{
  "data": {
    "a": {
      "slug": "fireworks-fine-tuning",
      "name": "Fireworks AI Fine-tuning",
      "vendor": "Fireworks AI",
      "vendorUrl": "https://fireworks.ai",
      "kind": "http-api",
      "category": "fine-tuning",
      "summary": "Managed supervised, preference and reinforcement fine-tuning for open models, with a training API for custom workflows.",
      "url": "https://www.anchorterminal.com/tools/fireworks-fine-tuning",
      "markdownUrl": "https://www.anchorterminal.com/tools/fireworks-fine-tuning.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/fireworks-fine-tuning.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json",
      "repo": "https://github.com/fw-ai-external/python-sdk",
      "license": "Apache-2.0 (SDK)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.fireworks.ai",
      "packages": [
        {
          "registry": "pypi",
          "name": "fireworks-ai"
        }
      ],
      "auth": "api-key",
      "authNotes": "`Authorization: Bearer` with an account key, read from `FIREWORKS_API_KEY` by the SDK and `firectl`. The Training API wants a training-scoped key. Resources are addressed as accounts/\u003caccount\u003e/..., and the SDK resolves the account from the key.",
      "pricing": "usage",
      "pricingNotes": "Managed training per 1M training tokens by model size. LoRA SFT $0.50 up to 16B parameters, $3 from 16.1B to 80B, $6 from 80B to 300B, $10 above; DPO is double, and full-parameter training is double LoRA. Serving a fine-tuned model costs the same as the base model. The serverless Training API is priced per model (Qwen 3.8 27B at $4.103 per 1M training tokens); dedicated training is $8 a GPU-hour for an H100 or H200, $13 for a B200, $15 for a B300 and $20 for a GB300, effective 2026-09-01. On-demand inference deployments cost $8 an hour for an H100 or H200 and $13 for a B200. New accounts get $1 of credit (https://fireworks.ai/pricing).",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 7,
        "npmWeekly": null,
        "pypiWeekly": 290162,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://docs.fireworks.ai/fine-tuning/fine-tuning-models",
      "llmsTxt": "https://docs.fireworks.ai/llms.txt",
      "openapi": "https://docs.fireworks.ai/merged.openapi.yaml",
      "capabilities": [
        "finetune.sft",
        "finetune.preference",
        "finetune.rl",
        "finetune.lora",
        "finetune.export"
      ],
      "tags": [
        "hosted",
        "usage-priced",
        "card-required",
        "open-weights",
        "llms-txt",
        "python",
        "async-jobs"
      ],
      "lastRelease": "2026-10-01",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 59.2,
        "grade": "C",
        "agentReady": false,
        "rank": 269,
        "ranked": true,
        "rankOf": 452,
        "categoryRank": 3,
        "methodology": "0.3",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 75,
          "maintenance": 82,
          "payments": 25,
          "reliability": 55,
          "schema": 77,
          "security": 65,
          "transparency": 66
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": -4,
        "negativeNotes": [
          "-4: on 2026-08-26 the changelog deprecated Qwen 3.5 9B and Qwen 3.6 27B from Serverless Training 'effective August 26, 2026', with no earlier entry announcing it, and told users to move existing workloads to Qwen 3.8 27B (https://docs.fireworks.ai/updates/changelog)"
        ],
        "verdict": "SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless.",
        "strengths": [
          "SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available",
          "LoRA SFT from $0.50 per 1M training tokens up to 16B parameters, with serving at base-model prices",
          "List endpoints take readMask, pageSize up to 200, AIP-160 filters and orderBy",
          "An Inference User role and revocable keys with an expiry date",
          "A public control-plane OpenAPI file, llms.txt and Markdown twins of every docs page"
        ],
        "weaknesses": [
          "Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless",
          "No training without a payment method; the $1 sign-up credit buys inference only",
          "The status page has no training component; a 46-hour cloud provider incident in August hit dedicated deployments",
