{ "data": { "a": { "slug": "azure-openai", "name": "Azure OpenAI in Foundry Models", "vendor": "Microsoft", "vendorUrl": "https://azure.microsoft.com", "kind": "model", "category": "inference", "summary": "Microsoft Azure's hosted API for OpenAI's GPT models and other Foundry Models sold by Azure, such as DeepSeek, Llama, Mistral and Grok, called through an OpenAI-compatible v1 endpoint on each customer's Azure resource.", "url": "https://www.anchorterminal.com/tools/azure-openai", "markdownUrl": "https://www.anchorterminal.com/tools/azure-openai.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/azure-openai.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/azure-openai.json", "dns": "azure-openai.grade.anchorterminal.com", "repo": "https://github.com/Azure/azure-rest-api-specs", "license": "Proprietary service under the Microsoft Product Terms. The v1 OpenAPI spec is MIT, and the OpenAI SDKs the docs use are Apache-2.0", "transports": [ "http" ], "remoteUrl": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "packages": [ { "registry": "pypi", "name": "openai" }, { "registry": "npm", "name": "openai" }, { "registry": "npm", "name": "@azure/openai" } ], "auth": "mixed", "authNotes": "`api-key` header with one of the resource's two regenerable keys, or a Microsoft Entra ID bearer token (scope https://ai.azure.com/.default) with the Cognitive Services OpenAI User role. Key authentication can be disabled per resource (`disableLocalAuth`). Each model must be deployed in the resource before it can be called, by deployment name.", "pricing": "usage", "pricingNotes": "Pay per token on an Azure subscription. Global Standard GPT-6 and GPT-6.1 prices match OpenAI's list (GPT-6.1 Sol $2/$10, GPT-6.1 Terra $1/$5 per million tokens), Data Zone costs 10% more, long-context requests cost 2x input and 1.5x output, cached input 0.1x, cache writes 1.25x, Flex half price and priority processing 2x (GPT-6 Sol). Batch is 50% off Global Standard. Provisioned throughput is billed per PTU hour. Prices from the Azure Retail Prices API, because the pricing page fills its table with JavaScript (checked 2026-11-06).", "priceSummary": "from $0.10 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the Foundry docs, the v1 OpenAPI spec or the pricing page. Access needs an Azure subscription with a payment method (checked 2026-11-06).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": 454039, "pypiWeekly": null, "asOf": "2026-11-06" }, "docsUrl": "https://learn.microsoft.com/en-us/azure/foundry/openai/", "rateLimitsUrl": "https://learn.microsoft.com/en-us/azure/foundry/openai/quotas-limits", "openapi": "https://github.com/Azure/azure-rest-api-specs/blob/main/specification/ai/data-plane/OpenAI.v1/azure-v1-v1-generated.json", "capabilities": [ "inference.llm", "inference.open-weights" ], "tags": [ "official", "hosted", "model", "card-required", "openapi", "openai-compatible", "closed-source", "enterprise", "eu", "python", "typescript", "status-page", "bug-bounty" ], "lastRelease": "2026-10-26", "graded": true, "anchor": { "graded": true, "score": 80, "grade": "A", "agentReady": true, "rank": 14, "ranked": true, "rankOf": 1486, "categoryRank": 4, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 98, "maintenance": 86, "payments": 20, "reliability": 80, "schema": 87, "security": 95, "transparency": 83 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": 0, "verdict": "An OpenAI-compatible v1 API with a public OpenAPI spec, Entra ID roles in place of shared keys and FedRAMP High scope, with GPT-6 and GPT-6.1 at OpenAI's list prices on Global Standard. Access needs an Azure subscription with a card and a deployment per model, and a 29 September 2026 incident caused about six hours of failures in Sweden Central.", "bestFor": "Teams on Azure that need OpenAI models with Entra ID, private networking, EU or US data zones or FedRAMP scope, and one resource that also serves DeepSeek, Llama, Mistral and Grok models.", "strengths": [ "Public OpenAPI 3.2 spec for the v1 API, 71 paths covering Responses, Chat Completions, batches, files and vector stores", "Microsoft Entra ID tokens with Azure roles, and key authentication can be switched off per resource", "GA models carry a retirement date 18 months after launch, set at release and readable through the Models API", "Data Zone deployments keep processing inside the EU or US, and Azure OpenAI is in FedRAMP High scope", "GPT-6 Astra, Sol and Luna at $10/$50, $2/$10 and $0.10/$0.50 per million tokens on Global Standard" ], "weaknesses": [ "Intermittent failures and 5xx errors in Sweden Central for about six hours on 29 September 2026", "An Azure subscription with a card, a resource and a model deployment come before the first call", "No llms.txt, and no Azure OpenAI what's new page was found; the Foundry one still covered August on 6 November", "No retention period stated for prompts kept for human abuse review, and Responses data is kept 30 days by default", "The retirement schedule lists two `gpt-realtime-mini` versions twice with different retirement dates, and no GPT-6 retirement dates yet" ], "agentNotes": [ "Point the OpenAI SDK at https://\u003cresource\u003e.openai.azure.com/openai/v1/ and pass the deployment name as `model`, not the model id", "Prefer Entra ID with the Cognitive Services OpenAI User role over the resource key, which opens the whole data plane", "On 429 wait for `retry-after-ms`, then back off with jitter. The OpenAI SDK retries twice by default", "Set `store: false` on Responses calls you don't need kept. Stored responses persist 30 days by default", "Keep remote MCP approvals on for servers you don't control. They're required by default" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "A", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 80 } ], "editorialScores": { "ergonomics": 98, "maintenance": 86, "payments": 20, "reliability": 80, "schema": 87, "security": 95, "transparency": 77 }, "provenanceScore": 88, "checked": { "categories": { "ergonomics": "2026-11-06", "maintenance": "2026-11-06", "payments": "2026-11-06", "reliability": "2026-11-06", "schema": "2026-11-06", "security": "2026-11-06", "transparency": "2026-11-06" }, "last": "2026-11-06", "oldest": "2026-11-06", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install openai azure-identity # or: npm i openai @azure/identity", "http": "curl \"https://$AZURE_OPENAI_RESOURCE.openai.azure.com/openai/v1/responses\" \\\n -H \"api-key: $AZURE_OPENAI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"\u003cyour-deployment-name\u003e\",\"input\":\"hello\"}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/azure-openai" }, "sameCompany": [ "azure-foundry-fine-tuning", "azure-ai-content-safety", "azure-speech-to-text", "azure-text-to-speech", "microsoft-agent-framework", "microsoft-execution-containers", "microsoft-entra-agent-id", "azure-key-vault", "bing-grounding", "azure-document-intelligence", "azure-devops-mcp", "microsoft-learn-mcp", "tokeneconomizer", "playwright-mcp", "azure-mcp", "azure-maps", "azure-translator", "microsoft-graph-calendar", "azure-blob-storage", "onedrive-sharepoint", "microsoft-teams", "dynamics-365-sales", "copilot-studio-mcp", "power-automate", "foundry-local", "microsoft-decision-1", "microsoft-advertising-api", "microsoft-excel-graph", "outlook-mail-graph", "microsoft-graph-people", "mage-vl-cowork" ], "area": "models", "unitPrices": [ { "item": "GPT-6.1 Sol input, Global Standard", "unit": "1m-tokens", "usd": 2, "note": "short context, Retail Prices API meter effective 2026-10-01" }, { "item": "GPT-6.1 Sol output, Global Standard", "unit": "1m-tokens", "usd": 10, "note": "short context" }, { "item": "GPT-6.1 Terra input, Global Standard", "unit": "1m-tokens", "usd": 1, "note": "short context" }, { "item": "GPT-6.1 Terra output, Global Standard", "unit": "1m-tokens", "usd": 5, "note": "short context" }, { "item": "GPT-6 Astra input, Global Standard", "unit": "1m-tokens", "usd": 10, "note": "short context" }, { "item": "GPT-6 Astra output, Global Standard", "unit": "1m-tokens", "usd": 50, "note": "short context" } ], "provenance": { "legalEntity": "Microsoft Corporation", "domain": "microsoft.com", "domainRegistered": "1991-05-02", "domainNote": "Endpoints are on openai.azure.com and cognitiveservices.azure.com. microsoft.com publishes a security.txt, but