{ "data": { "a": { "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": "2026-10-09T11:00:30.72469802Z", "lastOk": true, "lastStatus": 404, "lastMs": 87, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 87, "p95ms24h": 172, "samples24h": 36, "samples30d": 36, "days": [ { "date": "2026-10-09", "probes": 36, "ok": 36 } ] }, "vendorStatus": { "page": "https://status.nebius.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-09T11:03:49.658305196Z" }, "updatedAt": "2026-10-09T11:03:49.658305196Z" } }, "answer": "Together AI Fine-tuning scores 54.7 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 4 of 7 scored categories. Nebius Token Factory fine-tuning leads on agent ergonomics and transparency \u0026 trust.", "b": { "slug": "together-fine-tuning", "name": "Together AI Fine-tuning", "vendor": "Together AI", "vendorUrl": "https://www.together.ai", "kind": "http-api", "category": "fine-tuning", "summary": "Managed LoRA and full fine-tuning, supervised or DPO, on about 30 open models from Qwen3.5 0.8B to Kimi K2.7, billed per training token with a $4 minimum.", "url": "https://www.anchorterminal.com/tools/together-fine-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json", "repo": "https://github.com/togethercomputer/together-py", "license": "Apache-2.0 (SDKs)", "transports": [ "http" ], "remoteUrl": "https://api.together.ai/v1", "packages": [ { "registry": "pypi", "name": "together" }, { "registry": "npm", "name": "together-ai" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with the key from the console, read from `TOGETHER_API_KEY` by the SDKs and the `tg` CLI. One key covers files, fine-tuning jobs, downloads and endpoints.", "pricing": "usage", "pricingNotes": "Per training token, where tokens = epochs x training tokens + evaluations x validation tokens. LoRA SFT from $0.34 per 1M (Llama 3.1 8B, Qwen3.5 9B) through $1.05 (Qwen3.8 27B), $2.03 (Llama 3.3 70B), $2.50 (gpt-oss-120b), $7 (DeepSeek V3.1) and $15 (Kimi K2.6) to $40 (GLM-5.2). DPO is 2.5x the SFT rate ($0.84 for Llama 3.1 8B, $37.50 for Kimi K2.6). Full fine-tuning $0.38 (8B and 9B models) to $2.24 (Llama 3.3 70B). Minimum $4 a job, rising to $6 for gpt-oss-120b, $20 for DeepSeek V3.1 and $60 for Kimi K2.6. Hosting the result needs a dedicated endpoint; the pricing page lists dedicated endpoint GPUs at $5.49 an hour for an H100 and $8.99 for a B200, with H200 and B300 by quote. No free trial; access needs a $5 prepaid credit purchase (https://www.together.ai/pricing, https://docs.together.ai/docs/billing-credits).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 10, "npmWeekly": 117852, "pypiWeekly": 369054, "asOf": "2026-09-30" }, "docsUrl": "https://docs.together.ai/docs/fine-tuning/overview", "llmsTxt": "https://docs.together.ai/llms.txt", "openapi": "https://docs.together.ai/openapi.yaml", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.lora", "finetune.export" ], "tags": [ "hosted", "usage-priced", "card-required", "open-weights", "llms-txt", "python", "typescript", "async-jobs" ], "lastRelease": "2026-09-30", "graded": true, "anchor": { "graded": true, "score": 54.7, "grade": "C", "agentReady": false, "rank": 608, "ranked": true, "rankOf": 842, "categoryRank": 6, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 42, "maintenance": 80, "payments": 20, "reliability": 55, "schema": 78, "security": 50, "transparency": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour.", "bestFor": "Teams that want to tune a large open model, possibly with full fine-tuning, and take the weights away.", "strengths": [ "31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA", "GET /v1/finetune/download returns merged weights or the adapter, at any saved checkpoint", "POST /v1/fine-tunes/estimate-price quotes a job before it runs", "Project-scoped API keys with expiry dates from 1 hour", "Python and TypeScript SDKs, an OpenAPI file and llms.txt" ], "weaknesses": [ "Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour", "No free trial, a $5 prepaid purchase before the first call, and job minimums up to $60", "The status page covers serverless models only, and no fine-tuning rate limits are published", "No pagination on the job list and no documented error responses for fine-tuning calls", "No read-only project role and no audit log found" ], "agentNotes": [ "Call POST /v1/fine-tunes/estimate-price with the same body before creating the job, and check the model's minimum