{ "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": "2026-10-04T23:48:08.531865249Z", "lastOk": true, "lastStatus": 404, "lastMs": 26, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 29, "p95ms24h": 62, "samples24h": 272, "samples30d": 898, "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": 270, "ok": 270 } ] }, "vendorStatus": { "page": "https://status.fireworks.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-04T23:49:14.717658716Z" }, "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": "2026-10-04T23:49:14.717658716Z" } }, "b": { "slug": "unsloth", "name": "Unsloth", "vendor": "Unsloth", "vendorUrl": "https://unsloth.ai", "kind": "framework", "category": "fine-tuning", "summary": "Open-source library, web UI (Studio) and desktop app for LoRA, QLoRA, full fine-tuning and RL (GRPO, DPO, ORPO) of open models on your own GPU, from 3 GB of VRAM.", "url": "https://www.anchorterminal.com/tools/unsloth", "markdownUrl": "https://www.anchorterminal.com/tools/unsloth.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/unsloth.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/unsloth.json", "repo": "https://github.com/unslothai/unsloth", "license": "Apache-2.0 (core), AGPL-3.0 (Studio UI)", "transports": [], "packages": [ { "registry": "pypi", "name": "unsloth" } ], "auth": "none", "authNotes": "No account. Studio asks for an admin password when exposed beyond loopback (`--secure`, `--cloudflare` or a non-loopback host), and hands out API keys for its OpenAI-compatible server under Settings.", "pricing": "free", "pricingNotes": "Free and open source. You pay for the GPU it runs on, whether a free Colab or Kaggle notebook, your own card or a rented one. Docker images `unsloth/unsloth` and `unsloth/unsloth-rocm` on Docker Hub. No hosted plan or price list appears on the site or in the docs index (https://unsloth.ai/docs).", "priceSummary": "Free · OSS", "where": "library", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 76900, "npmWeekly": null, "pypiWeekly": 230075, "asOf": "2026-09-30" }, "docsUrl": "https://unsloth.ai/docs", "llmsTxt": "https://unsloth.ai/docs/llms.txt", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.rl", "finetune.lora", "finetune.export" ], "tags": [ "open-source", "framework", "self-hosted", "local", "free", "python", "llms-txt", "open-weights" ], "lastRelease": "2026-09-28", "graded": true, "anchor": { "graded": true, "score": 51.7, "grade": "D", "agentReady": false, "rank": 347, "ranked": true, "rankOf": 452, "categoryRank": 5, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 53, "maintenance": 82, "payments": 60, "reliability": 43, "schema": 66, "security": 35, "transparency": 34 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.", "strengths": [ "Free and open source, Apache-2.0 core, with the weights staying on your hardware", "LoRA, QLoRA, full fine-tuning, GRPO, DPO and ORPO from one package", "Exports adapters, merged 16-bit weights and GGUF for vLLM, Ollama or llama.cpp", "Fifteen PyPI releases between 25 August and 28 September 2026", "llms.txt and over 100 model-specific notebooks" ], "weaknesses": [ "Not a hosted service; you bring and pay for the GPU", "Studio is AGPL-3.0, and its server-side tools are on by default when exposed", "792 open issues and 472 open pull requests", "No legal entity in the terms, no privacy page and no security.txt", "Calendar versions with no breaking-change notes and no deprecation policy" ], "agentNotes": [ "Install with `uv pip install unsloth --torch-backend=auto` on a CUDA machine; the desktop app is for people", "Start from the notebook for the model family in unslothai/notebooks; it sets LoRA targets and the chat template", "Save the LoRA adapter while iterating and merge to 16-bit or GGUF only when you ship", "If Studio must be reachable by other agents, pass --disable-tools and keep it on 127.0.0.1 behind a tunnel", "Pin the exact unsloth version; releases land several times a week and don't flag breaking changes" ], "metrics": { "kind": "local", "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": 51.7 } ], "editorialScores": { "ergonomics": 53, "maintenance": 82, "payments": 60, "reliability": 43, "schema": 66, "security": 35, "transparency": 40 }, "provenanceScore": 27 }, "connect": { "install": "curl -fsSL https://unsloth.ai/install.sh | sh # or: uv pip install unsloth --torch-backend=auto" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/unsloth" }, "area": "models", "provenance": { "legalEntity": "", "domain": "unsloth.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://unsloth.ai/terms", "privacy": "", "statusPage": "", "changelog": "https://github.com/unslothai/unsloth/releases", "securityTxt": "none", "checked": "2026-09-30", "notes": [ "The terms page names no company, address or date, and unsloth.ai/privacy returns 404. Copyright notices in the source credit Daniel Han-Chen and the Unsloth team.", "A local library has no endpoint to check against the domain.", "unsloth.ai/.well-known/security.txt returns 404.", "The .ai registry's RDAP server refused our requests, so the registration date is blank.", "lastRelease is blank because releases are versioned by date (2026.9.12) and we didn't confirm the tag date; the last commit was 2026-09-30." ], "score": 27 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/unsloth.json", "live": { "slug": "unsloth", "versions": [ { "registry": "github", "name": "unslothai/unsloth", "version": "v0.1.902-beta", "released": "2026-10-01", "seenAt": "2026-10-04T16:42:47.22948531Z" }, { "registry": "pypi", "name": "unsloth", "version": "2026.9.14", "released": "2026-10-01", "seenAt": "2026-10-04T16:42:47.034258052Z" } ], "githubStars": 77198, "pypiWeekly": 198310, "securityTxt": { "url": "https://unsloth.