it passed its Expires date on 2026-09-23 and was still expired on 2026-11-06.", "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/foundry/whats-new-foundry", "securityTxt": "expired", "checked": "2026-11-06", "score": 88 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/azure-openai.json", "live": { "slug": "azure-openai", "probe": { "target": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "method": "get", "lastAt": "2026-11-07T18:20:12.145420458Z", "lastOk": false, "lastStatus": 0, "lastMs": 0, "lastNote": "DNS lookup failed", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 200, "samples30d": 200, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 0 }, { "date": "2026-11-07", "probes": 187, "ok": 0 } ], "outages": [ { "start": "2026-11-06T22:45:05.888142257Z", "end": "0001-01-01T00:00:00Z", "note": "DNS lookup failed" } ] }, "versions": [ { "registry": "npm", "name": "@azure/openai", "version": "2.0.0", "seenAt": "2026-11-06T23:09:28.988578937Z" }, { "registry": "npm", "name": "openai", "version": "7.53.0", "seenAt": "2026-11-06T23:09:28.954764315Z" }, { "registry": "pypi", "name": "openai", "version": "3.46.0", "released": "2026-11-05", "seenAt": "2026-11-06T23:09:28.844701934Z" } ], "githubStars": 3113, "npmWeekly": 56543040, "pypiWeekly": 91156146, "securityTxt": { "url": "https://microsoft.com/.well-known/security.txt", "state": "valid", "expires": "2027-10-27T16:00:00.000Z", "checkedAt": "2026-11-07T16:02:01.196666056Z" }, "pages": [ { "url": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:13:01.613678733Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "51438ff38076" } ], "updatedAt": "2026-11-07T18:20:12.145420458Z" } }, "answer": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against Simplismart's 29.5 (F), and leads in 6 of 7 scored categories. Simplismart leads on payments \u0026 pricing.", "b": { "slug": "simplismart", "name": "Simplismart", "vendor": "Verute Technologies Private Limited", "vendorUrl": "https://simplismart.ai", "kind": "http-api", "category": "inference", "summary": "Simplismart is an inference platform for open models. It runs 150+ models on shared pay-per-token endpoints with an OpenAI-compatible API, and handles dedicated GPU deployments, bring-your-own-cloud clusters and fine-tuning jobs.", "url": "https://www.anchorterminal.com/tools/simplismart", "markdownUrl": "https://www.anchorterminal.com/tools/simplismart.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/simplismart.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/simplismart.json", "dns": "simplismart.grade.anchorterminal.com", "license": "Proprietary hosted service. The community Pipecat integration (pipecat-simplismart, by the simpli-smart GitHub account) is BSD-2-Clause; served models keep their own licences.", "transports": [ "http" ], "remoteUrl": "https://api.simplismart.live", "packages": [ { "registry": "pypi", "name": "simplismart-sdk" } ], "auth": "api-key", "authNotes": "Bearer API keys made under Settings, API Keys. Each key has a unique name and an expiry chosen at creation; non-expiring keys need a request to support. Keys return 401 after midnight UTC on the expiry date and can be deleted at once. The Python SDK and CLI use a separate Playground token. Organisation roles are Admin, Member and Billing User, and workspaces scope models and deployments.", "pricing": "usage", "pricingNotes": "Shared endpoints are priced per 1M tokens, per image or per audio minute, such as Llama 3.1 8B at $0.13 and DeepSeek-V3 at $0.90 per 1M tokens. Dedicated GPUs run from $1.20 (T4) to $5.20 (H200) per GPU-hour. Prepaid credits at $1 each; new organisations may receive $5 of free credits.", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs (llms.txt index, billing, API keys and API reference pages) or on the pricing page, checked 28 October 2026. Credits are bought by card, UPI or netbanking, or billed postpaid.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-28" }, "docsUrl": "https://docs.simplismart.ai", "llmsTxt": "https://docs.simplismart.ai/llms.txt", "openapi": "https://docs.simplismart.ai/openapi-specs/playground/llama-8b.yaml", "capabilities": [ "inference.llm", "inference.open-weights", "speech.stt", "image.generate", "compute.endpoints", "finetune.sft", "finetune.lora", "finetune.preference", "finetune.rl" ], "tags": [ "hosted", "model", "open-weights", "usage-based", "openai-compatible", "openapi", "llms-txt", "python", "dedicated-endpoints", "byoc", "fine-tuning" ], "lastRelease": "2026-10-19", "graded": true, "anchor": { "graded": true, "score": 29.5, "grade": "F", "agentReady": false, "rank": 1401, "ranked": true, "rankOf": 1486, "categoryRank": 42, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 17, "maintenance": 42, "payments": 25, "reliability": 15, "schema": 51, "security": 35, "transparency": 29 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-28" }, "negative": 0, "verdict": "Simplismart exposes open models through an OpenAI-compatible endpoint with public per-token prices and expiring, revocable keys. Its public docs give no request rate limits, status page, retry guidance, tool-calling or structured-output reference, and its terms and privacy policy cover the website, not API data.", "bestFor": "Teams that want open models on a pay-per-token OpenAI-compatible endpoint and a path to dedicated GPUs, their own cloud or fine-tuning with one vendor, and can work without published rate limits or data-handling terms.", "strengths": [ "Public per-unit prices without a login, such as Llama 3.1 8B at $0.13 per 1M tokens and Whisper v3 Turbo at $0.0018 per audio minute", "API keys are named, carry an expiry date chosen at creation, return 401 after expiry and can be deleted at once", "Markdown docs and an llms.txt index, with OpenAPI 3.1 files for seven LLMs, Whisper and Flux", "Shared endpoints, dedicated GPU deployments and bring-your-own-cloud clusters sit on one account, with workspaces and organisation roles" ], "weaknesses": [ "No request or token rate limits, 429 handling or retry guidance found in the docs; only GPU quotas, given as 1 and as 8 H100s on two pages", "No status page, incident history or SLA document found; the 99.99% uptime figure is a homepage claim", "Tool calling, structured output, prompt caching and context lengths are not documented for the shared endpoints", "The terms (13 January 2023) and privacy policy cover the website and say nothing about prompt retention or training on API data", "The pricing page lists older models (Llama 3.1, DeepSeek-R1) and omits Gemma 4 31B, the model the quickstart uses" ], "agentNotes": [ "Point an OpenAI-compatible client at https://api.simplismart.live with SIMPLISMART_API_KEY as a Bearer token, and take model ids from the marketplace or the per-model API pages.", "Set a key expiry and rotate before midnight UTC on the expiry date, because an expired key returns 401.", "Cap retries yourself with exponential backoff, because no rate limits, 429 format or retry guidance are published.", "Check that a model supports tools or JSON output with a test call before relying on it, because neither is documented.", "Confirm the per-token price of the exact model in the console before running large jobs, because the public price list is incomplete." ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "F", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 29.5 } ], "editorialScores": { "ergonomics": 17, "maintenance": 42, "payments": 25, "reliability": 15, "schema": 51, "security": 35, "transparency": 14 }, "provenanceScore": 44, "checked": { "categories": { "ergonomics": "2026-10-28", "maintenance": "2026-10-28", "payments": "2026-10-28", "reliability": "2026-10-28", "schema": "2026-10-28", "security": "2026-10-28", "transparency": "2026-10-28" }, "last": "2026-10-28", "lastMoved": "2026-10-30", "oldest": "2026-10-28", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl 'https://api.simplismart.live/chat/completions' -H 'Content-Type: application/json' -H \"Authorization: Bearer $SIMPLISMART_API_KEY\" -d '{\"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Say hello.