{ "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": "Vertex AI Gemini tuning scores 64.2 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 6 of 7 scored categories.", "b": { "slug": "vertex-ai-tuning", "name": "Vertex AI Gemini tuning", "vendor": "Google Cloud", "vendorUrl": "https://cloud.google.com", "kind": "http-api", "category": "fine-tuning", "summary": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen, on Google Cloud's Gemini Enterprise Agent Platform (the platform formerly called Vertex AI).", "url": "https://www.anchorterminal.com/tools/vertex-ai-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json", "repo": "https://github.com/googleapis/python-genai", "license": "Apache-2.0 (SDK)", "transports": [ "http" ], "remoteUrl": "https://us-central1-aiplatform.googleapis.com/v1", "packages": [ { "registry": "pypi", "name": "google-genai" } ], "auth": "oauth", "authNotes": "OAuth 2.0 bearer token from a service account or `gcloud auth print-access-token` on a project with billing and the platform API turned on. Training data comes from a Cloud Storage URI, so the caller also needs read access to the bucket. Tuning is a Vertex-only feature. The SDK says tuning is supported only on the enterprise platform, not the Gemini Developer API.", "pricing": "usage", "pricingNotes": "Per training token, where training tokens = dataset tokens x epochs. Gemini 3.5 Flash $10 per 1M for supervised or reinforcement learning fine-tuning (listed as $0.01 per 1,000), Gemini 3.1 Flash Lite $3, Gemini 2.5 Pro $25, Gemini 2.5 Flash $5 for supervised or preference tuning, Gemini 2.5 Flash Lite $1.50. Open models run from Gemma 3 at $0.47 (1B) to $6.83 (27B), Llama 3.1 8B $0.67, Llama 3.3 70B $6.72, Llama 4 Scout $5.77, Qwen 3 4B $1.35 to Qwen 3 32B $6.57. From Gemini 3 on, a tuned model endpoint costs 1.5x the base model's prediction price; older Gemini tuned models cost the same as base (https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 3900, "npmWeekly": null, "pypiWeekly": 32928433, "asOf": "2026-09-30" }, "docsUrl": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/tuning", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.rl", "finetune.lora" ], "tags": [ "hosted", "usage-priced", "closed-source", "card-required", "enterprise", "python", "async-jobs" ], "lastRelease": "2026-10-01", "graded": true, "anchor": { "graded": true, "score": 64.2, "grade": "B", "agentReady": false, "rank": 325, "ranked": true, "rankOf": 842, "categoryRank": 3, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 48, "maintenance": 80, "payments": 20, "reliability": 67, "schema": 82, "security": 71, "transparency": 88 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.", "bestFor": "Teams already on Google Cloud who need to tune Gemini itself, especially with RL, and will serve it there.", "strengths": [ "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen", "No Vertex or Gemini incidents on the Google Cloud status dashboard from July to September 2026", "Public proto for GenAiTuningService with filter and pagination on job lists, and docs pages served as Markdown at `.md.txt`", "Google says it won't train or fine-tune on customer data without permission, and a dated model lifecycle table promises 12 months from release", "ISO 27001, 27017 and 27018 and SOC 1, 2 and 3 cover Gemini Enterprise Agent Platform, and the subprocessor list gives locations" ], "weaknesses": [ "No weight export. The tuned model exists only as a Google Cloud endpoint", "Tuned Gemini 3 inference costs 1.5x the base model for as long as you serve it", "Setup needs a project, billing, IAM and a Cloud Storage bucket before the first job", "RL tuning is Pre-GA on v1beta1, and the SDK's `tunings.tune()` is marked experimental", "Gemini 2.5 Pro, Flash and Flash-Lite retire on 20 October 2026, and the docs don't say what happens to their tunes" ], "agentNotes": [ "Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API", "Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown", "Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20", "List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it", "Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.2 } ], "editorialScores": { "ergonomics": 48, "maintenance": 80, "payments": 20, "reliability": 67, "schema": 82, "security": 71, "transparency": 78 }, "provenanceScore": 97 }, "connect": { "install": "pip install google-genai", "http": "curl -X POST \"https://us-central1-aiplatform.googleapis.com/v1/projects/$GOOGLE_CLOUD_PROJECT/locations/us-central1/tuningJobs\" \\\n -H \"Authorization: Bearer $(gcloud auth print-access-token)\" -H \"content-type: application/json\" \\\n -d '{\"baseModel\":\"gemini-3.5-flash\",\"supervisedTuningSpec\":{\"trainingDatasetUri\":\"gs://my-bucket/train.jsonl\",\"hyperParameters\":{\"epochCount\":3,\"adapterSize\":\"ADAPTER_SIZE_FOUR\"}},\"tunedModelDisplayName\":\"my-tune\"}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/vertex-ai-tuning" }, "sameCompany": [ "gemini-api", "gemini-embedding", "google-model-armor", "google-imagen", "google-veo", "google-lyria", "google-speech-to-text", "gemini-live", "google-adk", "google-secret-manager", "google-weather-api", "chrome-devtools-mcp", "google-maps-platform", "google-cloud-translation", "google-calendar-api", "firebase-cloud-messaging", "google-drive-api", "gemini-cli", "google-search-console", "google-ads-api", "google-forms", "google-sheets-api", "gmail-api" ], "area": "models", "unitPrices": [ { "item": "Gemini 3.5 Flash, supervised tuning", "unit": "1m-tokens", "usd": 10 }, { "item": "Gemini 3.5 Flash, reinforcement learning fine-tuning", "unit": "1m-tokens", "usd": 10 }, { "item": "Gemini 3.1 Flash Lite, supervised tuning", "unit": "1m-tokens", "usd": 3 }, { "item": "Gemini 2.5 Pro, supervised tuning", "unit": "1m-tokens", "usd": 25 }, { "item": "Gemini 2.5 Flash, supervised or preference tuning", "unit": "1m-tokens", "usd": 5 }, { "item": "Gemini 2.5 Flash Lite, supervised or preference tuning", "unit": "1m-tokens", "usd": 1.5 }, { "item": "Gemma 3 27B IT, supervised tuning", "unit": "1m-tokens", "usd": 6.83 }, { "item": "Llama 3.3 70B, supervised tuning", "unit": "1m-tokens", "usd": 6.72 }, { "item": "Qwen 3 32B, supervised tuning", "unit": "1m-tokens", "usd": 6.57 } ], "provenance": { "legalEntity": "Google LLC", "domain": "google.com", "domainRegistered": "1997-09-15", "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.", "endpointOnVendorDomain": true, "terms": "https://cloud.google.com/terms", "privacy": "https://policies.google.com/privacy", "statusPage": "https://status.cloud.google.com", "changelog": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/release-notes", "securityTxt": "valid", "checked": "2026-09-30", "notes": [ "Entity, domain and security.txt are the same as the gemini-api listing, which uses the same Google privacy policy. The terms differ: this product runs under the Google Cloud Platform terms, whose contracting entity is set per billing country at cloud.google.com/terms/google-entity.", "The docs site serves navigation first and truncates the article body for a text fetcher, so the supported-model list, dataset limits and the checkpoint export page couldn't be read. Model and price facts come from the pricing page and the google-genai source.", "The old Vertex AI pricing page at cloud.google.com/vertex-ai/generative-ai/pricing still serves, but its tuning table stops at Gemini 2.5; the Gemini Enterprise Agent Platform pricing page has the Gemini 3 rows." ], "score": 97 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.json", "live": { "slug": "vertex-ai-tuning", "probe": { "target": "https://us-central1-aiplatform.googleapis.com/v1", "method": "get", "lastAt": "2026-10-09T11:00:41.506879544Z", "lastOk": true, "lastStatus": 404, "lastMs": 609, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 601, "p95ms24h": 675, "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 } ] }, "versions": [ { "registry": "github", "name": "googleapis/python-genai", "version": "v2.29.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:34:14.536586178Z" }, { "registry": "pypi", "name": "google-genai", "version": "2.29.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:34:14.345198998Z" } ], "githubStars": 4009, "pypiWeekly": 34128301, "securityTxt": { "url": "https://google.com/.well-known/security.txt", "state": "valid", "expires": "2030-04-01T00:00:00z", "checkedAt": "2026-10-08T15:38:39.75078566Z" }, "domain": { "domain": "google.com", "registered": "1997-09-15", "source": "https://rdap.verisign.com/com/v1/domain/google.com", "checkedAt": "2026-10-04T13:05:50.737985829Z" }, "pages": [ { "url": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/release-notes", "kind": "changelog", "status": 200, "checkedAt": "2026-10-08T18:18:25.711138322Z", "changedAt": "2026-10-08T18:18:25.711138322Z", "fingerprint": "5ca95b459af7" }, { "url": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:16:15.480038421Z", "changedAt": "2026-10-08T18:16:15.480038421Z", "fingerprint": "9f07a469a718" } ], "updatedAt": "2026-10-09T11:00:41.506879544Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Nebius", "b": "Google Cloud", "name": "Vendor" }, { "a": "https://api.tokenfactory.nebius.com/v1", "b": "https://us-central1-aiplatform.googleapis.com/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "OAuth", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary service (cookbook examples MIT)", "b": "Apache-2.0 (SDK)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-09-30", "b": "2026-10-01", "name": "Last release" }, { "a": "2026-09-28", "b": "2026-09-02", "name": "Terms last updated" }, { "a": "2026-09-23", "b": "2026-10-01", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "yes", "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": "not found in the text", "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": "3.9k stars, 32.9M PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "2.