{ "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, "grade": "C", "agentReady": false, "rank": 506, "ranked": true, "rankOf": 842, "categoryRank": 5, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 75, "maintenance": 82, "payments": 25, "reliability": 55, "schema": 77, "security": 65, "transparency": 64 }, "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.", "bestFor": "Teams that want managed SFT, DPO or RFT on large open models and may later write a custom RL loop on the same platform.", "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 $1 sign-up credit buys inference only", "The status page has no training component; a 46-hour cloud provider incident in August hit dedicated deployments", "Two Serverless Training base models were deprecated with same-day effect on 2026-08-26", "Audit logs are Enterprise only and security.txt returns 404" ], "agentNotes": [ "Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute", "Check `firectl model get -a fireworks \u003cMODEL-ID\u003e` for Tunable: true before uploading a dataset", "Pass your own `supervisedFineTuningJobId` on create, so after a timeout you can GET the job by that name instead of guessing whether it started", "Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends", "Download with `firectl model download` and keep the exact base model; the adapter alone won't run" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "ergonomics": 75, "maintenance": 82, "payments": 25, "reliability": 55, "schema": 77, "security": 65, "transparency": 57 }, "provenanceScore": 71 }, "connect": { "install": "pip install fireworks-ai # add [training] for the Training API", "http": "curl https://api.fireworks.ai/v1/accounts/$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": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/fireworks-fine-tuning.json", "live": { "slug": "fireworks-fine-tuning", "probe": { "target": "https://api.fireworks.ai", "method": "get", "lastAt": "2026-10-09T11:46:28.699286695Z", "lastOk": true, "lastStatus": 404, "lastMs": 28, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 30, "p95ms24h": 71, "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.fireworks.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-09T11:39:22.120211378Z" }, "versions": [ { "registry": "github", "name": "fw-ai-external/python-sdk", "version": "v1.2.20", "released": "2026-10-06", "seenAt": "2026-10-08T16:11:59.751275418Z" }, { "registry": "pypi", "name": "fireworks-ai", "version": "1.2.20", "released": "2026-10-06", "seenAt": "2026-10-08T16:11:59.542957612Z" } ], "githubStars": 10, "pypiWeekly": 281946, "securityTxt": { "url": "https://fireworks.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:39:06.393055593Z" }, "llmsTxt": { "url": "https://docs.fireworks.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:26.379688262Z" }, "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": "2026-10-08T18:18:43.993471365Z", "changedAt": "2026-10-08T18:18:43.993471365Z", "fingerprint": "73724d4498c3" }, { "url": "https://fireworks.ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:20:23.293613999Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "9f6b298d1e67" }, { "url": "https://fireworks.ai/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-08T18:20:25.487694157Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "7220d287a92b" }, { "url": "https://fireworks.ai/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:20:27.561123635Z", "changedAt": "0001-01-01T00:00:00Z" } ], "updatedAt": "2026-10-09T11:46:28.699286695Z" } }, "answer": "Fireworks AI Fine-tuning scores 59 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 6 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency \u0026 trust.", "b": { "slug": "nebius-token-factory-fine-tuning", "name": "Nebius Token Factory fine-tuning", "vendor": "Nebius", "vendorUrl": "https://nebius.com", "kind": "http-api", "category": "fine-tuning", "summary": "Nebius Token Factory runs supervised fine-tuning jobs on open models such as Llama, Qwen, gpt-oss, Gemma and DeepSeek through an OpenAI-compatible REST API, with LoRA or full weights and downloadable checkpoints.", "url": "https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nebius-token-factory-fine-tuning.json", "repo": "https://github.com/nebius/token-factory-cookbook", "license": "Proprietary service (cookbook examples MIT)", "transports": [ "http" ], "remoteUrl": "https://api.tokenfactory.nebius.com/v1", "packages": [], "auth": "api-key", "authNotes": "`Authorization: Bearer` with an API key created by a person in the console under API keys, shown once and read from `NEBIUS_API_KEY` in the docs. Keys belong to a project. Project Admins and Members both have full access to the Files and Fine-tuning APIs, and no per-key scopes or expiry were found in the reviewed documentation.", "pricing": "usage", "pricingNotes": "Pay as you go, with no monthly fee stated. No fine-tuning price was found in the docs or in the public catalogue at `/api/public/models_info`. The product page sends readers to the Token Factory console's prices page, a script-drawn console page that robots.txt disallows, so we did not read it. A bank card is mandatory at onboarding. New accounts get $1 of trial credit valid for 30 days (https://docs.tokenfactory.nebius.com/other-capabilities/billing-new.md). Dedicated endpoints bill while one or more replicas are ready.", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs index, the OpenAPI file or the billing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://docs.tokenfactory.nebius.com/post-training/overview", "llmsTxt": "https://docs.tokenfactory.nebius.com/llms.txt", "openapi": "https://api.tokenfactory.nebius.com/openapi.json", "capabilities": [ "finetune.sft", "finetune.lora", "finetune.export" ], "tags": [ "hosted", "usage-priced", "card-required", "open-weights", "llms-txt", "openapi", "async-jobs" ], "lastRelease": "2026-09-30", "graded": true, "anchor": { "graded": true, "score": 47.7, "grade": "D", "agentReady": false, "rank": 738, "ranked": true, "rankOf": 842, "categoryRank": 9, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 51, "maintenance": 61, "payments": 0, "reliability": 45, "schema": 68, "security": 52, "transparency": 79 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": -2, "negativeNotes": [ "-2: on 2026-10-08 the product page said a fine-tuned model goes live with one click on 'serverless endpoints, on-demand GPU, or dedicated enterprise clusters', while the docs say deployment is by dedicated endpoints only and custom model weights are in beta on request, and the guide's deployment links return 404 (https://nebius.com/services/token-factory/fine-tuning, https://docs.tokenfactory.nebius.com/post-training/models.md)" ], "verdict": "Supervised fine-tuning on 49 open base models through OpenAI-style `/v1/fine_tuning/jobs` calls, with LoRA or full weights and every checkpoint file downloadable. No fine-tuning price was found outside the script-drawn console, and the docs say tuned models deploy only to dedicated endpoints, with custom weights in beta on request.", "bestFor": "Teams that want supervised LoRA or full fine-tuning of a wide list of open models, up to Qwen3 Coder 480B and DeepSeek, through OpenAI-style calls, with EU storage and the weights to take away.", "strengths": [ "49 base models listed, 42 with LoRA and full fine-tuning and 7 with full fine-tuning only, at context lengths from 8,192 to 131,072 tokens", "Checkpoint files download through `GET /v1/files/{file_id}/content`, and an `hf` integration pushes the result to a Hugging Face repository", "A public OpenAPI 3.1 file covers the fine-tuning, files, datasets and operations paths, with ranges on every hyperparameter", "The legal guide says content is not used to train models and that customers own the models they fine-tune", "A dated sub-processor list for Token Factory, with 15 days' notice of changes, and a valid security.txt on nebius.com" ], "weaknesses": [ "No fine-tuning price found in the docs or the public catalogue JSON. The price page is a script-drawn console page that robots.txt disallows", "The models page says deployment is by dedicated endpoints only, and custom model weights are in beta and available on request", "A bank card is mandatory at onboarding, so the $1 trial credit (30 days) is not a card-free trial", "Two major incidents tagged Token Factory in 90 days, 93 minutes on 27 July and about 21.5 hours in us-central1 from 19 August 2026", "No public changelog, no idempotency key on job creation and only a 422 response documented in the reference", "The Services Agreement (clause 4.1.10) forbids competitive analysis or benchmarking" ], "agentNotes": [ "Use the OpenAI client with `base_url` `https://api.tokenfactory.nebius.com/v1/` and `NEBIUS_API_KEY`. Upload JSONL with `purpose=fine-tune`, then create the job.", "Set `hyperparameters.lora` to true for an adapter. The default is false, which runs full fine-tuning.", "Poll `GET /v1/fine_tuning/jobs/{job_id}` no faster than every 15 seconds. There is no idempotency key, so list jobs before recreating one after a timeout.", "Download every ID in a checkpoint's `result_files` before relying on hosted copies. The terms allow deletion of tuned models at three days' notice.", "The spec requires `wandb.api_key` although the guide omits it, and it also accepts `mlflow` and `hf` integrations. Check the price in the console before starting a job." ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 47.7 } ], "editorialScores": { "ergonomics": 51, "maintenance": 61, "payments": 0, "reliability": 45, "schema": 68, "security": 52, "transparency": 70 }, "provenanceScore": 88 }, "connect": { "install": "pip3 install --upgrade openai", "http": "curl 'https://api.tokenfactory.nebius.com/v1/fine_tuning/jobs' \\\n -X POST \\\n -H 'Accept: application/json' \\\n -H 'Content-Type: application/json' \\\n -H \"Authorization: Bearer $NEBIUS_API_KEY\" \\\n -d '{\"model\":\"meta-llama/Llama-3.1-8B-Instruct\",\"suffix\":\"my-domain-adapter\",\"training_file\":\"\u003ctraining_file_ID\u003e\",\"hyperparameters\":{\"n_epochs\":3,\"lora\":true,\"lora_r\":16,\"lora_alpha\":16}}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/nebius-token-factory-fine-tuning" }, "sameCompany": [ "nebius-ai-cloud" ], "area": "models", "provenance": { "legalEntity": "Nebius B.V.", "domain": "nebius.com", "domainRegistered": "2004-06-26", "endpointOnVendorDomain": true, "terms": "https://docs.nebius.com/legal/agreement", "privacy": "https://docs.nebius.com/legal/privacy", "statusPage": "https://status.nebius.com", "changelog": "", "securityTxt": "valid", "checked": "2026-10-08", "notes": [ "The Nebius Services Agreement (published 15 September 2026, effective 28 September 2026) names Nebius B.V. as the contracting entity by default, Nebius Inc. for US customers who registered from 15 September 2026 and Nebius Israel Ltd for some customers in Israel. The parent is Nebius Group N.V.", "The Token Factory Supplemental Terms at https://docs.nebius.com/legal/token-factory are incorporated into the agreement and carry the fine-tuning clauses.", "The privacy policy (effective 23 September 2026) names Nebius Token Factory in its scope and covers data Nebius holds as controller. Customer content is covered by the DPA at https://docs.nebius.com/legal/dpa.", "security.txt at nebius.com gives security@nebius.com and expires on 31 December 2027. The same path on tokenfactory.nebius.com returns the console's HTML shell.", "status.nebius.com is an Atlassian Statuspage for the whole Nebius cloud, with a Token Factory component in each of nine regions and no separate fine-tuning component.", "No public changelog for Token Factory was found in the docs index. The OpenAPI file carries the version stamp 20260930-cfb76be12.", "nebius.com was registered on 26 June 2004 per Verisign RDAP." ], "score": 88 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.json", "live": { "slug": "nebius-token-factory-fine-tuning", "probe": { "target": "https://api.tokenfactory.nebius.com/v1", "method": "get", "lastAt": "2026-10-09T11:46:35.082907032Z", "lastOk": true, "lastStatus": 404, "lastMs": 90, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 87, "p95ms24h": 167, "samples24h": 44, "samples30d": 44, "days": [ { "date": "2026-10-09", "probes": 44, "ok": 44 } ] }, "vendorStatus": { "page": "https://status.nebius.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-09T11:39:58.137422205Z" }, "updatedAt": "2026-10-09T11:46:35.082907032Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Fireworks AI", "b": "Nebius", "name": "Vendor" }, { "a": "https://api.fireworks.ai", "b": "https://api.tokenfactory.nebius.com/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "$0.50 per 1M tokens", "b": "not published", "name": "Price for finetune sft" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0 (SDK)", "b": "Proprietary service (cookbook examples MIT)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-01", "b": "2026-09-30", "name": "Last release" }, { "a": "couldn't be read", "b": "2026-09-28", "name": "Terms last updated" }, { "a": "no date given", "b": "2026-09-23", "name": "Privacy policy last updated" }, { "a": "couldn't be read", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "couldn't be read", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "couldn't be read", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "couldn't be read", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "couldn't be read", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "7 stars, 290k PyPI/wk", "b": "none", "name": "Popularity" }, { "a": "2.5/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Fireworks AI Fine-tuning scores 59 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 6 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency \u0026 trust.", "question": "Which is better for AI agents, Fireworks AI Fine-tuning or Nebius Token Factory fine-tuning?" }, { "answer": "Both need an API key.", "question": "Do Fireworks AI Fine-tuning and Nebius Token Factory fine-tuning need an API key?" }, { "answer": "Yes. Fireworks AI Fine-tuning has a hosted endpoint at https://api.fireworks.ai and Nebius Token Factory fine-tuning at https://api.tokenfactory.nebius.com/v1.", "question": "Can an agent call Fireworks AI Fine-tuning and Nebius Token Factory fine-tuning without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 55 against 45", "Schema \u0026 documentation, 77 against 68", "Agent ergonomics, 75 against 51", "Security \u0026 auth, 65 against 52", "Payments \u0026 pricing, 25 against 0", "Maintenance \u0026 community, 82 against 61" ], "also": null, "goodFor": "Teams that want managed SFT, DPO or RFT on large open models and may later write a custom RL loop on the same platform.", "slug": "fireworks-fine-tuning", "watchFor": "Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless" }, { "aheadOn": [ "Transparency \u0026 trust, 79 against 64" ], "also": null, "goodFor": "Teams that want supervised LoRA or full fine-tuning of a wide list of open models, up to Qwen3 Coder 480B and DeepSeek, through OpenAI-style calls, with EU storage and the weights to take away.", "slug": "nebius-token-factory-fine-tuning", "watchFor": "No fine-tuning price found in the docs or the public catalogue JSON. The price page is a script-drawn console page that robots.txt disallows" } ], "job": { "capability": "finetune.sft", "name": "Finetune sft" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning.json", "title": "Amazon Bedrock model customisation vs Fireworks AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-nebius-token-factory-fine-tuning.json", "title": "Amazon Bedrock model customisation vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-nebius-token-factory-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/azure-foundry-fine-tuning-vs-fireworks-fine-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Fireworks AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-tinker.json", "title": "Fireworks AI Fine-tuning vs Tinker", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-tinker" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.json", "title": "Fireworks AI Fine-tuning vs Unsloth", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.json", "title": "Fireworks AI Fine-tuning vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning" }, { "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-tinker.json", "title": "Nebius Token Factory fine-tuning vs Tinker", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-tinker" }, { "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning.json", "title": "Nebius Token Factory fine-tuning vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-unsloth.json", "title": "Nebius Token Factory fine-tuning vs Unsloth", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.json", "title": "Nebius Token Factory fine-tuning vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning" } ], "scores": [ { "by": 10, "edge": "fireworks-fine-tuning", "fireworks-fine-tuning": 55, "key": "reliability", "name": "Reliability", "nebius-token-factory-fine-tuning": 45, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 9, "edge": "fireworks-fine-tuning", "fireworks-fine-tuning": 77, "key": "schema", "name": "Schema \u0026 documentation", "nebius-token-factory-fine-tuning": 68, "weight": 13 }, { "by": 24, "edge": "fireworks-fine-tuning", "fireworks-fine-tuning": 75, "key": "ergonomics", "name": "Agent ergonomics", "nebius-token-factory-fine-tuning": 51, "weight": 13 }, { "by": 13, "edge": "fireworks-fine-tuning", "fireworks-fine-tuning": 65, "key": "security", "name": "Security \u0026 auth", "nebius-token-factory-fine-tuning": 52, "weight": 14 }, { "by": 25, "edge": "fireworks-fine-tuning", "fireworks-fine-tuning": 25, "key": "payments", "name": "Payments \u0026 