\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/simplismart" }, "area": "models", "unitPrices": [ { "item": "Llama 3.1 8B (shared)", "unit": "1m-tokens", "usd": 0.13, "note": "Single per-1M-token rate on the pricing page, checked 28 October 2026; input and output are not priced separately" }, { "item": "Llama 3.3 70B (shared)", "unit": "1m-tokens", "usd": 0.74, "note": "Pricing page, checked 28 October 2026" }, { "item": "DeepSeek-V3 (shared)", "unit": "1m-tokens", "usd": 0.9, "note": "Pricing page, checked 28 October 2026" }, { "item": "Whisper v3 Turbo (shared)", "unit": "audio-minute", "usd": 0.0018, "note": "Pricing page, checked 28 October 2026" }, { "item": "Flux Dev (shared, 1024x1024)", "unit": "image", "usd": 0.03, "note": "Pricing page, checked 28 October 2026" }, { "item": "NVIDIA H100, dedicated", "unit": "gpu-hour", "usd": 5, "note": "Pricing page table order, checked 28 October 2026; a Simplismart article gives $4.00 per hour" } ], "provenance": { "legalEntity": "Verute Technologies Private Limited", "domain": "simplismart.ai", "domainRegistered": "", "endpointOnVendorDomain": false, "terms": "https://simplismart.ai/terms-of-service", "privacy": "https://simplismart.ai/privacy-policy", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-28", "notes": [ "The terms (last updated 13 January 2023) and privacy policy (effective 13 January 2023) name Verute Technologies Private Limited, Indian law and an address on Residency Road, Bengaluru. Both define the Service as the simplismart.ai website.", "The inference API answers on api.simplismart.live, a separate domain named in the docs. Domain registration was not checked.", "No status page found. The docs and site link none, and status.simplismart.ai could not be reached from our network.", "No changelog found in the docs sitemap or llms.txt. Model additions appear as blog posts.", "simplismart.ai/.well-known/security.txt answered 404." ], "score": 44 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/simplismart.json", "live": { "slug": "simplismart", "probe": { "target": "https://api.simplismart.live", "method": "get", "lastAt": "2026-11-07T18:20:54.751062838Z", "lastOk": true, "lastStatus": 405, "lastMs": 96, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 61, "p95ms24h": 169, "samples24h": 239, "samples30d": 2563, "days": [ { "date": "2026-10-28", "probes": 208, "ok": 208 }, { "date": "2026-10-29", "probes": 249, "ok": 249 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 187, "ok": 187 } ] }, "versions": [ { "registry": "pypi", "name": "simplismart-sdk", "version": "0.1.15", "released": "2026-11-02", "seenAt": "2026-11-07T00:06:08.735466052Z" } ], "pypiWeekly": 186, "securityTxt": { "url": "https://simplismart.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:01:22.590979779Z" }, "llmsTxt": { "url": "https://docs.simplismart.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:33:56.047475338Z" }, "domain": { "domain": "simplismart.ai", "registered": "2022-11-18", "source": "https://rdap.identitydigital.services/rdap/domain/simplismart.ai", "checkedAt": "2026-11-01T14:28:09.738527702Z" }, "pages": [ { "url": "https://simplismart.ai/privacy-policy", "kind": "privacy", "status": 304, "checkedAt": "2026-11-07T14:19:55.485469388Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "bd2d6d5ffe37" }, { "url": "https://simplismart.ai/terms-of-service", "kind": "terms", "status": 304, "checkedAt": "2026-11-07T14:19:59.722810598Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "94fe8807daf7" } ], "updatedAt": "2026-11-07T18:20:54.751062838Z" } }, "edges": [ { "by": 81, "category": "ergonomics", "leads": "azure-openai", "name": "Agent ergonomics", "reasons": { "a": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use with parallel calls and a forced call by naming the function in `tool_choice`, 18 of 20 because `gpt-6.1-sol` calls tools only through the Responses API (18). Strict structured outputs (15). Prompt caching from 1,024 tokens, cached input at 0.1x on GPT-6 ($1 per million on Astra) and cache writes at 1.25x from GPT-5.6 on, with explicit breakpoints (15). 1,050,000-token context on GPT-6 and GPT-6.1, input capped at 922,000 (15). Batch at 50% off Global Standard with a 24-hour target (10). The docs use OpenAI's own SDKs for Python, JavaScript, C#, Go and Java against the v1 endpoint (10). 429 and quota errors documented with retry headers (15). Calls name a deployment rather than a model, so each model must be deployed in the resource first, which the model checklist doesn't score.", "b": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). The LiveKit guide calls the LLM API fully OpenAI-compatible, but no page documents tools or tool_choice, and the OpenAPI files allow only system, user and assistant roles (3 of 20). No structured-output or JSON mode documented (0 of 15). No prompt caching documented for shared endpoints (0 of 15). Context lengths are not stated per model; output is capped at 4,096 tokens in the API files (3 of 15). No batch API for shared endpoints (0 of 10). Inference goes through the OpenAI client; the simplismart-sdk Python package and CLI manage repositories and deployments, not inference, and a community Pipecat package exists (5 of 10). Expired keys return a documented 401, and the SDK exposes status_code, message and payload, but no error codes or bodies are listed for the inference API (6 of 15). 3+0+0+3+0+5+6 = 17." }, "scores": { "a": 98, "b": 17 } }, { "by": 65, "category": "reliability", "leads": "azure-openai", "name": "Reliability", "reasons": { "a": "Azure status page with post-incident reviews, listing Azure OpenAI Service, Foundry Models and Foundry Agent Service as components (20). One incident in the 90 days to 6 November 2026 names Azure OpenAI, and no review after it does, intermittent request failures, higher latency and HTTP 5xx errors in Sweden Central from 10:03 to 15:58 UTC on 29 September 2026 (tracking 7QL5-Z50), counted as one major outage as in the Foundry fine-tuning dossier. The 23 July West US review and the 30 September gateway incident don't name it (10). Quota tiers from Free to Tier 6 with TPM and RPM per model and deployment type, 1,000 RPM and 1,000,000 TPM for each GPT-6 model on Global Standard at Tier 1 (15). 429 responses carry `retry-after-ms`, every response carries `x-ratelimit-*` headers, and the quota guide gives exponential backoff with jitter and SDK retry settings (15). The models page says models sold by Azure are covered by Azure service-level agreements and links the Online Services SLA. The consolidated SLA document wouldn't download on 6 October and wasn't retried on 6 November, so the clause rests on Microsoft's docs, read the same way as the 5 October Azure Speech check (10). The v1 API is GA and the GPT-6 models carry no preview label (10).", "b": "Hosted reading. No status page found. The docs and site link none, a search found none, and status.simplismart.ai could not be reached from our network (0). No readable incident history (5). No request or token rate limits are published for shared endpoints. The docs give GPU quotas for training, compilation and private deployments only, 1 H100 and 1 L40 on the Quotas page and 8 of each on the Organisation page (0). No 429 response, Retry-After header or retry guidance found; the OpenAPI files list only 400, 401 and 500 (0). No SLA document found. The homepage claims 99.99% uptime and a Simplismart post calls its autoscaling SLA-backed, but no terms state a commitment, and the terms provide the service as is (0). Shared endpoints carry no beta label, while Muse Spark Open is in beta (10). 