charge", "Read `lora_training.max_rank` from the model limits response before setting `lora_r`; most models went to 128 on 2026-09-29", "Don't retry a create call blindly after a timeout; there's no idempotency key, so list jobs and check first", "Download with checkpoint=adapter if you'll merge locally; merged weights for a 70B model are a large stream", "Tear down the dedicated endpoint once evaluation ends, since it bills while idle" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54.7 } ], "editorialScores": { "ergonomics": 42, "maintenance": 80, "payments": 20, "reliability": 55, "schema": 78, "security": 50, "transparency": 55 }, "provenanceScore": 81 }, "connect": { "install": "pip install together # or: npm i together-ai", "http": "curl https://api.together.ai/v1/fine-tunes \\\n -H \"Authorization: Bearer $TOGETHER_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"Qwen/Qwen3.5-9B\",\"training_file\":\"file-abc123\",\"n_epochs\":3,\"training_type\":{\"type\":\"Lora\",\"lora_r\":16,\"lora_alpha\":32},\"training_method\":{\"method\":\"sft\"},\"suffix\":\"my-run\"}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/together-fine-tuning" }, "sameCompany": [ "together-code-sandbox" ], "area": "models", "unitPrices": [ { "item": "LoRA SFT, Llama 3.1 8B", "unit": "1m-tokens", "usd": 0.34, "note": "Same rate for Qwen3.5 9B. $4 minimum" }, { "item": "LoRA DPO, Llama 3.1 8B", "unit": "1m-tokens", "usd": 0.84 }, { "item": "Full SFT, Llama 3.1 8B", "unit": "1m-tokens", "usd": 0.38 }, { "item": "LoRA SFT, Qwen3.8 27B", "unit": "1m-tokens", "usd": 1.05 }, { "item": "LoRA SFT, Llama 3.3 70B", "unit": "1m-tokens", "usd": 2.03, "note": "Full SFT $2.24" }, { "item": "LoRA SFT, gpt-oss-120b", "unit": "1m-tokens", "usd": 2.5, "note": "$6 minimum" }, { "item": "LoRA SFT, DeepSeek V3.1", "unit": "1m-tokens", "usd": 7, "note": "$20 minimum" }, { "item": "LoRA SFT, Kimi K2.6", "unit": "1m-tokens", "usd": 15, "note": "$60 minimum" }, { "item": "H100 on demand", "unit": "gpu-hour", "usd": 3.99 }, { "item": "H200 on demand", "unit": "gpu-hour", "usd": 5.99 }, { "item": "B200 on demand", "unit": "gpu-hour", "usd": 8.19 } ], "provenance": { "legalEntity": "Together Computer, Inc.", "domain": "together.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://www.together.ai/terms-of-service", "privacy": "https://www.together.ai/privacy", "statusPage": "https://status.together.ai", "changelog": "https://docs.together.ai/docs/changelog", "securityTxt": "valid", "checked": "2026-09-30", "notes": [ "The terms (2026-05-19) name Together Computer, Inc., a Delaware corporation. The privacy policy (2025-12-17) says data isn't used to train models without opt-in.", "security.txt points Contact and Policy at hackerone.com/together_ai and has no Expires field.", "The status page monitors serverless inference models only; there's no fine-tuning component.", "The .ai registry's RDAP server refused our requests, so the registration date is blank.", "The MCP registry has a third-party io.usefulapi/together-ai server that wraps fine-tunes; Together doesn't publish one." ], "score": 81 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.json", "live": { "slug": "together-fine-tuning", "probe": { "target": "https://api.together.ai/v1", "method": "get", "lastAt": "2026-10-09T11:00:39.9723159Z", "lastOk": true, "lastStatus": 404, "lastMs": 225, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 230, "p95ms24h": 525, "samples24h": 260, "samples30d": 2101, "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": 117, "ok": 117 } ] }, "vendorStatus": { "page": "https://status.together.ai", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-09T07:58:36.144917886Z" }, "versions": [ { "registry": "github", "name": "togethercomputer/together-py", "version": "v2.40.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:32:29.219602138Z" }, { "registry": "npm", "name": "together-ai", "version": "0.58.0", "seenAt": "2026-10-08T16:32:28.317335771Z" }, { "registry": "pypi", "name": "together", "version": "2.40.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:32:28.188942399Z" } ], "githubStars": 10, "npmWeekly": 120454, "pypiWeekly": 388333, "securityTxt": { "url": "https://together.ai/.well-known/security.txt", "state": "valid", "expires": "2028-09-30T00:00:00.000Z", "checkedAt": "2026-10-08T15:38:43.914614283Z" }, "llmsTxt": { "url": "https://docs.together.