{ "data": { "a": { "slug": "azure-foundry-fine-tuning", "name": "Microsoft Foundry fine-tuning (Azure OpenAI)", "vendor": "Microsoft Azure", "vendorUrl": "https://azure.microsoft.com", "kind": "http-api", "category": "fine-tuning", "summary": "Azure's managed service for supervised, preference and reinforcement fine-tuning of supported OpenAI and open-weight models.", "url": "https://www.anchorterminal.com/tools/azure-foundry-fine-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/azure-foundry-fine-tuning.json", "transports": [ "http" ], "remoteUrl": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "packages": [ { "registry": "pypi", "name": "openai" }, { "registry": "npm", "name": "openai" } ], "auth": "mixed", "authNotes": "`api-key` header with a resource key, or a Microsoft Entra ID bearer token. Training a model needs the Foundry User role and deploying it needs Foundry Owner (renamed from Azure AI User and Azure AI Owner). Deployments are created through the Azure Resource Manager API at management.azure.com, a second credential.", "pricing": "usage", "pricingNotes": "SFT and DPO bill training tokens x epochs at a per-model rate. The Azure Retail Prices API lists, per 1M training tokens, gpt-4.1 at $25 global and $30.25 regional, gpt-4.1-mini at $5 and $6.05, and gpt-4.1-nano at $1.50 and $1.815 (regional is 21 per cent above global). RFT bills training hours plus grader tokens; the cost guide's example uses $100 an hour for o4-mini and jobs pause at $5,000. The developer tier is 50 per cent below global on pre-emptible capacity, without data residency. A fine-tuned model on a Standard or Global Standard deployment costs $1.70 an hour to host plus per-token inference (gpt-4.1-ft $2 input and $8 output per 1M, global); developer deployments have no hosting fee and are deleted after 24 hours (https://prices.azure.com/api/retail/prices, https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/fine-tuning-cost-management).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": 47155661, "pypiWeekly": 72103251, "asOf": "2026-09-30" }, "docsUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/fine-tuning", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.rl", "finetune.lora" ], "tags": [ "hosted", "usage-priced", "closed-source", "card-required", "enterprise", "eu", "python", "typescript", "async-jobs" ], "graded": true, "anchor": { "graded": true, "score": 61.4, "grade": "C", "agentReady": false, "rank": 228, "ranked": true, "rankOf": 452, "categoryRank": 2, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 47, "maintenance": 55, "payments": 20, "reliability": 65, "schema": 67, "security": 85, "transparency": 88 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "SFT, DPO and RFT on GPT-4.1 and o4-mini through the OpenAI-shaped /openai/v1 API. No weight export; checkpoints copy only between Azure resources.", "strengths": [ "SFT, DPO and RFT on GPT-4.1 and o4-mini through the OpenAI-shaped /openai/v1 API", "Retirement policy with 60 days' notice and published training and deployment retirement dates per tunable model", "Entra ID with RBAC, Azure Monitor logs and an activity log for every customer", "Training files and tuned models stay in the resource's geography, are deletable and exclusive to the customer", "Fine-tuning limits published with numbers, from 3 concurrent jobs to 2 billion tokens per job" ], "weaknesses": [ "No weight export; checkpoints copy only between Azure resources", "$1.70 an hour hosting on Standard deployments, and deletion after 15 idle days", "GPT-4.1 training at $25 per 1M tokens globally, and no free tier without a card", "Deployment goes through management.azure.com with a separate credential and the Foundry Owner role", "The Azure OpenAI 'what's new' page hasn't had a dated section since May 2026" ], "agentNotes": [ "Point the OpenAI SDK at https://\u003cresource\u003e.openai.azure.com/openai/v1 with the `api-key` header or an Entra token; job, file and checkpoint calls are the OpenAI shapes", "Read prices from the Azure Retail Prices API (meters named like 'gpt-4.1 FT Training global'), not the pricing page, which needs a browser", "Keep at most 3 jobs running and 20 queued per resource, and keep training files under 512 MB and 1 GB in total", "Create the deployment through the Resource Manager API with a Foundry Owner identity, then call it at least once a fortnight or it's deleted", "Query the Models API for `deprecationDate` before choosing a base model" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.4 } ], "editorialScores": { "ergonomics": 47, "maintenance": 55, "payments": 20, "reliability": 65, "schema": 67, "security": 85, "transparency": 81 }, "provenanceScore": 95 }, "connect": { "install": "pip install openai # or: npm i openai", "http": "curl \"https://$AZURE_OPENAI_RESOURCE.openai.azure.com/openai/v1/fine_tuning/jobs\" \\\n -H \"api-key: $AZURE_OPENAI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"gpt-4.1-2025-04-14\",\"training_file\":\"file-abc123\",\"seed\":105}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/azure-foundry-fine-tuning" }, "sameCompany": [ "azure-ai-content-safety", "azure-speech-to-text", "azure-text-to-speech", "microsoft-learn-mcp", "playwright-mcp", "azure-mcp", "azure-translator", "microsoft-graph-calendar" ], "area": "models", "provenance": { "legalEntity": "Microsoft Corporation", "domain": "microsoft.com", "domainRegistered": "1991-05-02", "domainNote": "Endpoints are on openai.azure.com and management.azure.com. microsoft.com publishes a security.txt, but it passed its Expires date on 2026-09-23.", "endpointOnVendorDomain": true, "terms": "https://www.microsoft.com/licensing/terms/product/ForOnlineServices/all", "privacy": "https://privacy.microsoft.com/en-us/privacystatement", "statusPage": "https://azure.status.microsoft/en-us/status", "changelog": "https://learn.microsoft.com/en-us/azure/ai-foundry/whats-new-foundry", "securityTxt": "expired", "checked": "2026-09-30", "notes": [ "Entity, domain, privacy statement, status page and security.txt are the same as the azure-speech-to-text listing; the terms link here is the Product Terms for online services, which hold the generative AI clause.", "The Azure status page lists Azure OpenAI Service, Foundry Agent Service and Foundry Models as components.", "The npm and PyPI figures are for the openai package as a whole, which Azure customers share with OpenAI's own API; there's no Azure-only SDK to count.", "Docs facts were read from the MicrosoftDocs/azure-ai-docs repository (articles/foundry/openai, updated 2026-09-30) because Learn pages are long; the live how-to page confirms the model table and roles." ], "score": 95 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.json", "live": { "slug": "azure-foundry-fine-tuning", "probe": { "target": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "method": "get", "lastAt": "2026-10-04T23:06:07.144001602Z", "lastOk": false, "lastStatus": 0, "lastMs": 0, "lastNote": "DNS lookup failed", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 272, "samples30d": 890, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 0 }, { "date": "2026-10-02", "probes": 248, "ok": 0 }, { "date": "2026-10-03", "probes": 271, "ok": 0 }, { "date": "2026-10-04", "probes": 262, "ok": 0 } ] }, "vendorStatus": { "page": "https://azure.status.microsoft/en-us/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-04T21:39:49.453465033Z" }, "versions": [ { "registry": "npm", "name": "openai", "version": "7.27.0", "seenAt": "2026-10-04T16:21:30.502857331Z" }, { "registry": "pypi", "name": "openai", "version": "3.24.0", "released": "2026-10-02", "seenAt": "2026-10-04T16:21:30.317969684Z" } ], "npmWeekly": 50351921, "pypiWeekly": 72574929, "securityTxt": { "url": "https://microsoft.com/.well-known/security.txt", "state": "expired", "expires": "2026-09-23T16:00:00.000Z", "checkedAt": "2026-10-04T15:16:01.36832038Z" }, "domain": { "domain": "microsoft.com", "registered": "1991-05-02", "source": "https://rdap.verisign.com/com/v1/domain/microsoft.com", "checkedAt": "2026-10-04T13:04:13.488857536Z" }, "pages": [ { "url": "https://learn.microsoft.com/en-us/azure/ai-foundry/whats-new-foundry", "kind": "changelog", "status": 304, "checkedAt": "2026-10-04T15:45:24.699524258Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "237edf8365ef" }, { "url": "https://prices.azure.com/api/retail/prices", "kind": "pricing", "status": 200, "checkedAt": "2026-10-04T15:46:58.974430601Z", "changedAt": "0001-01-01T00:00:00Z" }, { "url": "https://www.microsoft.com/licensing/terms/product/ForOnlineServices/all", "kind": "terms", "status": 502, "checkedAt": "2026-10-04T15:51:24.287211028Z", "changedAt": "0001-01-01T00:00:00Z" } ], "updatedAt": "2026-10-04T23:06:07.144001602Z" } }, "b": { "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": "2026-10-04T23:06:11.247920609Z", "lastOk": true, "lastStatus": 404, "lastMs": 28, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 29, "p95ms24h": 65, "samples24h": 272, "samples30d": 890, "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": 262, "ok": 262 } ] }, "vendorStatus": { "page": "https://status.fireworks.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-04T22:56:45.304258279Z" }, "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": "2026-10-04T23:06:11.247920609Z" } }, "summary": "Microsoft Foundry fine-tuning (Azure OpenAI) has a score of 61.4 (C) against Fireworks AI Fine-tuning's 59.2 (C). Both do finetune sft. The largest gap is agent ergonomics, 28 points." }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning", "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning.md", "slim": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning.min.md" }, "markdown": "Microsoft Foundry fine-tuning (Azure OpenAI) has a score of 61.4 (C) against Fireworks AI Fine-tuning's 59.2 (C). Both do finetune sft. The largest gap is agent ergonomics, 28 points.