0+5+0+0+0+10 = 15." }, "scores": { "a": 80, "b": 15 } }, { "by": 60, "category": "security", "leads": "azure-openai", "name": "Security \u0026 auth", "reasons": { "a": "Model reading. Microsoft Entra ID tokens with Azure roles (the Cognitive Services OpenAI User role for calls), managed identities, and key authentication that can be disabled per resource, or two regenerable resource keys in the `api-key` header. No documented route puts the key in a URL query string (30). Prompts, completions, embeddings and training data aren't used to train foundation models without the customer's permission or instruction, per the data privacy page updated 4 August 2026 (20). Flagged prompts and completions may be stored in the customer's geography for human abuse review, with no retention period stated, and approved customers can turn that storage off through modified abuse monitoring. Responses API data is kept 30 days by default unless `store` is false (12 of 15). Azure Monitor resource logs and the subscription activity log, as read in the Foundry fine-tuning dossier (15). MSRC coordinated disclosure policy and Microsoft's bounty programme, both named in microsoft.com's security.txt, and Azure OpenAI is listed in FedRAMP High and DoD IL2 scope in Azure public regions. The security.txt passed its Expires date on 2026-09-23 and was still expired on 6 November, which the Bedrock and S3 dossiers mark down by 2, applied here too (18 of 20, down from 20 on 6 October for consistency).", "b": "Model reading. Named Bearer keys with an expiry chosen at creation, a 401 after expiry and immediate deletion. No scopes or per-key limits found, keys belong to the organisation, and the SDK uses a separate Playground token (20 of 30). No statement on training on API data in the terms, the privacy policy, the AI policy or the docs (0 of 20). No retention or zero-retention statement for prompts and outputs. The privacy policy covers the website only (0 of 15). The Usage page shows request counts and a per-request timeline per model, and an LLM metrics call returns token counts and timings per request id. A Simplismart article claims exportable audit trails, not documented (8 of 15). The homepage shows GDPR, ISO 27001 v2022 and AICPA SOC 2 badges, and a Simplismart article says it holds a SOC 2 Type 2 report, available on request. No trust centre was reachable, security.txt answered 404, and no disclosure policy or bug bounty was found (7 of 20). 20+0+0+8+7 = 35." }, "scores": { "a": 95, "b": 35 } }, { "by": 54, "category": "transparency", "leads": "azure-openai", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed service under the Microsoft Product Terms, with the v1 spec under MIT and the OpenAI SDKs under Apache-2.0 (15). The data privacy page agrees with the product terms on no training and states where data is processed and stored by deployment type, but gives no retention period for the human-review store and sits beside a 30-day default for Responses data (24 of 30). A published lifecycle policy and a retirement schedule with dates and replacements, but on 6 November the schedule listed `gpt-realtime-mini` 2025-10-06 and 2025-12-15 twice each with different retirement dates, and no GPT-6 model yet (18 of 20, down from 20). Data locations stated for Standard, Data Zone and Global deployments, and a public sub-processor list per the 5 October Azure Speech check, not re-read (20).", "b": "Closed service. The terms, last updated 13 January 2023, name Verute Technologies Private Limited and Indian law but define the Service as the website, grant Simplismart a licence to use and redistribute posted Content, bar robots and automatic access, and rule out refunds. No API or platform terms were found (8 of 30). The privacy policy, effective 13 January 2023, is a website policy that says personal data is processed in India. It says nothing about prompts, outputs, logs or training, and no DPA, retention period or subprocessor list was found. A Simplismart article claims GDPR and HIPAA support (3 of 30). No deprecation policy or dated removals (0 of 20). Data goes to India per the privacy policy, and an article says workloads are kept in approved regions by scheduling policy. No subprocessor or hosting-provider list (3 of 20). 8+3+0+3 = 14." }, "scores": { "a": 83, "b": 29 } }, { "by": 44, "category": "maintenance", "leads": "azure-openai", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading. GPT-6.1 Terra released on Azure on 26 October 2026 and a `gpt-chat-latest` preview of 7 October was added, after GPT-6.1 Sol on 29 September (30). GA models get a retirement date 18 months after launch, set at release and readable through the Models API, with at least 60 days' email and Service Health notice, and previews at least 30 days, so a GA retirement is known more than six months ahead (12 of 12). Many shutdown dates in the 90 days to 6 November, most of them previews or partner models, among them DeepSeek-R1 (13 August), MAI-Image-2 and 2e (15 August), model-router previews (30 August), MAI-Transcribe-1 (15 September), MAI-Image-2.5 previews (1 October), the 2025-03-20 audio models and sora-2 (15 October), a Cohere Command A Plus preview (16 October) and gpt-image-1 (23 October), each in the schedule with a replacement where one exists (4 of 8). The Foundry what's new page, still on August when read on 6 November, plus Microsoft Q\u0026A and paid support; no Azure OpenAI what's new was found (10 of 15). The SDKs are OpenAI's repositories, replies not sampled (5 of 10). Current official SDKs, `openai` 3.46.0 on PyPI (5 November) and 7.53.0 on npm, plus Microsoft's `@azure/openai` 2.0.0 companion (15). Supported runtimes stated, Python 3.9 or later and Node.js 20 or later, though `openai` 3.46.0 requires Python 3.10 (10).", "b": "Model reading for a closed service. Muse Spark Open was added in beta on 19 October 2026, and the docs sitemap shows pages changed on 7 October (30). No deprecation policy or notice period found (0 of 12). One dated model addition in the 90 days to 28 October and no recorded removals; the pricing page still lists Llama 3.1 405B and DeepSeek-R1, which the API reference doesn't cover, so the live model set is unclear (2 of 8). No public changelog or community channel. Support is support@simplismart.ai and a contact form, not tested (4 of 15). SDK issue replies not read, because github.com gave no readable robots.txt (0 of 10). simplismart-sdk for Python, documented with a CLI, and the OpenAI client for inference. No TypeScript SDK, and the PyPI page answered with a browser challenge, so the version and date are unchecked (6 of 15). CI not checked (0 of 10). 30+0+2+4+0+6+0 = 42." }, "scores": { "a": 86, "b": 42 } }, { "by": 36, "category": "schema", "leads": "azure-openai", "name": "Schema \u0026 documentation", "reasons": { "a": "OpenAPI 3.2 spec for the v1 data plane in Azure/azure-rest-api-specs (`azure-v1-v1-generated.json`, 71 paths and 852 schemas), linked from the v1 API page (25). No llms.txt at learn.microsoft.com (404). Learn pages offer Copy Markdown and the Markdown sources are public in MicrosoftDocs/azure-ai-docs, last merged 5 October (5 of 10). The models page lists each model's capabilities, context and API support, but the model choice guide, dated 19 May 2026, doesn't cover GPT-6 (15 of 20). Structured outputs with `strict: true` on schemas and function tools, though not together with parallel function calls (15). Examples in Python, C#, JavaScript, Go, Java and REST, and an error table on the quotas page with a fix for 429 and other codes (15). The v1 path replaces monthly `api-version` values, with preview capabilities opted into by header, a dated API version changelog and dated model versions. On 6 November the Foundry what's new still covered August, though updated on 5 November, and no Azure OpenAI what's new was found at learn.microsoft.com/en-us/azure/foundry/openai/whats-new, which answered 404 (12 of 15).", "b": "Model reading. OpenAPI 3.1 files for seven LLMs and 3.0 or 3.1 files for Whisper, Flux, an LLM metrics call and training jobs. They cover a handful of the 150+ models, omit tools and response_format, show a streaming `delta` as the non-streaming response, and several name hosts on s9t.link instead of api.simplismart.live. The Llama 4 Maverick file gives `llama3_1` as its only model id, and /api-reference/openapi.json is the Flux spec (12 of 25). llms.txt and a Markdown copy of each docs page (10). The docs say when to use shared or dedicated endpoints; model pages carry one-line descriptions (10 of 20). Roles are enums, temperature is bounded 0 to 2, max_tokens 1 to 4096, and messages and model are required (10 of 15). curl and OpenAI-client Python examples. Errors are bare 400, 401 and 500 descriptions, plus an SDK example of a 404 payload (7 of 15). No changelog. Every spec reads version 1.0.0 (2 of 15). 