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Vertex AI Gemini tuning scores 64.2 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 6 of 7 scored categories.", "question": "Which is better for AI agents, Nebius Token Factory fine-tuning or Vertex AI Gemini tuning?" }, { "answer": "Nebius Token Factory fine-tuning needs an API key. Vertex AI Gemini tuning uses an OAuth sign-in.", "question": "Do Nebius Token Factory fine-tuning and Vertex AI Gemini tuning need an API key?" }, { "answer": "Yes. Nebius Token Factory fine-tuning has a hosted endpoint at https://api.tokenfactory.nebius.com/v1 and Vertex AI Gemini tuning at https://us-central1-aiplatform.googleapis.com/v1.", "question": "Can an agent call Nebius Token Factory fine-tuning and Vertex AI Gemini tuning without installing anything?" } ], "goodFor": [ { "aheadOn": null, "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, 67 against 45", "Schema \u0026 documentation, 82 against 68", "Security \u0026 auth, 71 against 52", "Payments \u0026 pricing, 20 against 0", "Maintenance \u0026 community, 80 against 61", "Transparency \u0026 trust, 88 against 79" ], "also": null, "goodFor": "Teams already on Google Cloud who need to tune Gemini itself, especially with RL, and will serve it there.", "slug": "vertex-ai-tuning", "watchFor": "No weight export. The tuned model exists only as a Google Cloud endpoint" } ], "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-vertex-ai-tuning.json", "title": "Amazon Bedrock model customisation vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-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-vertex-ai-tuning.json", "title": "Axolotl vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-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-vertex-ai-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-vertex-ai-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-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/tinker-vs-vertex-ai-tuning.json", "title": "Tinker vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning.json", "title": "Unsloth vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning" } ], "scores": [ { "by": 22, "edge": "vertex-ai-tuning", "key": "reliability", "name": "Reliability", "nebius-token-factory-fine-tuning": 45, "vertex-ai-tuning": 67, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 14, "edge": "vertex-ai-tuning", "key": "schema", "name": "Schema \u0026 documentation", "nebius-token-factory-fine-tuning": 68, "vertex-ai-tuning": 82, "weight": 13 }, { "by": 3, "edge": "nebius-token-factory-fine-tuning", "key": "ergonomics", "name": "Agent ergonomics", "nebius-token-factory-fine-tuning": 51, "vertex-ai-tuning": 48, "weight": 13 }, { "by": 19, "edge": "vertex-ai-tuning", "key": "security", "name": "Security \u0026 auth", "nebius-token-factory-fine-tuning": 52, "vertex-ai-tuning": 71, "weight": 14 }, { "by": 20, "edge": "vertex-ai-tuning", "key": "payments", "name": "Payments \u0026 pricing", "nebius-token-factory-fine-tuning": 0, "vertex-ai-tuning": 20, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 19, "edge": "vertex-ai-tuning", "key": "maintenance", "name": "Maintenance \u0026 community", "nebius-token-factory-fine-tuning": 61, "vertex-ai-tuning": 80, "weight": 7 }, { "by": 9, "edge": "vertex-ai-tuning", "key": "transparency", "name": "Transparency \u0026 trust", "nebius-token-factory-fine-tuning": 79, "vertex-ai-tuning": 88, "weight": 7 } ], "summary": "Vertex AI Gemini tuning scores 64.2 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 6 of 7 scored categories. 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.", "vertex-ai-tuning": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint." } }, "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-vertex-ai-tuning", "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.md", "slim": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.min.md" }, "markdown": "Vertex AI Gemini tuning scores 64.2 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 6 of 7 scored categories. 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- Vertex AI Gemini tuning: grade B, 64.2/100, rank #325 of 842. Markdown https://www.anchorterminal.com/tools/vertex-ai-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json\n\n## Which one, for what\n\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\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### Vertex AI Gemini tuning (B)\n\nGood for: Teams already on Google Cloud who need to tune Gemini itself, especially with RL, and will serve it there.