pricing", "nebius-token-factory-fine-tuning": 0, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 21, "edge": "fireworks-fine-tuning", "fireworks-fine-tuning": 82, "key": "maintenance", "name": "Maintenance \u0026 community", "nebius-token-factory-fine-tuning": 61, "weight": 7 }, { "by": 15, "edge": "nebius-token-factory-fine-tuning", "fireworks-fine-tuning": 64, "key": "transparency", "name": "Transparency \u0026 trust", "nebius-token-factory-fine-tuning": 79, "weight": 7 } ], "summary": "Fireworks AI Fine-tuning scores 59 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 6 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency \u0026 trust. Both do finetune sft.", "verdicts": { "fireworks-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.", "nebius-token-factory-fine-tuning": "Supervised fine-tuning on 49 open base models through OpenAI-style `/v1/fine_tuning/jobs` calls, with LoRA or full weights and every checkpoint file downloadable. No fine-tuning price was found outside the script-drawn console, and the docs say tuned models deploy only to dedicated endpoints, with custom weights in beta on request." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning", "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.md", "slim": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.min.md" }, "markdown": "Fireworks AI Fine-tuning scores 59 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 6 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency \u0026 trust. Both do finetune sft.\n\n- Fireworks AI Fine-tuning: grade C, 59/100, rank #506 of 842. Markdown https://www.anchorterminal.com/tools/fireworks-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json\n- Nebius Token Factory fine-tuning: grade D, 47.7/100, rank #738 of 842. Markdown https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/nebius-token-factory-fine-tuning.json\n\n## Which one, for what\n\n### Fireworks AI Fine-tuning (C)\n\nGood for: Teams that want managed SFT, DPO or RFT on large open models and may later write a custom RL loop on the same platform.\n\nAhead on:\n- Reliability, 55 against 45\n- Schema \u0026 documentation, 77 against 68\n- Agent ergonomics, 75 against 51\n- Security \u0026 auth, 65 against 52\n- Payments \u0026 pricing, 25 against 0\n- Maintenance \u0026 community, 82 against 61\n\nWatch for: Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless\n\n### Nebius Token Factory fine-tuning (D)\n\nGood for: Teams that want supervised LoRA or full fine-tuning of a wide list of open models, up to Qwen3 Coder 480B and DeepSeek, through OpenAI-style calls, with EU storage and the weights to take away.\n\nAhead on:\n- Transparency \u0026 trust, 79 against 64\n\nWatch for: No fine-tuning price found in the docs or the public catalogue JSON. The price page is a script-drawn console page that robots.txt disallows\n\n\n## Score by category\n\n| Category | Weight | Fireworks AI Fine-tuning | Nebius Token Factory fine-tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 55 | 45 | Fireworks AI Fine-tuning +10 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 77 | 68 | Fireworks AI Fine-tuning +9 |\n| Agent ergonomics | 13% (16.2 this run) | 75 | 51 | Fireworks AI Fine-tuning +24 |\n| Security \u0026 auth | 14% (17.5 this run) | 65 | 52 | Fireworks AI Fine-tuning +13 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 25 | 0 | Fireworks AI Fine-tuning +25 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 82 | 61 | Fireworks AI Fine-tuning +21 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 64 | 79 | Nebius Token Factory fine-tuning +15 |\n| Negative events | ≤15 | -4 | -2 | |\n| **Total** | | **59 · C** | **47.7 · D** | |\n\n## Facts side by side\n\n| Fact | Fireworks AI Fine-tuning | Nebius Token Factory fine-tuning |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Fireworks AI | Nebius |\n| Hosted endpoint | `https://api.fireworks.ai` | `https://api.tokenfactory.nebius.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\n| Price for finetune sft | $0.50 per 1M tokens | not published |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDK) | Proprietary service (cookbook examples MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-01 | 2026-09-30 |\n| Terms last updated | couldn't be read | 2026-09-28 |\n| Privacy policy last updated | no date given | 2026-09-23 |\n| Customer content may train models | couldn't be read | not found in the text |\n| Terms restrict automated access | couldn't be read | not found in the text |\n| Terms restrict benchmarking | couldn't be read | yes |\n| Terms or service can change without notice | couldn't be read | not found in the text |\n| Arbitration or class-action waiver | couldn't be read | yes |\n| Popularity | 7 stars, 290k PyPI/wk | none |\n| Agent reviews | 2.5/5 (2) | none |\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**Nebius Token Factory fine-tuning.