          "Two Serverless Training base models were deprecated with same-day effect on 2026-08-26",
          "Audit logs are Enterprise only and security.txt returns 404"
        ],
        "agentNotes": [
          "Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute",
          "Check `firectl model get -a fireworks \u003cMODEL-ID\u003e` for Tunable: true before uploading a dataset",
          "Pass your own `supervisedFineTuningJobId` on create, so after a timeout you can GET the job by that name instead of guessing whether it started",
          "Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends",
          "Download with `firectl model download` and keep the exact base model; the adapter alone won't run"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.3",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 59.2
          }
        ],
        "editorialScores": {
          "ergonomics": 75,
          "maintenance": 82,
          "payments": 25,
          "reliability": 55,
          "schema": 77,
          "security": 65,
          "transparency": 57
        },
        "provenanceScore": 75
      },
      "connect": {
        "install": "pip install fireworks-ai   # add [training] for the Training API",
        "http": "curl https://api.fireworks.ai/v1/accounts/$FIREWORKS_ACCOUNT_ID/supervisedFineTuningJobs \\\n  -H \"Authorization: Bearer $FIREWORKS_API_KEY\" -H \"content-type: application/json\" \\\n  -d '{\"baseModel\":\"accounts/fireworks/models/gemma-4-31b-it\",\"dataset\":\"accounts/'$FIREWORKS_ACCOUNT_ID'/datasets/my-dataset\",\"outputModel\":\"accounts/'$FIREWORKS_ACCOUNT_ID'/models/my-tune\",\"loraRank\":16}'"
      },
      "letme": {
        "capability": "https://letme.dev/finetune.sft",
        "tool": "https://letme.dev/fireworks-fine-tuning"
      },
      "area": "models",
      "unitPrices": [
        {
          "item": "LoRA SFT, models up to 16B",
          "unit": "1m-tokens",
          "usd": 0.5
        },
        {
          "item": "LoRA DPO, models up to 16B",
          "unit": "1m-tokens",
          "usd": 1
        },
        {
          "item": "Full-parameter SFT, models up to 16B",
          "unit": "1m-tokens",
          "usd": 1
        },
        {
          "item": "LoRA SFT, 16.1B to 80B",
          "unit": "1m-tokens",
          "usd": 3
        },
        {
          "item": "LoRA SFT, 80B to 300B",
          "unit": "1m-tokens",
          "usd": 6
        },
        {
          "item": "LoRA SFT, over 300B",
          "unit": "1m-tokens",
          "usd": 10
        },
        {
          "item": "Serverless Training API, Qwen 3.8 27B",
          "unit": "1m-tokens",
          "usd": 4.103
        },
        {
          "item": "Dedicated training, H100 or H200",
          "unit": "gpu-hour",
          "usd": 8,
          "note": "Effective 2026-09-01"
        },
        {
          "item": "Dedicated training, B200",
          "unit": "gpu-hour",
          "usd": 13
        },
        {
          "item": "Dedicated training, B300",
          "unit": "gpu-hour",
          "usd": 15
        },
        {
          "item": "Dedicated training, GB300",
          "unit": "gpu-hour",
          "usd": 20
        }
      ],
      "provenance": {
        "legalEntity": "Fireworks.ai, Inc.",
        "domain": "fireworks.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": true,
        "terms": "https://fireworks.ai/terms-of-service",
        "privacy": "https://fireworks.ai/privacy-policy",
        "statusPage": "https://status.fireworks.ai",
        "changelog": "https://docs.fireworks.ai/updates/changelog",
        "securityTxt": "none",
        "checked": "2026-09-30",
        "notes": [
          "The privacy policy, last updated 8/11/2026, names Fireworks.ai, Inc. and gives no address.",
          "fireworks.ai/robots.txt disallows the terms of service page to crawlers, so we read the entity from the privacy policy instead.",
          "fireworks.ai/.well-known/security.txt returns 404.",
          "The status page lists 16 serverless model components and no training component.",
          "The .ai registry's RDAP server refused our requests, so the registration date is blank.",
          "The MCP registry holds a third-party io.usefulapi/fireworks server; Fireworks doesn't publish one."
