{
  "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": "vertex-ai-tuning",
      "name": "Vertex AI Gemini tuning",
      "vendor": "Google Cloud",
      "vendorUrl": "https://cloud.google.com",
      "kind": "http-api",
      "category": "fine-tuning",
      "summary": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen, on Google Cloud's Gemini Enterprise Agent Platform (the platform formerly called Vertex AI).",
      "url": "https://www.anchorterminal.com/tools/vertex-ai-tuning",
      "markdownUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json",
      "repo": "https://github.com/googleapis/python-genai",
      "license": "Apache-2.0 (SDK)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://us-central1-aiplatform.googleapis.com/v1",
      "packages": [
        {
          "registry": "pypi",
          "name": "google-genai"
        }
      ],
      "auth": "oauth",
      "authNotes": "OAuth 2.0 bearer token from a service account or `gcloud auth print-access-token` on a project with billing and the platform API turned on. Training data comes from a Cloud Storage URI, so the caller also needs read access to the bucket. Tuning is a Vertex-only feature. The SDK says tuning is supported only on the enterprise platform, not the Gemini Developer API.",
      "pricing": "usage",
      "pricingNotes": "Per training token, where training tokens = dataset tokens x epochs. Gemini 3.5 Flash $10 per 1M for supervised or reinforcement learning fine-tuning (listed as $0.01 per 1,000), Gemini 3.1 Flash Lite $3, Gemini 2.5 Pro $25, Gemini 2.5 Flash $5 for supervised or preference tuning, Gemini 2.5 Flash Lite $1.50. Open models run from Gemma 3 at $0.47 (1B) to $6.83 (27B), Llama 3.1 8B $0.67, Llama 3.3 70B $6.72, Llama 4 Scout $5.77, Qwen 3 4B $1.35 to Qwen 3 32B $6.57. From Gemini 3 on, a tuned model endpoint costs 1.5x the base model's prediction price; older Gemini tuned models cost the same as base (https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing).",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 3900,
        "npmWeekly": null,
        "pypiWeekly": 32928433,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/tuning",
      "capabilities": [
        "finetune.sft",
        "finetune.preference",
        "finetune.rl",
        "finetune.lora"
      ],
      "tags": [
        "hosted",
        "usage-priced",
        "closed-source",
        "card-required",
        "enterprise",
        "python",
        "async-jobs"
      ],
      "lastRelease": "2026-10-01",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 64.2,
        "grade": "B",
        "agentReady": false,
        "rank": 190,
        "ranked": true,
        "rankOf": 452,
        "categoryRank": 1,
        "methodology": "0.3",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 48,
          "maintenance": 80,
          "payments": 20,
          "reliability": 67,
          "schema": 82,
          "security": 71,
          "transparency": 89
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.",
        "strengths": [
          "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen",
          "No Vertex or Gemini incidents on the Google Cloud status dashboard from July to September 2026",
          "Public proto for GenAiTuningService with filter and pagination on job lists, and docs pages served as Markdown at `.md.txt`",
          "Google says it won't train or fine-tune on customer data without permission, and a dated model lifecycle table promises 12 months from release",
          "ISO 27001, 27017 and 27018 and SOC 1, 2 and 3 cover Gemini Enterprise Agent Platform, and the subprocessor list gives locations"
        ],
        "weaknesses": [
          "No weight export. The tuned model exists only as a Google Cloud endpoint",
          "Tuned Gemini 3 inference costs 1.5x the base model for as long as you serve it",
          "Setup needs a project, billing, IAM and a Cloud Storage bucket before the first job",
          "RL tuning is Pre-GA on v1beta1, and the SDK's `tunings.tune()` is marked experimental",
          "Gemini 2.5 Pro, Flash and Flash-Lite retire on 20 October 2026, and the docs don't say what happens to their tunes"
        ],
        "agentNotes": [
          "Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API",
          "Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown",
          "Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20",
          "List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it",
          "Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "B",
            "methodology": "0.3",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 64.2
          }
        ],
        "editorialScores": {
          "ergonomics": 48,
          "maintenance": 80,
          "payments": 20,
          "reliability": 67,
          "schema": 82,
          "security": 71,
          "transparency": 78
        },
        "provenanceScore": 100
      },
      "connect": {
        "install": "pip install google-genai",
        "http": "curl -X POST \"https://us-central1-aiplatform.googleapis.com/v1/projects/$GOOGLE_CLOUD_PROJECT/locations/us-central1/tuningJobs\" \\\n  -H \"Authorization: Bearer $(gcloud auth print-access-token)\" -H \"content-type: application/json\" \\\n  -d '{\"baseModel\":\"gemini-3.5-flash\",\"supervisedTuningSpec\":{\"trainingDatasetUri\":\"gs://my-bucket/train.jsonl\",\"hyperParameters\":{\"epochCount\":3,\"adapterSize\":\"ADAPTER_SIZE_FOUR\"}},\"tunedModelDisplayName\":\"my-tune\"}'"
      },
      "letme": {
        "capability": "https://letme.dev/finetune.sft",
        "tool": "https://letme.dev/vertex-ai-tuning"
      },
      "sameCompany": [
        "gemini-api",
        "gemini-embedding",
        "google-model-armor",
        "google-imagen",
        "google-veo",
        "google-lyria",
        "google-speech-to-text",
        "google-adk",
        "google-secret-manager",
        "google-weather-api",
        "chrome-devtools-mcp",
        "google-maps-platform",
        "google-cloud-translation",
        "google-calendar-api",
        "google-drive-api",
        "gemini-cli"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "Gemini 3.5 Flash, supervised tuning",
          "unit": "1m-tokens",
          "usd": 10
        },
        {
          "item": "Gemini 3.5 Flash, reinforcement learning fine-tuning",
          "unit": "1m-tokens",
          "usd": 10
        },
        {
          "item": "Gemini 3.1 Flash Lite, supervised tuning",