** Supervised fine-tuning on 49 open base models through OpenAI-style `/v1/fine_tuning/jobs` calls, with LoRA or full weights and every checkpoint file downloadable. No fine-tuning price was found outside the script-drawn console, and the docs say tuned models deploy only to dedicated endpoints, with custom weights in beta on request.\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### Nebius Token Factory fine-tuning\n\n1. Use the OpenAI client with `base_url` `https://api.tokenfactory.nebius.com/v1/` and `NEBIUS_API_KEY`. Upload JSONL with `purpose=fine-tune`, then create the job.\n2. Set `hyperparameters.lora` to true for an adapter. The default is false, which runs full fine-tuning.\n3. Poll `GET /v1/fine_tuning/jobs/{job_id}` no faster than every 15 seconds. There is no idempotency key, so list jobs before recreating one after a timeout.\n4. Download every ID in a checkpoint's `result_files` before relying on hosted copies. The terms allow deletion of tuned models at three days' notice.\n5. The spec requires `wandb.api_key` although the guide omits it, and it also accepts `mlflow` and `hf` integrations. Check the price in the console before starting a job.\n\n## Questions\n\n### Which is better for AI agents, Fireworks AI Fine-tuning or Nebius Token Factory fine-tuning?\n\nFireworks AI Fine-tuning scores 59 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 6 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency \u0026 trust.\n\n### Do Fireworks AI Fine-tuning and Nebius Token Factory fine-tuning need an API key?\n\nBoth need an API key.\n\n### Can an agent call Fireworks AI Fine-tuning and Nebius Token Factory fine-tuning without installing anything?\n\nYes. Fireworks AI Fine-tuning has a hosted endpoint at https://api.fireworks.ai and Nebius Token Factory fine-tuning at https://api.tokenfactory.nebius.com/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"fireworks-fine-tuning\", \"b\": \"nebius-token-factory-fine-tuning\"}`. From a terminal: `anchor compare fireworks-fine-tuning nebius-token-factory-fine-tuning`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json and https://www.anchorterminal.com/api/v1/tools/nebius-token-factory-fine-tuning.json\n\n## Other comparisons with Fireworks AI Fine-tuning or Nebius Token Factory fine-tuning\n\n- [Amazon Bedrock model customisation vs Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning.md)\n- [Amazon Bedrock model customisation vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-nebius-token-factory-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- [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 Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-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 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- [Nebius Token Factory fine-tuning vs Tinker](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-tinker.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- [Nebius Token Factory fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-unsloth.md)\n- [Nebius Token Factory fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Fireworks AI Fine-tuning vs Nebius Token Factory fine-tuning", "url": "" } ], "description": "Fireworks AI Fine-tuning scores 59 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 6 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency \u0026 trust. Both do finetune sft. Category scores, facts, verdicts and agent…", "facts": [ "Fireworks AI Fine-tuning C 59", "Nebius Token Factory fine-tuning D 47.7", "scores" ], "h1": "Fireworks AI Fine-tuning vs Nebius Token Factory fine-tuning", "image": "https://www.anchorterminal.com/assets/og/compare-fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.png", "path": "/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning", "published": "2026-10-01", "section": "tools", "title": "Fireworks AI Fine-tuning vs Nebius Token Factory fine-tuning", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning" }, "tokens": { "markdown": 2550, "slim": 730 }, "version": 1 }