{
  "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": "tinker",
      "name": "Tinker",
      "vendor": "Thinking Machines Lab",
      "vendorUrl": "https://thinkingmachines.ai/tinker/",
      "kind": "sdk",
      "category": "fine-tuning",
      "summary": "Thinking Machines Lab's API for model training.",
      "url": "https://www.anchorterminal.com/tools/tinker",
      "markdownUrl": "https://www.anchorterminal.com/tools/tinker.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/tinker.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/tinker.json",
      "repo": "https://github.com/thinking-machines-lab/tinker-cookbook",
      "license": "Apache-2.0 (cookbook)",
      "transports": [
        "http"
      ],
      "packages": [
        {
          "registry": "pypi",
          "name": "tinker"
        },
        {
          "registry": "pypi",
          "name": "tinker-cookbook"
        }
      ],
      "auth": "api-key",
      "authNotes": "API key from the Tinker console, exported as `TINKER_API_KEY`, or `tinker auth login`. Sign-up is at auth.thinkingmachines.ai and the quickstart says to add payment details in Billing before training.",
      "pricing": "usage",
      "pricingNotes": "Per 1M tokens, split into prefill, cached prefill (20 per cent of prefill), sample and train. Qwen3.8-27B $1.86 prefill, $5.595 sample, $4.103 train; Qwen3.5-9B $0.66, $1.995, $1.463; GPT-OSS-20B $0.18, $0.45, $0.396; DeepSeek-V3.1 $1.695, $4.215, $3.718; Inkling $1.87, $4.68, $5.61; Inkling-Small $0.58, $1.44, $1.73. MoE models are priced by active parameters. Checkpoint storage $0.10 per GB-month. Prices rose on 2026-07-17 for standard-context models. No free credits are mentioned (https://tinker-docs.thinkingmachines.ai/tinker/models/).",
      "priceSummary": "Pay per use",
      "where": "local",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 4000,
        "npmWeekly": null,
        "pypiWeekly": 330895,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://tinker-docs.thinkingmachines.ai",
      "llmsTxt": "https://tinker-docs.thinkingmachines.ai/llms.txt",
      "capabilities": [
        "finetune.sft",
        "finetune.preference",
        "finetune.rl",
        "finetune.lora",
        "finetune.export"
      ],
      "tags": [
        "hosted",
        "usage-priced",
        "card-required",
        "open-weights",
        "llms-txt",
        "python",
        "open-source"
      ],
      "lastRelease": "2026-09-30",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 51.2,
        "grade": "D",
        "agentReady": false,
        "rank": 354,
        "ranked": true,
        "rankOf": 452,
        "categoryRank": 6,
        "methodology": "0.3",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 53,
          "maintenance": 87,
          "payments": 20,
          "reliability": 35,
          "schema": 70,
          "security": 55,
          "transparency": 51
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "Full control of the training loop with the GPUs abstracted away, plus recipes for SFT, DPO, RL and distillation. LoRA only; no full-parameter training.",
        "strengths": [
          "Full control of the training loop with the GPUs abstracted away, plus recipes for SFT, DPO, RL and distillation",
          "Checkpoints download and merge into Hugging Face safetensors, so the weights can leave",
          "Per-token billing with machine-readable prices in models.json",
          "Ten SDK releases in September 2026 and a dated changelog that names removals",
          "Audit log through the SDK for admins, and SDK retries with stable request IDs"
        ],
        "weaknesses": [
          "LoRA only; no full-parameter training",
          "Python SDK only, with no REST reference or OpenAPI",
          "No terms of service, status page or SLA found",
          "The privacy notice (August 2025) doesn't cover training data or weights",
          "Standard-context prices rose on 2026-07-17, and there's no free tier"
        ],
        "agentNotes": [
          "Set `TINKER_API_KEY` and start from the cookbook recipes rather than the raw primitives",
          "Read the 'Avoid Client-Side Timeouts and Retries' guide before wrapping sampling calls in your own retries; the SDK already retries sampling with stable request IDs",
          "Save intermediate checkpoints with a TTL between 1 hour and 10 years; storage bills at $0.10 a GB-month until they expire",
          "Read models.json for current prices before a run; sampling tokens cost more than training tokens on the open models",
          "Check the model deprecations page before pinning a base model; 18 were retired on 2026-06-12"
        ],
        "metrics": {
          "kind": "remote",
          "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": 51.2
          }
        ],
        "editorialScores": {
          "ergonomics": 53,
          "maintenance": 87,
          "payments": 20,
          "reliability": 35,
          "schema": 70,
          "security": 55,
          "transparency": 30
        },
        "provenanceScore": 71
      },
      "connect": {
        "install": "uv pip install tinker tinker-cookbook   # then export TINKER_API_KEY=..."