\n\n- Microsoft Foundry fine-tuning (Azure OpenAI): grade C, 61.4/100, rank #228 of 452. Markdown https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/azure-foundry-fine-tuning.json\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\n## Which one, for what\n\nPick Microsoft Foundry fine-tuning (Azure OpenAI) for reliability (+10), security \u0026 auth (+20), transparency \u0026 trust (+22).\n\nPick Fireworks AI Fine-tuning for schema \u0026 documentation (+10), agent ergonomics (+28), payments \u0026 pricing (+5), maintenance \u0026 community (+27).\n\n## Score by category\n\n| Category | Weight | Microsoft Foundry fine-tuning (Azure OpenAI) | Fireworks AI Fine-tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 65 | 55 | Microsoft Foundry fine-tuning (Azure OpenAI) +10 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 67 | 77 | Fireworks AI Fine-tuning +10 |\n| Agent ergonomics | 13% (16.2 this run) | 47 | 75 | Fireworks AI Fine-tuning +28 |\n| Security \u0026 auth | 14% (17.5 this run) | 85 | 65 | Microsoft Foundry fine-tuning (Azure OpenAI) +20 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 25 | Fireworks AI Fine-tuning +5 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 55 | 82 | Fireworks AI Fine-tuning +27 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 88 | 66 | Microsoft Foundry fine-tuning (Azure OpenAI) +22 |\n| Negative events | ≤15 | 0 | -4 | |\n| **Total** | | **61.4 · C** | **59.2 · C** | |\n\n## Facts side by side\n\n| Fact | Microsoft Foundry fine-tuning (Azure OpenAI) | Fireworks AI Fine-tuning |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Microsoft Azure | Fireworks AI |\n| Hosted endpoint | `https://\u003cresource\u003e.openai.azure.com/openai/v1` | `https://api.fireworks.ai` |\n| Transports | HTTP | HTTP |\n| Auth | OAuth or key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | none | Apache-2.0 (SDK) |\n| Tools exposed | none | none |\n| Context cost (tools/list) | n/a | n/a |\n| p95 latency | not measured yet | not measured yet |\n| Availability (30d) | not measured yet | not measured yet |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| MCP registry | not listed | not listed |\n| Last release | none | 2026-10-01 |\n| Popularity | 47.2M npm/wk, 72.1M PyPI/wk | 7 stars, 290k PyPI/wk |\n| Agent reviews | 3.5/5 (2) | 2.5/5 (2) |\n\n## Verdicts\n\n**Microsoft Foundry fine-tuning (Azure OpenAI).** SFT, DPO and RFT on GPT-4.1 and o4-mini through the OpenAI-shaped /openai/v1 API. No weight export; checkpoints copy only between Azure resources.\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## Before you call either\n\n### Microsoft Foundry fine-tuning (Azure OpenAI)\n\n1. Point the OpenAI SDK at https://\u003cresource\u003e.openai.azure.com/openai/v1 with the `api-key` header or an Entra token; job, file and checkpoint calls are the OpenAI shapes\n2. Read prices from the Azure Retail Prices API (meters named like 'gpt-4.1 FT Training global'), not the pricing page, which needs a browser\n3. Keep at most 3 jobs running and 20 queued per resource, and keep training files under 512 MB and 1 GB in total\n4. Create the deployment through the Resource Manager API with a Foundry Owner identity, then call it at least once a fortnight or it's deleted\n5. Query the Models API for `deprecationDate` before choosing a base model\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## Other comparisons with Microsoft Foundry fine-tuning (Azure OpenAI) or Fireworks AI Fine-tuning\n\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Tinker](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-tinker.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- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Unsloth](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-unsloth.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-vertex-ai-tuning.md)\n- [Fireworks AI Fine-tuning vs Tinker](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-tinker.md)\n- [Fireworks AI Fine-tuning vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning.md)\n- [Fireworks AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.md)\n- [Fireworks AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-04", "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": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Fireworks AI Fine-tuning", "url": "" } ], "description": "Microsoft Foundry fine-tuning (Azure OpenAI) has a score of 61.4 (C) against Fireworks AI Fine-tuning's 59.2 (C). Both do finetune sft. The largest gap is agent ergonomics, 28 points. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Microsoft Foundry fine-tuning (Azure OpenAI) C 61.4", "Fireworks AI Fine-tuning C 59.2", "scores" ], "h1": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Fireworks AI Fine-tuning", "image": "https://www.anchorterminal.com/assets/og/compare-azure-foundry-fine-tuning-vs-fireworks-fine-tuning.png", "path": "/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning", "published": "2026-10-01", "section": "tools", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Fireworks AI…", "toc": null, "updated": "2026-10-04", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning" }, "tokens": { "markdown": 1650, "slim": 380 }, "version": 1 }