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-04T15:15:40.917718435Z" }, "llmsTxt": { "url": "https://unsloth.ai/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:18:19.343297803Z" }, "domain": { "domain": "unsloth.ai", "registered": "2023-11-27", "source": "https://rdap.identitydigital.services/rdap/domain/unsloth.ai", "checkedAt": "2026-10-04T13:08:02.898044488Z" }, "pages": [ { "url": "https://unsloth.ai/terms", "kind": "terms", "status": 404, "checkedAt": "2026-10-04T15:48:37.812526194Z", "changedAt": "0001-01-01T00:00:00Z" } ], "updatedAt": "2026-10-04T16:42:47.22948531Z" } }, "summary": "Fireworks AI Fine-tuning has a score of 59.2 (C) against Unsloth's 51.7 (D). Both do finetune sft. The largest gap is payments \u0026 pricing, 35 points." }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth", "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.md", "slim": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.min.md" }, "markdown": "Fireworks AI Fine-tuning has a score of 59.2 (C) against Unsloth's 51.7 (D). Both do finetune sft. The largest gap is payments \u0026 pricing, 35 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- Unsloth: grade D, 51.7/100, rank #347 of 452. Markdown https://www.anchorterminal.com/tools/unsloth.md · JSON https://www.anchorterminal.com/api/v1/tools/unsloth.json\n\n## Which one, for what\n\nPick Fireworks AI Fine-tuning for reliability (+12), schema \u0026 documentation (+11), agent ergonomics (+22), security \u0026 auth (+30), transparency \u0026 trust (+32).\n\nPick Unsloth for payments \u0026 pricing (+35).\n\n## Score by category\n\n| Category | Weight | Fireworks AI Fine-tuning | Unsloth | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 55 | 43 | Fireworks AI Fine-tuning +12 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 77 | 66 | Fireworks AI Fine-tuning +11 |\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 | 35 | Fireworks AI Fine-tuning +30 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 25 | 60 | Unsloth +35 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 82 | 82 | even |\n| Transparency \u0026 trust | 7% (8.8 this run) | 66 | 34 | Fireworks AI Fine-tuning +32 |\n| Negative events | ≤15 | -4 | 0 | |\n| **Total** | | **59.2 · C** | **51.7 · D** | |\n\n## Facts side by side\n\n| Fact | Fireworks AI Fine-tuning | Unsloth |\n| --- | --- | --- |\n| Kind | HTTP API | Agent framework |\n| Vendor | Fireworks AI | Unsloth |\n| Hosted endpoint | `https://api.fireworks.ai` | no (local only) |\n| Transports | HTTP | |\n| Auth | API key | None |\n| Pricing | Pay per use | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDK) | Apache-2.0 (core), AGPL-3.0 (Studio UI) |\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 | 2026-09-28 |\n| Popularity | 7 stars, 290k PyPI/wk | 77k stars, 230k 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**Unsloth.** The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.\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### Unsloth\n\n1. Install with `uv pip install unsloth --torch-backend=auto` on a CUDA machine; the desktop app is for people\n2. Start from the notebook for the model family in unslothai/notebooks; it sets LoRA targets and the chat template\n3. Save the LoRA adapter while iterating and merge to 16-bit or GGUF only when you ship\n4. If Studio must be reachable by other agents, pass --disable-tools and keep it on 127.0.0.1 behind a tunnel\n5. Pin the exact unsloth version; releases land several times a week and don't flag breaking changes\n\n## Other comparisons with Fireworks AI Fine-tuning or Unsloth\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 Unsloth](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-unsloth.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 Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.md)\n- [Tinker vs Unsloth](https://www.anchorterminal.com/compare/tinker-vs-unsloth.md)\n- [Together AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.md)\n- [Unsloth vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-04", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.3", "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 Unsloth", "url": "" } ], "description": "Fireworks AI Fine-tuning has a score of 59.2 (C) against Unsloth's 51.7 (D). Both do finetune sft. The largest gap is payments \u0026 pricing, 35 points. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Fireworks AI Fine-tuning C 59.2", "Unsloth D 51.7", "scores" ], "h1": "Fireworks AI Fine-tuning vs Unsloth", "image": "https://www.anchorterminal.com/assets/og/compare-fireworks-fine-tuning-vs-unsloth.png", "path": "/compare/fireworks-fine-tuning-vs-unsloth", "published": "2026-10-01", "section": "tools", "title": "Fireworks AI Fine-tuning vs Unsloth for AI agents, C 59.2 vs D 51.7", "toc": null, "updated": "2026-10-04", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth" }, "tokens": { "markdown": 1450, "slim": 330 }, "version": 1 }