12+10+10+10+7+2 = 51." }, "scores": { "a": 87, "b": 51 } } ], "facts": [ { "a": "Model API", "b": "HTTP API", "name": "Kind" }, { "a": "Microsoft", "b": "Verute Technologies Private Limited", "name": "Vendor" }, { "a": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "b": "https://api.simplismart.live", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary service under the Microsoft Product Terms. The v1 OpenAPI spec is MIT, and the OpenAI SDKs the docs use are Apache-2.0", "b": "Proprietary hosted service. The community Pipecat integration (pipecat-simplismart, by the simpli-smart GitHub account) is BSD-2-Clause; served models keep their own licences.", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-26", "b": "2026-10-19", "name": "Last release" }, { "a": "no date given", "b": "2023-01-13", "name": "Terms last updated" }, { "a": "2026-09-01", "b": "2023-01-13", "name": "Privacy policy last updated" }, { "a": "yes", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "yes", "b": "yes", "name": "Terms restrict automated access" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "yes", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "454k npm/wk", "b": "none", "name": "Popularity" } ], "faq": [ { "answer": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against Simplismart's 29.5 (F), and leads in 6 of 7 scored categories. Simplismart leads on payments \u0026 pricing.", "question": "Which is better for AI agents, Azure OpenAI in Foundry Models or Simplismart?" }, { "answer": "Azure OpenAI in Foundry Models takes an API key or an OAuth sign-in. Simplismart needs an API key.", "question": "Do Azure OpenAI in Foundry Models and Simplismart need an API key?" }, { "answer": "Yes. Azure OpenAI in Foundry Models has a hosted endpoint at https://\u003cresource\u003e.openai.azure.com/openai/v1 and Simplismart at https://api.simplismart.live.", "question": "Can an agent call Azure OpenAI in Foundry Models and Simplismart without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 80 against 15", "Schema \u0026 documentation, 87 against 51", "Agent ergonomics, 98 against 17", "Security \u0026 auth, 95 against 35", "Maintenance \u0026 community, 86 against 42", "Transparency \u0026 trust, 83 against 29" ], "also": [ "Agent-ready, a grade of BB or better" ], "goodFor": "Teams on Azure that need OpenAI models with Entra ID, private networking, EU or US data zones or FedRAMP scope, and one resource that also serves DeepSeek, Llama, Mistral and Grok models.", "slug": "azure-openai", "watchFor": "Intermittent failures and 5xx errors in Sweden Central for about six hours on 29 September 2026" }, { "aheadOn": [ "Payments \u0026 pricing, 25 against 20" ], "also": null, "goodFor": "Teams that want open models on a pay-per-token OpenAI-compatible endpoint and a path to dedicated GPUs, their own cloud or fine-tuning with one vendor, and can work without published rate limits or data-handling terms.", "slug": "simplismart", "watchFor": "No request or token rate limits, 429 handling or retry guidance found in the docs; only GPU quotas, given as 1 and as 8 H100s on two pages" } ], "history": { "azure-openai": null, "simplismart": [ { "causes": [ "method" ], "date": "2026-10-30", "delta": -0.2, "from": 29.7, "fromGrade": "F", "to": 29.5, "toGrade": "F" } ] }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-azure-openai.json", "title": "Amazon Bedrock vs Azure OpenAI in Foundry Models", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-azure-openai" }, { "json": 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{ "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-simplismart.json", "title": "DeepSeek API vs Simplismart", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-simplismart.json", "title": "Eden AI vs Simplismart", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-simplismart.json", "title": "Glimpse vs Simplismart", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-simplismart.json", "title": "Fireworks AI Inference vs Simplismart", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-simplismart.json", "title": "Gemini Developer API vs Simplismart", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-simplismart.json", "title": "GroqCloud vs Simplismart", "url": "https://www.anchorterminal.com/compare/groq-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-simplismart.json", "title": "Hugging Face Inference Providers vs Simplismart", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-simplismart.json", "title": "Inkling 2 Small vs Simplismart", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-simplismart.json", "title": "Jais 3 vs Simplismart", "url": "https://www.anchorterminal.com/compare/jais-3-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-simplismart.json", "title": "Meta Model API vs Simplismart", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-simplismart.json", "title": "MiniMax API vs Simplismart", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-simplismart.json", "title": "Mistral AI API vs Simplismart", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-simplismart.json", "title": "Muse Spark Open vs Simplismart", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/novita-ai-vs-simplismart.json", "title": "Novita AI vs Simplismart", "url": "https://www.anchorterminal.com/compare/novita-ai-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-simplismart.json", "title": "OpenAI API vs Simplismart", "url": "https://www.anchorterminal.com/compare/openai-api-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/openrouter-vs-simplismart.json", "title": "OpenRouter vs Simplismart", "url": "https://www.anchorterminal.com/compare/openrouter-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-simplismart.json", "title": "Prism Inference vs Simplismart", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/ramp-router-vs-simplismart.json", "title": "Router.com (Ramp Router) vs Simplismart", "url": "https://www.anchorterminal.com/compare/ramp-router-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/reflection-beam-vs-simplismart.json", "title": "Beam 1.0 vs Simplismart", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/routerplus-vs-simplismart.json", "title": "RouterPlus vs Simplismart", "url": "https://www.anchorterminal.com/compare/routerplus-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-simplismart.json", "title": "SambaCloud vs Simplismart", "url": "https://www.anchorterminal.com/compare/sambanova-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-simplismart.json", "title": "SiliconFlow vs Simplismart", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-stepfun-api.json", "title": "Simplismart vs StepFun API", "url": "https://www.anchorterminal.com/compare/simplismart-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-swe-2-flash.json", "title": "Simplismart vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/simplismart-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-tencent-tokenhub.json", "title": "Simplismart vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/simplismart-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-together-inference.json", "title": "Simplismart vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/simplismart-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-vercel-ai-gateway.json", "title": "Simplismart vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/simplismart-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-vertex-ai.json", "title": "Simplismart vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/simplismart-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-xai-api.json", "title": "Simplismart vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/simplismart-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-simplismart.json", "title": "Nemotron 3.5 Super VL vs Simplismart", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-simplismart" } ], "panel": null, "scores": [ { "azure-openai": 80, "by": 65, "edge": "azure-openai", "key": "reliability", "name": "Reliability", "simplismart": 15, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "azure-openai": 87, "by": 36, "edge": "azure-openai", "key": "schema", "name": "Schema \u0026 documentation", "simplismart": 51, "weight": 13 }, { "azure-openai": 98, "by": 81, "edge": "azure-openai", "key": "ergonomics", "name": "Agent ergonomics", "simplismart": 17, "weight": 13 }, { "azure-openai": 95, "by": 60, "edge": "azure-openai", "key": "security", "name": "Security \u0026 auth", "simplismart": 35, "weight": 14 }, { "azure-openai": 20, "by": 5, "edge": "simplismart", "key": "payments", "name": "Payments \u0026 pricing", "simplismart": 25, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "azure-openai": 86, "by": 44, "edge": "azure-openai", "key": "maintenance", "name": "Maintenance \u0026 community", "simplismart": 42, "weight": 7 }, { "azure-openai": 83, "by": 54, "edge": "azure-openai", "key": "transparency", "name": "Transparency \u0026 trust", "simplismart": 29, "weight": 7 } ], "summary": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against Simplismart's 29.5 (F), and leads in 6 of 7 scored categories. Simplismart leads on payments \u0026 pricing. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "azure-openai": "An OpenAI-compatible v1 API with a public OpenAPI spec, Entra ID roles in place of shared keys and FedRAMP High scope, with GPT-6 and GPT-6.1 at OpenAI's list prices on Global Standard. Access needs an Azure subscription with a card and a deployment per model, and a 29 September 2026 incident caused about six hours of failures in Sweden Central.", "simplismart": "Simplismart exposes open models through an OpenAI-compatible endpoint with public per-token prices and expiring, revocable keys. Its public docs give no request rate limits, status page, retry guidance, tool-calling or structured-output reference, and its terms and privacy policy cover the website, not API data." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/azure-openai-vs-simplismart", "json": "https://www.anchorterminal.com/compare/azure-openai-vs-simplismart.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/azure-openai-vs-simplismart.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/azure-openai-vs-simplismart.min.md" }, "markdown": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against Simplismart's 29.5 (F), and leads in 6 of 7 scored categories. Simplismart leads on payments \u0026 pricing. Both do llm inference.\n\n- Azure OpenAI in Foundry Models: grade A, 80/100, rank #14 of 1486. Markdown https://www.anchorterminal.com/tools/azure-openai.md · JSON https://www.anchorterminal.com/api/v1/tools/azure-openai.json\n- Simplismart: grade F, 29.5/100, rank #1401 of 1486. Markdown https://www.anchorterminal.com/tools/simplismart.md · JSON https://www.anchorterminal.com/api/v1/tools/simplismart.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 830 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### Azure OpenAI in Foundry Models (A)\n\nGood for: Teams on Azure that need OpenAI models with Entra ID, private networking, EU or US data zones or FedRAMP scope, and one resource that also serves DeepSeek, Llama, Mistral and Grok models.\n\nAhead on:\n- Reliability, 80 against 15\n- Schema \u0026 documentation, 87 against 51\n- Agent ergonomics, 98 against 17\n- Security \u0026 auth, 95 against 35\n- Maintenance \u0026 community, 86 against 42\n- Transparency \u0026 trust, 83 against 29\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: Intermittent failures and 5xx errors in Sweden Central for about six hours on 29 September 2026\n\n### Simplismart (F)\n\nGood for: Teams that want open models on a pay-per-token OpenAI-compatible endpoint and a path to dedicated GPUs, their own cloud or fine-tuning with one vendor, and can work without published rate limits or data-handling terms.\n\nAhead on:\n- Payments \u0026 pricing, 25 against 20\n\nWatch for: No request or token rate limits, 429 handling or retry guidance found in the docs; only GPU quotas, given as 1 and as 8 H100s on two pages\n\n\n## Why the edge\n\n### Agent ergonomics: Azure OpenAI in Foundry Models +81\n\n- Azure OpenAI in Foundry Models (98): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use with parallel calls and a forced call by naming the function in `tool_choice`, 18 of 20 because `gpt-6.1-sol` calls tools only through the Responses API (18). Strict structured outputs (15). Prompt caching from 1,024 tokens, cached input at 0.1x on GPT-6 ($1 per million on Astra) and cache writes at 1.25x from GPT-5.6 on, with explicit breakpoints (15). 1,050,000-token context on GPT-6 and GPT-6.1, input capped at 922,000 (15). Batch at 50% off Global Standard with a 24-hour target (10). The docs use OpenAI's own SDKs for Python, JavaScript, C#, Go and Java against the v1 endpoint (10). 429 and quota errors documented with retry headers (15). Calls name a deployment rather than a model, so each model must be deployed in the resource first, which the model checklist doesn't score.\n- Simplismart (17): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). The LiveKit guide calls the LLM API fully OpenAI-compatible, but no page documents tools or tool_choice, and the OpenAPI files allow only system, user and assistant roles (3 of 20). No structured-output or JSON mode documented (0 of 15). No prompt caching documented for shared endpoints (0 of 15). Context lengths are not stated per model; output is capped at 4,096 tokens in the API files (3 of 15). No batch API for shared endpoints (0 of 10). Inference goes through the OpenAI client; the simplismart-sdk Python package and CLI manage repositories and deployments, not inference, and a community Pipecat package exists (5 of 10). Expired keys return a documented 401, and the SDK exposes status_code, message and payload, but no error codes or bodies are listed for the inference API (6 of 15). 3+0+0+3+0+5+6 = 17.\n\n### Reliability: Azure OpenAI in Foundry Models +65\n\n- Azure OpenAI in Foundry Models (80): Azure status page with post-incident reviews, listing Azure OpenAI Service, Foundry Models and Foundry Agent Service as components (20). One incident in the 90 days to 6 November 2026 names Azure OpenAI, and no review after it does, intermittent request failures, higher latency and HTTP 5xx errors in Sweden Central from 10:03 to 15:58 UTC on 29 September 2026 (tracking 7QL5-Z50), counted as one major outage as in the Foundry fine-tuning dossier. The 23 July West US review and the 30 September gateway incident don't name it (10). Quota tiers from Free to Tier 6 with TPM and RPM per model and deployment type, 1,000 RPM and 1,000,000 TPM for each GPT-6 model on Global Standard at Tier 1 (15). 429 responses carry `retry-after-ms`, every response carries `x-ratelimit-*` headers, and the quota guide gives exponential backoff with jitter and SDK retry settings (15). The models page says models sold by Azure are covered by Azure service-level agreements and links the Online Services SLA. The consolidated SLA document wouldn't download on 6 October and wasn't retried on 6 November, so the clause rests on Microsoft's docs, read the same way as the 5 October Azure Speech check (10). The v1 API is GA and the GPT-6 models carry no preview label (10).\n- Simplismart (15): Hosted reading. No status page found. The docs and site link none, a search found none, and status.simplismart.ai could not be reached from our network (0). No readable incident history (5). No request or token rate limits are published for shared endpoints. The docs give GPU quotas for training, compilation and private deployments only, 1 H100 and 1 L40 on the Quotas page and 8 of each on the Organisation page (0). No 429 response, Retry-After header or retry guidance found; the OpenAPI files list only 400, 401 and 500 (0). No SLA document found. The homepage claims 99.99% uptime and a Simplismart post calls its autoscaling SLA-backed, but no terms state a commitment, and the terms provide the service as is (0). Shared endpoints carry no beta label, while Muse Spark Open is in beta (10). 0+5+0+0+0+10 = 15.\n\n### Security \u0026 auth: Azure OpenAI in Foundry Models +60\n\n- Azure OpenAI in Foundry Models (95): Model reading. Microsoft Entra ID tokens with Azure roles (the Cognitive Services OpenAI User role for calls), managed identities, and key authentication that can be disabled per resource, or two regenerable resource keys in the `api-key` header. No documented route puts the key in a URL query string (30). Prompts, completions, embeddings and training data aren't used to train foundation models without the customer's permission or instruction, per the data privacy page updated 4 August 2026 (20). Flagged prompts and completions may be stored in the customer's geography for human abuse review, with no retention period stated, and approved customers can turn that storage off through modified abuse monitoring. Responses API data is kept 30 days by default unless `store` is false (12 of 15). Azure Monitor resource logs and the subscription activity log, as read in the Foundry fine-tuning dossier (15). MSRC coordinated disclosure policy and Microsoft's bounty programme, both named in microsoft.com's security.txt, and Azure OpenAI is listed in FedRAMP High and DoD IL2 scope in Azure public regions. The security.txt passed its Expires date on 2026-09-23 and was still expired on 6 November, which the Bedrock and S3 dossiers mark down by 2, applied here too (18 of 20, down from 20 on 6 October for consistency).