\n\nAhead on:\n- Reliability, 67 against 45\n- Schema \u0026 documentation, 82 against 68\n- Security \u0026 auth, 71 against 52\n- Payments \u0026 pricing, 20 against 0\n- Maintenance \u0026 community, 80 against 61\n- Transparency \u0026 trust, 88 against 79\n\nWatch for: No weight export. The tuned model exists only as a Google Cloud endpoint\n\n\n## Score by category\n\n| Category | Weight | Nebius Token Factory fine-tuning | Vertex AI Gemini tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 45 | 67 | Vertex AI Gemini tuning +22 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 68 | 82 | Vertex AI Gemini tuning +14 |\n| Agent ergonomics | 13% (16.2 this run) | 51 | 48 | Nebius Token Factory fine-tuning +3 |\n| Security \u0026 auth | 14% (17.5 this run) | 52 | 71 | Vertex AI Gemini tuning +19 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 0 | 20 | Vertex AI Gemini 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 | Vertex AI Gemini tuning +19 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 79 | 88 | Vertex AI Gemini tuning +9 |\n| Negative events | ≤15 | -2 | 0 | |\n| **Total** | | **47.7 · D** | **64.2 · B** | |\n\n## Facts side by side\n\n| Fact | Nebius Token Factory fine-tuning | Vertex AI Gemini tuning |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Nebius | Google Cloud |\n| Hosted endpoint | `https://api.tokenfactory.nebius.com/v1` | `https://us-central1-aiplatform.googleapis.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | OAuth |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Proprietary service (cookbook examples MIT) | Apache-2.0 (SDK) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-30 | 2026-10-01 |\n| Terms last updated | 2026-09-28 | 2026-09-02 |\n| Privacy policy last updated | 2026-09-23 | 2026-10-01 |\n| Customer content may train models | not found in the text | yes |\n| Terms restrict automated access | not found in the text | not found in the text |\n| Terms restrict benchmarking | yes | not found in the text |\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 | 3.9k stars, 32.9M PyPI/wk |\n| Agent reviews | none | 2.5/5 (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**Vertex AI Gemini tuning.** Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.\n\n## Before you call either\n\n### 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### Vertex AI Gemini tuning\n\n1. Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API\n2. Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown\n3. Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20\n4. List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it\n5. Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes\n\n## Questions\n\n### Which is better for AI agents, Nebius Token Factory fine-tuning or Vertex AI Gemini tuning?\n\nVertex AI Gemini tuning scores 64.2 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 6 of 7 scored categories.\n\n### Do Nebius Token Factory fine-tuning and Vertex AI Gemini tuning need an API key?\n\nNebius Token Factory fine-tuning needs an API key. Vertex AI Gemini tuning uses an OAuth sign-in.\n\n### Can an agent call Nebius Token Factory fine-tuning and Vertex AI Gemini tuning without installing anything?\n\nYes. Nebius Token Factory fine-tuning has a hosted endpoint at https://api.tokenfactory.nebius.com/v1 and Vertex AI Gemini tuning at https://us-central1-aiplatform.googleapis.com/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nebius-token-factory-fine-tuning\", \"b\": \"vertex-ai-tuning\"}`. From a terminal: `anchor compare nebius-token-factory-fine-tuning vertex-ai-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/vertex-ai-tuning.json\n\n## Other comparisons with Nebius Token Factory fine-tuning or Vertex AI Gemini 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 Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-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 Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-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 Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-vertex-ai-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 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- [Tinker vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning.md)\n- [Together AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning.md)\n- [Unsloth vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning.md)\n", "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 Vertex AI Gemini tuning", "url": "" } ], "description": "Vertex AI Gemini tuning scores 64.2 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 6 of 7 scored categories. Both do finetune sft. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Nebius Token Factory fine-tuning D 47.7", "Vertex AI Gemini tuning B 64.2", "scores" ], "h1": "Nebius Token Factory fine-tuning vs Vertex AI Gemini tuning", "image": "https://www.anchorterminal.com/assets/og/compare-nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.png", "path": "/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning", "published": "2026-10-01", "section": "tools", "title": "Nebius Token Factory fine-tuning vs Vertex AI Gemini tuning", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning" }, "tokens": { "markdown": 2500, "slim": 680 }, "version": 1 }