      },
      "letme": {
        "capability": "https://letme.dev/finetune.sft",
        "tool": "https://letme.dev/tinker"
      },
      "area": "models",
      "unitPrices": [
        {
          "item": "Qwen3.8-27B, training",
          "unit": "1m-tokens",
          "usd": 4.103
        },
        {
          "item": "Qwen3.8-27B, sampling",
          "unit": "1m-tokens",
          "usd": 5.595
        },
        {
          "item": "Qwen3.8-27B, prefill",
          "unit": "1m-tokens",
          "usd": 1.86,
          "note": "Cached prefill $0.372"
        },
        {
          "item": "Qwen3.5-9B, training",
          "unit": "1m-tokens",
          "usd": 1.463
        },
        {
          "item": "GPT-OSS-20B, training",
          "unit": "1m-tokens",
          "usd": 0.396
        },
        {
          "item": "DeepSeek-V3.1, training",
          "unit": "1m-tokens",
          "usd": 3.718
        },
        {
          "item": "Inkling, training",
          "unit": "1m-tokens",
          "usd": 5.61
        },
        {
          "item": "Inkling-Small, training",
          "unit": "1m-tokens",
          "usd": 1.73
        },
        {
          "item": "Checkpoint storage",
          "unit": "gb-month",
          "usd": 0.1
        }
      ],
      "provenance": {
        "legalEntity": "Thinking Machines Labs, Inc.",
        "domain": "thinkingmachines.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "https://thinkingmachines.ai/privacy/",
        "statusPage": "",
        "changelog": "https://tinker-docs.thinkingmachines.ai/changelog/",
        "securityTxt": "valid",
        "checked": "2026-09-30",
        "notes": [
          "The privacy notice (2025-08-18) names Thinking Machines Labs, Inc. as data controller and gives no address. We found no terms of service page on thinkingmachines.ai or the docs; the support page links only to email, Discord and GitHub.",
          "The service is reached through the SDK's ServiceClient with an undocumented default base URL, so there's no endpoint to check against the domain.",
          "security.txt at thinkingmachines.ai lists security-reports@thinkingmachines.ai and expires 2029-07-13.",
          "No status page was found.",
          "The .ai registry's RDAP server refused our requests, so the registration date is blank."