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:56.837586142Z" }, "domain": { "domain": "together.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/together.ai", "checkedAt": "2026-10-04T13:04:15.476837291Z" }, "pages": [ { "url": "https://docs.together.ai/docs/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-10-08T18:19:36.548210442Z", "changedAt": "2026-10-08T18:19:36.548210442Z", "fingerprint": "ac6616429c58" }, { "url": "https://www.together.ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:31:00.887281809Z", "changedAt": "2026-10-08T18:31:00.887281809Z", "fingerprint": "ccd78896d28a" }, { "url": "https://www.together.ai/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-08T18:31:03.071776633Z", "changedAt": "2026-10-08T18:31:03.071776633Z", "fingerprint": "ca24e4a2e8da" }, { "url": "https://www.together.ai/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:31:05.089468507Z", "changedAt": "2026-10-08T18:31:05.089468507Z", "fingerprint": "e55cb7869607" } ], "updatedAt": "2026-10-09T11:00:39.9723159Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Nebius", "b": "Together AI", "name": "Vendor" }, { "a": "https://api.tokenfactory.nebius.com/v1", "b": "https://api.together.ai/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": "not published", "b": "$0.34 per 1M tokens", "name": "Price for finetune sft" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary service (cookbook examples MIT)", "b": "Apache-2.0 (SDKs)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-09-30", "b": "2026-09-30", "name": "Last release" }, { "a": "2026-09-28", "b": "no date given", "name": "Terms last updated" }, { "a": "2026-09-23", "b": "no date given", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "yes", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "10 stars, 118k npm/wk, 369k PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "3/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Together AI Fine-tuning scores 54.7 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 4 of 7 scored categories. Nebius Token Factory fine-tuning leads on agent ergonomics and transparency \u0026 trust.", "question": "Which is better for AI agents, Nebius Token Factory fine-tuning or Together AI Fine-tuning?" }, { "answer": "Both need an API key.", "question": "Do Nebius Token Factory fine-tuning and Together AI Fine-tuning need an API key?" }, { "answer": "Yes. Nebius Token Factory fine-tuning has a hosted endpoint at https://api.tokenfactory.nebius.com/v1 and Together AI Fine-tuning at https://api.together.ai/v1.", "question": "Can an agent call Nebius Token Factory fine-tuning and Together AI Fine-tuning without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Agent ergonomics, 51 against 42", "Transparency \u0026 trust, 79 against 68" ], "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" }, { "aheadOn": [ "Reliability, 55 against 45", "Schema \u0026 documentation, 78 against 68", "Payments \u0026 pricing, 20 against 0", "Maintenance \u0026 community, 80 against 61" ], "also": null, "goodFor": "Teams that want to tune a large open model, possibly with full fine-tuning, and take the weights away.", "slug": "together-fine-tuning", "watchFor": "Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour" } ], "job": { "capability": "finetune.sft", "name": "Finetune sft" }, "others": [ { "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/amazon-bedrock-customization-vs-together-fine-tuning.json", "title": "Amazon Bedrock model customisation vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-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/axolotl-vs-together-fine-tuning.json", "title": "Axolotl vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/axolotl-vs-together-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/azure-foundry-fine-tuning-vs-together-fine-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.json", "title": "Fireworks AI Fine-tuning vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning" }, { "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/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-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" }, { "json": "https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.json", "title": "Tinker vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.json", "title": "Together AI Fine-tuning vs Unsloth", "url": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning.json", "title": "Together