\n- Simplismart (35): Model reading. Named Bearer keys with an expiry chosen at creation, a 401 after expiry and immediate deletion. No scopes or per-key limits found, keys belong to the organisation, and the SDK uses a separate Playground token (20 of 30). No statement on training on API data in the terms, the privacy policy, the AI policy or the docs (0 of 20). No retention or zero-retention statement for prompts and outputs. The privacy policy covers the website only (0 of 15). The Usage page shows request counts and a per-request timeline per model, and an LLM metrics call returns token counts and timings per request id. A Simplismart article claims exportable audit trails, not documented (8 of 15). The homepage shows GDPR, ISO 27001 v2022 and AICPA SOC 2 badges, and a Simplismart article says it holds a SOC 2 Type 2 report, available on request. No trust centre was reachable, security.txt answered 404, and no disclosure policy or bug bounty was found (7 of 20). 20+0+0+8+7 = 35.\n\n### Transparency \u0026 trust: Azure OpenAI in Foundry Models +54\n\n- Azure OpenAI in Foundry Models (83): Closed service under the Microsoft Product Terms, with the v1 spec under MIT and the OpenAI SDKs under Apache-2.0 (15). The data privacy page agrees with the product terms on no training and states where data is processed and stored by deployment type, but gives no retention period for the human-review store and sits beside a 30-day default for Responses data (24 of 30). A published lifecycle policy and a retirement schedule with dates and replacements, but on 6 November the schedule listed `gpt-realtime-mini` 2025-10-06 and 2025-12-15 twice each with different retirement dates, and no GPT-6 model yet (18 of 20, down from 20). Data locations stated for Standard, Data Zone and Global deployments, and a public sub-processor list per the 5 October Azure Speech check, not re-read (20).\n- Simplismart (29): Closed service. The terms, last updated 13 January 2023, name Verute Technologies Private Limited and Indian law but define the Service as the website, grant Simplismart a licence to use and redistribute posted Content, bar robots and automatic access, and rule out refunds. No API or platform terms were found (8 of 30). The privacy policy, effective 13 January 2023, is a website policy that says personal data is processed in India. It says nothing about prompts, outputs, logs or training, and no DPA, retention period or subprocessor list was found. A Simplismart article claims GDPR and HIPAA support (3 of 30). No deprecation policy or dated removals (0 of 20). Data goes to India per the privacy policy, and an article says workloads are kept in approved regions by scheduling policy. No subprocessor or hosting-provider list (3 of 20). 8+3+0+3 = 14.\n\n### Maintenance \u0026 community: Azure OpenAI in Foundry Models +44\n\n- Azure OpenAI in Foundry Models (86): Model reading. GPT-6.1 Terra released on Azure on 26 October 2026 and a `gpt-chat-latest` preview of 7 October was added, after GPT-6.1 Sol on 29 September (30). GA models get a retirement date 18 months after launch, set at release and readable through the Models API, with at least 60 days' email and Service Health notice, and previews at least 30 days, so a GA retirement is known more than six months ahead (12 of 12). Many shutdown dates in the 90 days to 6 November, most of them previews or partner models, among them DeepSeek-R1 (13 August), MAI-Image-2 and 2e (15 August), model-router previews (30 August), MAI-Transcribe-1 (15 September), MAI-Image-2.5 previews (1 October), the 2025-03-20 audio models and sora-2 (15 October), a Cohere Command A Plus preview (16 October) and gpt-image-1 (23 October), each in the schedule with a replacement where one exists (4 of 8). The Foundry what's new page, still on August when read on 6 November, plus Microsoft Q\u0026A and paid support; no Azure OpenAI what's new was found (10 of 15). The SDKs are OpenAI's repositories, replies not sampled (5 of 10). Current official SDKs, `openai` 3.46.0 on PyPI (5 November) and 7.53.0 on npm, plus Microsoft's `@azure/openai` 2.0.0 companion (15). Supported runtimes stated, Python 3.9 or later and Node.js 20 or later, though `openai` 3.46.0 requires Python 3.10 (10).\n- Simplismart (42): Model reading for a closed service. Muse Spark Open was added in beta on 19 October 2026, and the docs sitemap shows pages changed on 7 October (30). No deprecation policy or notice period found (0 of 12). One dated model addition in the 90 days to 28 October and no recorded removals; the pricing page still lists Llama 3.1 405B and DeepSeek-R1, which the API reference doesn't cover, so the live model set is unclear (2 of 8). No public changelog or community channel. Support is support@simplismart.ai and a contact form, not tested (4 of 15). SDK issue replies not read, because github.com gave no readable robots.txt (0 of 10). simplismart-sdk for Python, documented with a CLI, and the OpenAI client for inference. No TypeScript SDK, and the PyPI page answered with a browser challenge, so the version and date are unchecked (6 of 15). CI not checked (0 of 10). 30+0+2+4+0+6+0 = 42.\n\n### Schema \u0026 documentation: Azure OpenAI in Foundry Models +36\n\n- Azure OpenAI in Foundry Models (87): OpenAPI 3.2 spec for the v1 data plane in Azure/azure-rest-api-specs (`azure-v1-v1-generated.json`, 71 paths and 852 schemas), linked from the v1 API page (25). No llms.txt at learn.microsoft.com (404). Learn pages offer Copy Markdown and the Markdown sources are public in MicrosoftDocs/azure-ai-docs, last merged 5 October (5 of 10). The models page lists each model's capabilities, context and API support, but the model choice guide, dated 19 May 2026, doesn't cover GPT-6 (15 of 20). Structured outputs with `strict: true` on schemas and function tools, though not together with parallel function calls (15). Examples in Python, C#, JavaScript, Go, Java and REST, and an error table on the quotas page with a fix for 429 and other codes (15). The v1 path replaces monthly `api-version` values, with preview capabilities opted into by header, a dated API version changelog and dated model versions. On 6 November the Foundry what's new still covered August, though updated on 5 November, and no Azure OpenAI what's new was found at learn.microsoft.com/en-us/azure/foundry/openai/whats-new, which answered 404 (12 of 15).\n- Simplismart (51): Model reading. OpenAPI 3.1 files for seven LLMs and 3.0 or 3.1 files for Whisper, Flux, an LLM metrics call and training jobs. They cover a handful of the 150+ models, omit tools and response_format, show a streaming `delta` as the non-streaming response, and several name hosts on s9t.link instead of api.simplismart.live. The Llama 4 Maverick file gives `llama3_1` as its only model id, and /api-reference/openapi.json is the Flux spec (12 of 25). llms.txt and a Markdown copy of each docs page (10). The docs say when to use shared or dedicated endpoints; model pages carry one-line descriptions (10 of 20). Roles are enums, temperature is bounded 0 to 2, max_tokens 1 to 4096, and messages and model are required (10 of 15). curl and OpenAI-client Python examples. Errors are bare 400, 401 and 500 descriptions, plus an SDK example of a 404 payload (7 of 15). No changelog. Every spec reads version 1.0.0 (2 of 15). 12+10+10+10+7+2 = 51.