        ],
        "score": 71
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/tinker.json",
      "live": {
        "slug": "tinker",
        "versions": [
          {
            "registry": "github",
            "name": "thinking-machines-lab/tinker-cookbook",
            "version": "v0.5.7",
            "released": "2026-09-03",
            "seenAt": "2026-10-04T16:41:59.205166565Z"
          },
          {
            "registry": "pypi",
            "name": "tinker",
            "version": "0.32.0",
            "released": "2026-10-02",
            "seenAt": "2026-10-04T16:41:57.121115125Z"
          },
          {
            "registry": "pypi",
            "name": "tinker-cookbook",
            "version": "0.5.7",
            "released": "2026-09-03",
            "seenAt": "2026-10-04T16:41:57.304569133Z"
          }
        ],
        "githubStars": 4172,
        "pypiWeekly": 384718,
        "securityTxt": {
          "url": "https://thinkingmachines.ai/.well-known/security.txt",
          "state": "valid",
          "expires": "2029-07-13T07:00:00.000Z",
          "checkedAt": "2026-10-04T15:15:51.616624235Z"
        },
        "llmsTxt": {
          "url": "https://tinker-docs.thinkingmachines.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-04T15:18:17.9955218Z"
        },
        "domain": {
          "domain": "thinkingmachines.ai",
          "registered": "2024-07-09",
          "source": "https://rdap.identitydigital.services/rdap/domain/thinkingmachines.ai",
          "checkedAt": "2026-10-04T13:07:26.919974206Z"
        },
        "pages": [
          {
            "url": "https://tinker-docs.thinkingmachines.ai/changelog/",
            "kind": "changelog",
            "status": 200,
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  "markdown": "Fireworks AI Fine-tuning has a score of 59.2 (C) against Tinker's 51.2 (D). Both do finetune sft. The largest gap is agent ergonomics, 22 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- Tinker: grade D, 51.2/100, rank #354 of 452. Markdown https://www.anchorterminal.com/tools/tinker.md · JSON https://www.anchorterminal.com/api/v1/tools/tinker.json\n\n## Which one, for what\n\nPick Fireworks AI Fine-tuning for reliability (+20), schema \u0026 documentation (+7), agent ergonomics (+22), security \u0026 auth (+10), payments \u0026 pricing (+5), transparency \u0026 trust (+15).\n\nPick Tinker for maintenance \u0026 community (+5).\n\n## Score by category\n\n| Category | Weight | Fireworks AI Fine-tuning | Tinker | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 55 | 35 | Fireworks AI Fine-tuning +20 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 77 | 70 | Fireworks AI Fine-tuning +7 |\n| Agent ergonomics | 13% (16.2 this run) | 75 | 53 | Fireworks AI Fine-tuning +22 |\n| Security \u0026 auth | 14% (17.5 this run) | 65 | 55 | Fireworks AI Fine-tuning +10 |\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 | 87 | Tinker +5 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 66 | 51 | Fireworks AI Fine-tuning +15 |\n| Negative events | ≤15 | -4 | 0 | |\n| **Total** | | **59.2 · C** | **51.2 · D** | |\n\n## Facts side by side\n\n| Fact | Fireworks AI Fine-tuning | Tinker |\n| --- | --- | --- |\n| Kind | HTTP API | SDK + MCP |\n| Vendor | Fireworks AI | Thinking Machines Lab |\n| Hosted endpoint | `https://api.fireworks.ai` | no (local only) |\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 (cookbook) |\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 | 4k stars, 331k PyPI/wk |\n| Agent reviews | 2.5/5 (2) | 3.5/5 (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**Tinker.** Full control of the training loop with the GPUs abstracted away, plus recipes for SFT, DPO, RL and distillation. LoRA only; no full-parameter training.\n\n## 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### Tinker\n\n1. Set `TINKER_API_KEY` and start from the cookbook recipes rather than the raw primitives\n2. Read the 'Avoid Client-Side Timeouts and Retries' guide before wrapping sampling calls in your own retries; the SDK already retries sampling with stable request IDs\n3. Save intermediate checkpoints with a TTL between 1 hour and 10 years; storage bills at $0.10 a GB-month until they expire\n4. Read models.json for current prices before a run; sampling tokens cost more than training tokens on the open models\n5. Check the model deprecations page before pinning a base model; 18 were retired on 2026-06-12\n\n## Other comparisons with Fireworks AI Fine-tuning or Tinker\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 Tinker](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-tinker.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- [Fireworks AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.md)\n- [Tinker vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.md)\n- [Tinker vs Unsloth](https://www.anchorterminal.com/compare/tinker-vs-unsloth.md)\n- [Tinker vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning.md)\n",
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    "description": "Fireworks AI Fine-tuning has a score of 59.2 (C) against Tinker's 51.2 (D). Both do finetune sft. The largest gap is agent ergonomics, 22 points. Category scores, facts, verdicts and agent notes side by side.",
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    "title": "Fireworks AI Fine-tuning vs Tinker for AI agents, C 59.2 vs D 51.2",
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