AI Fine-tuning vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning" } ], "scores": [ { "by": 10, "edge": "together-fine-tuning", "key": "reliability", "name": "Reliability", "nebius-token-factory-fine-tuning": 45, "together-fine-tuning": 55, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 10, "edge": "together-fine-tuning", "key": "schema", "name": "Schema \u0026 documentation", "nebius-token-factory-fine-tuning": 68, "together-fine-tuning": 78, "weight": 13 }, { "by": 9, "edge": "nebius-token-factory-fine-tuning", "key": "ergonomics", "name": "Agent ergonomics", "nebius-token-factory-fine-tuning": 51, "together-fine-tuning": 42, "weight": 13 }, { "by": 2, "edge": "nebius-token-factory-fine-tuning", "key": "security", "name": "Security \u0026 auth", "nebius-token-factory-fine-tuning": 52, "together-fine-tuning": 50, "weight": 14 }, { "by": 20, "edge": "together-fine-tuning", "key": "payments", "name": "Payments \u0026 pricing", "nebius-token-factory-fine-tuning": 0, "together-fine-tuning": 20, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 19, "edge": "together-fine-tuning", "key": "maintenance", "name": "Maintenance \u0026 community", "nebius-token-factory-fine-tuning": 61, "together-fine-tuning": 80, "weight": 7 }, { "by": 11, "edge": "nebius-token-factory-fine-tuning", "key": "transparency", "name": "Transparency \u0026 trust", "nebius-token-factory-fine-tuning": 79, "together-fine-tuning": 68, "weight": 7 } ], "summary": "Together AI Fine-tuning scores 54.7 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 4 of 7 scored categories. Nebius Token Factory fine-tuning leads on agent ergonomics and transparency \u0026 trust. Both do finetune sft.", "verdicts": { "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.", "together-fine-tuning": "31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "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-together-fine-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning.md", "slim": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning.min.md" }, "markdown": "Together AI Fine-tuning scores 54.7 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 4 of 7 scored categories. Nebius Token Factory fine-tuning leads on agent ergonomics and transparency \u0026 trust. Both do finetune sft.\n\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- Together AI Fine-tuning: grade C, 54.7/100, rank #608 of 842. Markdown https://www.anchorterminal.com/tools/together-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json\n\n## Which one, for what\n\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- Agent ergonomics, 51 against 42\n- Transparency \u0026 trust, 79 against 68\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### Together AI Fine-tuning (C)\n\nGood for: Teams that want to tune a large open model, possibly with full fine-tuning, and take the weights away.\n\nAhead on:\n- Reliability, 55 against 45\n- Schema \u0026 documentation, 78 against 68\n- Payments \u0026 pricing, 20 against 0\n- Maintenance \u0026 community, 80 against 61\n\nWatch for: Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour\n\n\n## Score by category\n\n| Category | Weight | Nebius Token Factory fine-tuning | Together AI Fine-tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 45 | 55 | Together AI Fine-tuning +10 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 68 | 78 | Together AI Fine-tuning +10 |\n| Agent ergonomics | 13% (16.2 this run) | 51 | 42 | Nebius Token Factory fine-tuning +9 |\n| Security \u0026 auth | 14% (17.5 this run) | 52 | 50 | Nebius Token Factory fine-tuning +2 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 0 | 20 | Together AI Fine-tuning +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 61 | 80 | Together AI Fine-tuning +19 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 79 | 68 | Nebius Token Factory fine-tuning +11 |\n| Negative events | ≤15 | -2 | 0 | |\n| **Total** | | **47.7 · D** | **54.7 · C** | |\n\n## Facts side by side\n\n| Fact | Nebius Token Factory fine-tuning | Together AI Fine-tuning |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Nebius | Together AI |\n| Hosted endpoint | `https://api.tokenfactory.nebius.com/v1` | `https://api.together.ai/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\n| Price for finetune sft | not published | $0.34 per 1M tokens |\n| x402 | no | no |\n| Licence | Proprietary service (cookbook examples MIT) | Apache-2.0 (SDKs) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-09-30 | 2026-09-30 |\n| Terms last updated | 2026-09-28 | no date given |\n| Privacy policy last updated | 2026-09-23 | no date given |\n| Customer content may train models | not found in the text | not found in the text |\n| Terms restrict automated access | not found in the text | not found in the text |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | yes | not found in the text |\n| Popularity | none | 10 stars, 118k npm/wk, 369k PyPI/wk |\n| Agent reviews | none | 3/5 (2) |\n\n## Verdicts\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**Together AI Fine-tuning.