\n\n## Score by category\n\n| Category | Weight | Azure OpenAI in Foundry Models | Simplismart | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 80 | 15 | Azure OpenAI in Foundry Models +65 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 87 | 51 | Azure OpenAI in Foundry Models +36 |\n| Agent ergonomics | 13% (16.2 this run) | 98 | 17 | Azure OpenAI in Foundry Models +81 |\n| Security \u0026 auth | 14% (17.5 this run) | 95 | 35 | Azure OpenAI in Foundry Models +60 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 25 | Simplismart +5 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 86 | 42 | Azure OpenAI in Foundry Models +44 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 83 | 29 | Azure OpenAI in Foundry Models +54 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **80 · A** | **29.5 · F** | |\n\n## Facts side by side\n\n| Fact | Azure OpenAI in Foundry Models | Simplismart |\n| --- | --- | --- |\n| Kind | Model API | HTTP API |\n| Vendor | Microsoft | Verute Technologies Private Limited |\n| Hosted endpoint | `https://\u003cresource\u003e.openai.azure.com/openai/v1` | `https://api.simplismart.live` |\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 | Proprietary service under the Microsoft Product Terms. The v1 OpenAPI spec is MIT, and the OpenAI SDKs the docs use are Apache-2.0 | Proprietary hosted service. The community Pipecat integration (pipecat-simplismart, by the simpli-smart GitHub account) is BSD-2-Clause; served models keep their own licences. |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-10-26 | 2026-10-19 |\n| Terms last updated | no date given | 2023-01-13 |\n| Privacy policy last updated | 2026-09-01 | 2023-01-13 |\n| Customer content may train models | yes | not found in the text |\n| Terms restrict automated access | yes | yes |\n| Terms restrict benchmarking | yes | not found in the text |\n| Terms or service can change without notice | not found in the text | yes |\n| Arbitration or class-action waiver | not found in the text | yes |\n| Popularity | 454k npm/wk | none |\n\n## How the scores have moved\n\n### Azure OpenAI in Foundry Models\n\nNo score change since it was listed.\n\n### Simplismart\n\n- -0.2 on 30 October 2026: checklist\n\n## Verdicts\n\n**Azure OpenAI in Foundry Models.** An OpenAI-compatible v1 API with a public OpenAPI spec, Entra ID roles in place of shared keys and FedRAMP High scope, with GPT-6 and GPT-6.1 at OpenAI's list prices on Global Standard. Access needs an Azure subscription with a card and a deployment per model, and a 29 September 2026 incident caused about six hours of failures in Sweden Central.\n\n**Simplismart.** Simplismart exposes open models through an OpenAI-compatible endpoint with public per-token prices and expiring, revocable keys. Its public docs give no request rate limits, status page, retry guidance, tool-calling or structured-output reference, and its terms and privacy policy cover the website, not API data.\n\n## Before you call either\n\n### Azure OpenAI in Foundry Models\n\n1. Point the OpenAI SDK at https://\u003cresource\u003e.openai.azure.com/openai/v1/ and pass the deployment name as `model`, not the model id\n2. Prefer Entra ID with the Cognitive Services OpenAI User role over the resource key, which opens the whole data plane\n3. On 429 wait for `retry-after-ms`, then back off with jitter. The OpenAI SDK retries twice by default\n4. Set `store: false` on Responses calls you don't need kept. Stored responses persist 30 days by default\n5. Keep remote MCP approvals on for servers you don't control. They're required by default\n\n### Simplismart\n\n1. Point an OpenAI-compatible client at https://api.simplismart.live with SIMPLISMART_API_KEY as a Bearer token, and take model ids from the marketplace or the per-model API pages.\n2. Set a key expiry and rotate before midnight UTC on the expiry date, because an expired key returns 401.\n3. Cap retries yourself with exponential backoff, because no rate limits, 429 format or retry guidance are published.\n4. Check that a model supports tools or JSON output with a test call before relying on it, because neither is documented.\n5. Confirm the per-token price of the exact model in the console before running large jobs, because the public price list is incomplete.\n\n## Questions\n\n### Which is better for AI agents, Azure OpenAI in Foundry Models or Simplismart?\n\nAzure OpenAI in Foundry Models scores 80 (A) on agent readiness against Simplismart's 29.5 (F), and leads in 6 of 7 scored categories. Simplismart leads on payments \u0026 pricing.\n\n### Do Azure OpenAI in Foundry Models and Simplismart need an API key?\n\nAzure OpenAI in Foundry Models takes an API key or an OAuth sign-in. Simplismart needs an API key.\n\n### Can an agent call Azure OpenAI in Foundry Models and Simplismart without installing anything?\n\nYes. Azure OpenAI in Foundry Models has a hosted endpoint at https://\u003cresource\u003e.openai.azure.com/openai/v1 and Simplismart at https://api.simplismart.live.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/azure-openai-vs-simplismart.json, and with the fewest tokens: https://www.anchorterminal.com/compare/azure-openai-vs-simplismart.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"azure-openai\", \"b\": \"simplismart\"}`. From a terminal: `anchor compare azure-openai simplismart`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/azure-openai.json and https://www.anchorterminal.com/api/v1/tools/simplismart.json\n\n## Other comparisons with Azure OpenAI in Foundry Models or Simplismart\n\n- [Amazon Bedrock vs Azure OpenAI in Foundry Models](https://www.anchorterminal.com/compare/amazon-bedrock-vs-azure-openai.md)\n- [Amazon Bedrock vs Simplismart](https://www.anchorterminal.com/compare/amazon-bedrock-vs-simplismart.md)\n- [Claude API vs Azure OpenAI in Foundry Models](https://www.anchorterminal.com/compare/anthropic-api-vs-azure-openai.md)\n- [Claude API vs Simplismart](https://www.anchorterminal.com/compare/anthropic-api-vs-simplismart.md)\n- [Antseed vs Azure OpenAI in Foundry Models](https://www.anchorterminal.com/compare/antseed-vs-azure-openai.md)\n- [Antseed vs Simplismart](https://www.anchorterminal.com/compare/antseed-vs-simplismart.md)\n- [Azure OpenAI in Foundry Models vs 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Simplismart](https://www.anchorterminal.com/compare/openai-api-vs-simplismart.md)\n- [OpenRouter vs Simplismart](https://www.anchorterminal.com/compare/openrouter-vs-simplismart.md)\n- [Prism Inference vs Simplismart](https://www.anchorterminal.com/compare/prism-inference-vs-simplismart.md)\n- [Router.com (Ramp Router) vs Simplismart](https://www.anchorterminal.com/compare/ramp-router-vs-simplismart.md)\n- [Beam 1.0 vs Simplismart](https://www.anchorterminal.com/compare/reflection-beam-vs-simplismart.md)\n- [RouterPlus vs Simplismart](https://www.anchorterminal.com/compare/routerplus-vs-simplismart.md)\n- [SambaCloud vs Simplismart](https://www.anchorterminal.com/compare/sambanova-vs-simplismart.md)\n- [SiliconFlow vs Simplismart](https://www.anchorterminal.com/compare/siliconflow-vs-simplismart.md)\n- [Simplismart vs StepFun API](https://www.anchorterminal.com/compare/simplismart-vs-stepfun-api.md)\n- [Simplismart vs SWE-2 Flash](https://www.anchorterminal.com/compare/simplismart-vs-swe-2-flash.md)\n- [Simplismart vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/simplismart-vs-tencent-tokenhub.md)\n- [Simplismart vs Together AI Inference](https://www.anchorterminal.com/compare/simplismart-vs-together-inference.md)\n- [Simplismart vs Vercel AI Gateway](https://www.anchorterminal.com/compare/simplismart-vs-vercel-ai-gateway.md)\n- [Simplismart vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/simplismart-vs-vertex-ai.md)\n- [Simplismart vs xAI API (Grok)](https://www.anchorterminal.com/compare/simplismart-vs-xai-api.md)\n- [Nemotron 3.5 Super VL vs Simplismart](https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-simplismart.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-07", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Azure OpenAI in Foundry Models vs Simplismart", "url": "" } ], "description": "Azure OpenAI in Foundry Models scores 80 (A) to Simplismart's 29.5 (F) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Azure OpenAI in Foundry Models A 80", "Simplismart F 29.5", "scores" ], "h1": "Azure OpenAI in Foundry Models vs Simplismart", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/azure-openai-vs-simplismart", "published": "2026-10-01", "section": "tools", "title": "Azure OpenAI in Foundry Models vs Simplismart for AI agents (2026)", "toc": null, "updated": "2026-11-07", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-simplismart" }, "tokens": { "markdown": 7600, "slim": 4030 }, "version": 1 }