        ],
        "score": 75
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/fireworks-fine-tuning.json",
      "live": {
        "slug": "fireworks-fine-tuning",
        "probe": {
          "target": "https://api.fireworks.ai",
          "method": "get",
          "lastAt": "2026-10-05T00:25:43.100083799Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 25,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 29,
          "p95ms24h": 62,
          "samples24h": 272,
          "samples30d": 905,
          "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": 5,
              "ok": 5
            }
          ]
        },
        "vendorStatus": {
          "page": "https://status.fireworks.ai",
          "indicator": "none",
          "summary": "All Systems Operational",
          "checkedAt": "2026-10-05T00:22:46.113359797Z"
        },
        "versions": [
          {
            "registry": "github",
            "name": "fw-ai-external/python-sdk",
            "version": "v1.2.19",
            "released": "2026-10-02",
            "seenAt": "2026-10-04T16:27:15.503949556Z"
          },
          {
            "registry": "pypi",
            "name": "fireworks-ai",
            "version": "1.2.19",
            "released": "2026-10-02",
            "seenAt": "2026-10-04T16:27:11.794181655Z"
          }
        ],
        "githubStars": 10,
        "pypiWeekly": 274409,
        "securityTxt": {
          "url": "https://fireworks.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-04T15:16:04.762370371Z"
        },
        "llmsTxt": {
          "url": "https://docs.fireworks.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-04T15:17:46.770278727Z"
        },
        "domain": {
          "domain": "fireworks.ai",
          "registered": "2020-03-11",
          "source": "https://rdap.identitydigital.services/rdap/domain/fireworks.ai",
          "checkedAt": "2026-10-04T13:05:45.131398465Z"
        },
        "pages": [
          {
            "url": "https://docs.fireworks.ai/updates/changelog",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-04T15:43:37.188332196Z",
            "changedAt": "2026-10-03T15:31:45.792871079Z",
            "fingerprint": "5ee4d578feb4"
          },
          {
            "url": "https://fireworks.ai/pricing",
            "kind": "pricing",
            "status": 200,
            "checkedAt": "2026-10-04T15:44:43.473872205Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "9f6b298d1e67"
          },
          {
            "url": "https://fireworks.ai/privacy-policy",
            "kind": "privacy",
            "status": 200,
            "checkedAt": "2026-10-04T15:44:45.619814106Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "7220d287a92b"
          },
          {
            "url": "https://fireworks.ai/terms-of-service",
            "kind": "terms",
            "status": 200,
            "checkedAt": "2026-10-04T15:44:47.623501485Z",
            "changedAt": "0001-01-01T00:00:00Z"
          }
        ],
        "updatedAt": "2026-10-05T00:25:43.100083799Z"
      }
    },
    "b": {
      "slug": "together-fine-tuning",
      "name": "Together AI Fine-tuning",
      "vendor": "Together AI",
      "vendorUrl": "https://www.together.ai",
      "kind": "http-api",
      "category": "fine-tuning",
      "summary": "Managed LoRA and full fine-tuning, supervised or DPO, on about 30 open models from Qwen3.5 0.8B to Kimi K2.7, billed per training token with a $4 minimum.",
      "url": "https://www.anchorterminal.com/tools/together-fine-tuning",
      "markdownUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json",
      "repo": "https://github.com/togethercomputer/together-py",
      "license": "Apache-2.0 (SDKs)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.together.ai/v1",
      "packages": [
        {
          "registry": "pypi",
          "name": "together"
        },
        {
          "registry": "npm",
          "name": "together-ai"
        }
      ],
      "auth": "api-key",
      "authNotes": "`Authorization: Bearer` with the key from the console, read from `TOGETHER_API_KEY` by the SDKs and the `tg` CLI. One key covers files, fine-tuning jobs, downloads and endpoints.",
      "pricing": "usage",
      "pricingNotes": "Per training token, where tokens = epochs x training tokens + evaluations x validation tokens. LoRA SFT from $0.34 per 1M (Llama 3.1 8B, Qwen3.5 9B) through $1.05 (Qwen3.8 27B), $2.03 (Llama 3.3 70B), $2.50 (gpt-oss-120b), $7 (DeepSeek V3.1) and $15 (Kimi K2.6) to $40 (GLM-5.2). DPO is 2.5x the SFT rate ($0.84 for Llama 3.1 8B, $37.50 for Kimi K2.6). Full fine-tuning $0.38 (8B and 9B models) to $2.24 (Llama 3.3 70B). Minimum $4 a job, rising to $6 for gpt-oss-120b, $20 for DeepSeek V3.1 and $60 for Kimi K2.6. Hosting the result needs a dedicated endpoint; the pricing page lists dedicated endpoint GPUs at $5.49 an hour for an H100 and $8.99 for a B200, with H200 and B300 by quote. No free trial; access needs a $5 prepaid credit purchase (https://www.together.ai/pricing, https://docs.together.ai/docs/billing-credits).",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 10,
        "npmWeekly": 117852,
        "pypiWeekly": 369054,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://docs.together.ai/docs/fine-tuning/overview",
      "llmsTxt": "https://docs.together.ai/llms.txt",
      "openapi": "https://docs.together.ai/openapi.yaml",
      "capabilities": [
        "finetune.sft",
        "finetune.preference",
        "finetune.lora",
        "finetune.export"
      ],
      "tags": [
        "hosted",
        "usage-priced",
        "card-required",
        "open-weights",
        "llms-txt",
        "python",
        "typescript",