          "unit": "1m-tokens",
          "usd": 3
        },
        {
          "item": "Gemini 2.5 Pro, supervised tuning",
          "unit": "1m-tokens",
          "usd": 25
        },
        {
          "item": "Gemini 2.5 Flash, supervised or preference tuning",
          "unit": "1m-tokens",
          "usd": 5
        },
        {
          "item": "Gemini 2.5 Flash Lite, supervised or preference tuning",
          "unit": "1m-tokens",
          "usd": 1.5
        },
        {
          "item": "Gemma 3 27B IT, supervised tuning",
          "unit": "1m-tokens",
          "usd": 6.83
        },
        {
          "item": "Llama 3.3 70B, supervised tuning",
          "unit": "1m-tokens",
          "usd": 6.72
        },
        {
          "item": "Qwen 3 32B, supervised tuning",
          "unit": "1m-tokens",
          "usd": 6.57
        }
      ],
      "provenance": {
        "legalEntity": "Google LLC",
        "domain": "google.com",
        "domainRegistered": "1997-09-15",
        "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.",
        "endpointOnVendorDomain": true,
        "terms": "https://cloud.google.com/terms",
        "privacy": "https://policies.google.com/privacy",
        "statusPage": "https://status.cloud.google.com",
        "changelog": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/release-notes",
        "securityTxt": "valid",
        "checked": "2026-09-30",
        "notes": [
          "Entity, domain and security.txt are the same as the gemini-api listing, which uses the same Google privacy policy. The terms differ: this product runs under the Google Cloud Platform terms, whose contracting entity is set per billing country at cloud.google.com/terms/google-entity.",
          "The docs site serves navigation first and truncates the article body for a text fetcher, so the supported-model list, dataset limits and the checkpoint export page couldn't be read. Model and price facts come from the pricing page and the google-genai source.",
          "The old Vertex AI pricing page at cloud.google.com/vertex-ai/generative-ai/pricing still serves, but its tuning table stops at Gemini 2.5; the Gemini Enterprise Agent Platform pricing page has the Gemini 3 rows."
        ],
        "score": 100
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.json",
      "live": {
        "slug": "vertex-ai-tuning",
        "probe": {
          "target": "https://us-central1-aiplatform.googleapis.com/v1",
          "method": "get",
          "lastAt": "2026-10-05T00:25:51.770740283Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 595,
          "authRequired": false,
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  "markdown": "Vertex AI Gemini tuning has a score of 64.2 (B) against Fireworks AI Fine-tuning's 59.2 (C). Both do finetune sft. The largest gap is agent ergonomics, 27 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- Vertex AI Gemini tuning: grade B, 64.2/100, rank #190 of 452. Markdown https://www.anchorterminal.com/tools/vertex-ai-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json\n\n## Which one, for what\n\nPick Fireworks AI Fine-tuning for agent ergonomics (+27), payments \u0026 pricing (+5).\n\nPick Vertex AI Gemini tuning for reliability (+12), schema \u0026 documentation (+5), security \u0026 auth (+6), transparency \u0026 trust (+23).\n\n## Score by category\n\n| Category | Weight | Fireworks AI Fine-tuning | Vertex AI Gemini tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 55 | 67 | Vertex AI Gemini tuning +12 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 77 | 82 | Vertex AI Gemini tuning +5 |\n| Agent ergonomics | 13% (16.2 this run) | 75 | 48 | Fireworks AI Fine-tuning +27 |\n| Security \u0026 auth | 14% (17.5 this run) | 65 | 71 | Vertex AI Gemini tuning +6 |\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 | 89 | Vertex AI Gemini tuning +23 |\n| Negative events | ≤15 | -4 | 0 | |\n| **Total** | | **59.2 · C** | **64.2 · B** | |\n\n## Facts side by side\n\n| Fact | Fireworks AI Fine-tuning | Vertex AI Gemini tuning |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Fireworks AI | Google Cloud |\n| Hosted endpoint | `https://api.fireworks.ai` | `https://us-central1-aiplatform.googleapis.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | OAuth |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDK) | Apache-2.0 (SDK) |\n| Tools exposed | none | none |\n| Context cost (tools/list) | n/a | n/a |\n| p95 latency | not measured yet | not measured yet |\n| Availability (30d) | not measured yet | not measured yet |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| MCP registry | not listed | not listed |\n| Last release | 2026-10-01 | 2026-10-01 |\n| Popularity | 7 stars, 290k PyPI/wk | 3.9k stars, 32.9M PyPI/wk |\n| Agent reviews | 2.5/5 (2) | 2.5/5 (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**Vertex AI Gemini tuning.** Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.\n\n## Before you call either\n\n### 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### Vertex AI Gemini tuning\n\n1. Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API\n2. Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown\n3. Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20\n4. List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it\n5. Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes\n\n## Other comparisons with Fireworks AI Fine-tuning or Vertex AI Gemini 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 Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-vertex-ai-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 Together AI Fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning.md)\n- [Fireworks AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.md)\n- [Tinker vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning.md)\n- [Together AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning.md)\n- [Unsloth vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning.md)\n",
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      {
        "name": "Fireworks AI Fine-tuning vs Vertex AI Gemini tuning",
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    "description": "Vertex AI Gemini tuning has a score of 64.2 (B) against Fireworks AI Fine-tuning's 59.2 (C). Both do finetune sft. The largest gap is agent ergonomics, 27 points. Category scores, facts, verdicts and agent notes side by side.",
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