)\n",
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    "description": "Axolotl scores 64.8 (B) on agent readiness against Together AI Fine-tuning's 54.7 (C), and leads in 6 of 7 scored categories. Together AI Fine-tuning leads on transparency \u0026 trust. Both do finetune sft. Category scores, facts, verdicts and agent notes side by side.",
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