        "async-jobs"
      ],
      "lastRelease": "2026-09-30",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 54.9,
        "grade": "C",
        "agentReady": false,
        "rank": 319,
        "ranked": true,
        "rankOf": 452,
        "categoryRank": 4,
        "methodology": "0.3",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 42,
          "maintenance": 80,
          "payments": 20,
          "reliability": 55,
          "schema": 78,
          "security": 50,
          "transparency": 70
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour.",
        "strengths": [
          "31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA",
          "GET /v1/finetune/download returns merged weights or the adapter, at any saved checkpoint",
          "POST /v1/fine-tunes/estimate-price quotes a job before it runs",
          "Project-scoped API keys with expiry dates from 1 hour",
          "Python and TypeScript SDKs, an OpenAPI file and llms.txt"
        ],
        "weaknesses": [
          "Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour",
          "No free trial, a $5 prepaid purchase before the first call, and job minimums up to $60",
          "The status page covers serverless models only, and no fine-tuning rate limits are published",
          "No pagination on the job list and no documented error responses for fine-tuning calls",
          "No read-only project role and no audit log found"
        ],
        "agentNotes": [
          "Call POST /v1/fine-tunes/estimate-price with the same body before creating the job, and check the model's minimum charge",
          "Read `lora_training.max_rank` from the model limits response before setting `lora_r`; most models went to 128 on 2026-09-29",
          "Don't retry a create call blindly after a timeout; there's no idempotency key, so list jobs and check first",
          "Download with checkpoint=adapter if you'll merge locally; merged weights for a 70B model are a large stream",
          "Tear down the dedicated endpoint once evaluation ends, since it bills while idle"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.3",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 54.9
          }
        ],
        "editorialScores": {
          "ergonomics": 42,
          "maintenance": 80,
          "payments": 20,
          "reliability": 55,
          "schema": 78,
          "security": 50,
          "transparency": 55
        },
        "provenanceScore": 85
      },
      "connect": {
        "install": "pip install together   # or: npm i together-ai",
        "http": "curl https://api.together.ai/v1/fine-tunes \\\n  -H \"Authorization: Bearer $TOGETHER_API_KEY\" -H \"content-type: application/json\" \\\n  -d '{\"model\":\"Qwen/Qwen3.5-9B\",\"training_file\":\"file-abc123\",\"n_epochs\":3,\"training_type\":{\"type\":\"Lora\",\"lora_r\":16,\"lora_alpha\":32},\"training_method\":{\"method\":\"sft\"},\"suffix\":\"my-run\"}'"
      },
      "letme": {
        "capability": "https://letme.dev/finetune.sft",
        "tool": "https://letme.dev/together-fine-tuning"
      },
      "area": "models",
      "unitPrices": [
        {
          "item": "LoRA SFT, Llama 3.1 8B",
          "unit": "1m-tokens",
          "usd": 0.34,
          "note": "Same rate for Qwen3.5 9B. $4 minimum"
        },
        {
          "item": "LoRA DPO, Llama 3.1 8B",
          "unit": "1m-tokens",
          "usd": 0.84
        },
        {
          "item": "Full SFT, Llama 3.1 8B",
          "unit": "1m-tokens",
          "usd": 0.38
        },
        {
          "item": "LoRA SFT, Qwen3.8 27B",
          "unit": "1m-tokens",
          "usd": 1.05
        },
        {
          "item": "LoRA SFT, Llama 3.3 70B",
          "unit": "1m-tokens",
          "usd": 2.03,
          "note": "Full SFT $2.24"
        },
        {
          "item": "LoRA SFT, gpt-oss-120b",
          "unit": "1m-tokens",
          "usd": 2.5,
          "note": "$6 minimum"
        },
        {
          "item": "LoRA SFT, DeepSeek V3.1",
          "unit": "1m-tokens",
          "usd": 7,
          "note": "$20 minimum"
        },
        {
          "item": "LoRA SFT, Kimi K2.6",
          "unit": "1m-tokens",
          "usd": 15,
          "note": "$60 minimum"
        },
        {
          "item": "H100 on demand",
          "unit": "gpu-hour",
          "usd": 3.99
        },
        {
          "item": "H200 on demand",
          "unit": "gpu-hour",
          "usd": 5.99
        },
        {
          "item": "B200 on demand",
          "unit": "gpu-hour",
          "usd": 8.19
        }
      ],
      "provenance": {
        "legalEntity": "Together Computer, Inc.",
        "domain": "together.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": true,
        "terms": "https://www.together.ai/terms-of-service",
        "privacy": "https://www.together.ai/privacy",
        "statusPage": "https://status.together.ai",
        "changelog": "https://docs.together.ai/docs/changelog",
        "securityTxt": "valid",
        "checked": "2026-09-30",
        "notes": [
          "The terms (2026-05-19) name Together Computer, Inc., a Delaware corporation. The privacy policy (2025-12-17) says data isn't used to train models without opt-in.",
          "security.txt points Contact and Policy at hackerone.com/together_ai and has no Expires field.",
          "The status page monitors serverless inference models only; there's no fine-tuning component.",
          "The .ai registry's RDAP server refused our requests, so the registration date is blank.",
          "The MCP registry has a third-party io.usefulapi/together-ai server that wraps fine-tunes; Together doesn't publish one."