** 31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour.\n\n## Before you call either\n\n### 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### Together AI Fine-tuning\n\n1. Call POST /v1/fine-tunes/estimate-price with the same body before creating the job, and check the model's minimum charge\n2. Read `lora_training.max_rank` from the model limits response before setting `lora_r`; most models went to 128 on 2026-09-29\n3. Don't retry a create call blindly after a timeout; there's no idempotency key, so list jobs and check first\n4. Download with checkpoint=adapter if you'll merge locally; merged weights for a 70B model are a large stream\n5. Tear down the dedicated endpoint once evaluation ends, since it bills while idle\n\n## Questions\n\n### Which is better for AI agents, Nebius Token Factory fine-tuning or Together AI Fine-tuning?\n\nTogether AI Fine-tuning scores 54.7 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 4 of 7 scored categories. Nebius Token Factory fine-tuning leads on agent ergonomics and transparency \u0026 trust.\n\n### Do Nebius Token Factory fine-tuning and Together AI Fine-tuning need an API key?\n\nBoth need an API key.\n\n### Can an agent call Nebius Token Factory fine-tuning and Together AI Fine-tuning without installing anything?\n\nYes. Nebius Token Factory fine-tuning has a hosted endpoint at https://api.tokenfactory.nebius.com/v1 and Together AI Fine-tuning at https://api.together.ai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nebius-token-factory-fine-tuning\", \"b\": \"together-fine-tuning\"}`. From a terminal: `anchor compare nebius-token-factory-fine-tuning together-fine-tuning`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nebius-token-factory-fine-tuning.json and https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json\n\n## Other comparisons with Nebius Token Factory fine-tuning or Together AI Fine-tuning\n\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- [Amazon Bedrock model customisation vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-together-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- [Axolotl vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-together-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- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning.md)\n- [Fireworks AI Fine-tuning vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.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- [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 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- [Tinker vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.md)\n- [Together AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.md)\n- [Together AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning.md)\n", "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": "Nebius Token Factory fine-tuning vs Together AI Fine-tuning", "url": "" } ], "description": "Together AI Fine-tuning scores 54.7 (C) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 4 of 7 scored categories. Nebius Token Factory fine-tuning leads on agent ergonomics and transparency \u0026 trust. Both do finetune sft. Category scores…", "facts": [ "Nebius Token Factory fine-tuning D 47.7", "Together AI Fine-tuning C 54.7", "scores" ], "h1": "Nebius Token Factory fine-tuning vs Together AI Fine-tuning", "image": "https://www.anchorterminal.com/assets/og/compare-nebius-token-factory-fine-tuning-vs-together-fine-tuning.png", "path": "/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning", "published": "2026-10-01", "section": "tools", "title": "Nebius Token Factory fine-tuning vs Together AI Fine-tuning", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning" }, "tokens": { "markdown": 2550, "slim": 680 }, "version": 1 }