        ],
        "score": 85
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.json",
      "live": {
        "slug": "together-fine-tuning",
        "probe": {
          "target": "https://api.together.ai/v1",
          "method": "get",
          "lastAt": "2026-10-05T00:25:50.980664901Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 522,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 223,
          "p95ms24h": 550,
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    "summary": "Fireworks AI Fine-tuning has a score of 59.2 (C) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is agent ergonomics, 33 points."
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  "markdown": "Fireworks AI Fine-tuning has a score of 59.2 (C) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is agent ergonomics, 33 points.\n\n- Fireworks AI Fine-tuning: grade C, 59.2/100, rank #269 of 452. Markdown https://www.anchorterminal.com/tools/fireworks-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json\n- Together AI Fine-tuning: grade C, 54.9/100, rank #319 of 452. Markdown https://www.anchorterminal.com/tools/together-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json\n\n## Which one, for what\n\nPick Fireworks AI Fine-tuning for agent ergonomics (+33), security \u0026 auth (+15), payments \u0026 pricing (+5).\n\nPick Together AI Fine-tuning for nothing in particular (no category where it leads by five points or more).\n\n## Score by category\n\n| Category | Weight | Fireworks AI Fine-tuning | Together AI Fine-tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 55 | 55 | even |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 77 | 78 | Together AI Fine-tuning +1 |\n| Agent ergonomics | 13% (16.2 this run) | 75 | 42 | Fireworks AI Fine-tuning +33 |\n| Security \u0026 auth | 14% (17.5 this run) | 65 | 50 | Fireworks AI Fine-tuning +15 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 25 | 20 | Fireworks AI Fine-tuning +5 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 82 | 80 | Fireworks AI Fine-tuning +2 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 66 | 70 | Together AI Fine-tuning +4 |\n| Negative events | ≤15 | -4 | 0 | |\n| **Total** | | **59.2 · C** | **54.9 · C** | |\n\n## Facts side by side\n\n| Fact | Fireworks AI Fine-tuning | Together AI Fine-tuning |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Fireworks AI | Together AI |\n| Hosted endpoint | `https://api.fireworks.ai` | `https://api.together.ai/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDK) | Apache-2.0 (SDKs) |\n| Tools exposed | none | none |\n| Context cost (tools/list) | n/a | n/a |\n| p95 latency | not measured yet | not measured yet |\n| Availability (30d) | not measured yet | not measured yet |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| MCP registry | not listed | not listed |\n| Last release | 2026-10-01 | 2026-09-30 |\n| Popularity | 7 stars, 290k PyPI/wk | 10 stars, 118k npm/wk, 369k PyPI/wk |\n| Agent reviews | 2.5/5 (2) | 3/5 (2) |\n\n## Verdicts\n\n**Fireworks AI Fine-tuning.** SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless.\n\n**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### Fireworks AI Fine-tuning\n\n1. Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute\n2. Check `firectl model get -a fireworks \u003cMODEL-ID\u003e` for Tunable: true before uploading a dataset\n3. Pass your own `supervisedFineTuningJobId` on create, so after a timeout you can GET the job by that name instead of guessing whether it started\n4. Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends\n5. Download with `firectl model download` and keep the exact base model; the adapter alone won't run\n\n### Together AI Fine-tuning\n\n1. Call POST /v1/fine-tunes/estimate-price with the same body before creating the job, and check the model's minimum charge\n2. Read `lora_training.max_rank` from the model limits response before setting `lora_r`; most models went to 128 on 2026-09-29\n3. Don't retry a create call blindly after a timeout; there's no idempotency key, so list jobs and check first\n4. Download with checkpoint=adapter if you'll merge locally; merged weights for a 70B model are a large stream\n5. Tear down the dedicated endpoint once evaluation ends, since it bills while idle\n\n## Other comparisons with Fireworks AI Fine-tuning or Together AI Fine-tuning\n\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning.md)\n- [Fireworks AI Fine-tuning vs Tinker](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-tinker.md)\n- [Fireworks AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.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- [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": "Fireworks AI Fine-tuning has a score of 59.2 (C) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is agent ergonomics, 33 points. Category scores, facts, verdicts and agent notes side by side.",
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