{ "data": { "a": { "slug": "groq", "name": "GroqCloud", "vendor": "Groq", "vendorUrl": "https://groq.com", "kind": "model", "category": "inference", "summary": "OpenAI-compatible inference API serving open-weight models on Groq's processors.", "url": "https://www.anchorterminal.com/tools/groq", "markdownUrl": "https://www.anchorterminal.com/tools/groq.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/groq.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/groq.json", "dns": "groq.grade.anchorterminal.com", "repo": "https://github.com/groq/groq-python", "license": "Apache-2.0 (SDKs)", "transports": [ "http" ], "remoteUrl": "https://api.groq.com/openai/v1", "packages": [ { "registry": "pypi", "name": "groq", "main": true }, { "registry": "npm", "name": "groq-sdk", "main": true } ], "auth": "api-key", "authNotes": "Bearer key from the GroqCloud console.", "pricing": "freemium", "pricingNotes": "Free with no card at 30 requests a minute and 1,000 a day. The Developer plan is postpaid by card, bank or SEPA. Cached input half price on gpt-oss only, batch half price (https://groq.com/pricing).", "priceSummary": "from $0.075 / 1M in", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 619, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-26" }, "docsUrl": "https://console.groq.com/docs", "rateLimitsUrl": "https://console.groq.com/docs/rate-limits", "llmsTxt": "https://console.groq.com/llms.txt", "capabilities": [ "inference.llm", "inference.fast", "inference.open-weights" ], "tags": [ "hosted", "model", "fast", "free-tier", "no-card", "llms-txt" ], "lastRelease": "2026-10-15", "graded": true, "anchor": { "graded": true, "score": 74.5, "grade": "BB", "agentReady": true, "rank": 81, "ranked": true, "rankOf": 1510, "categoryRank": 7, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 80, "maintenance": 72, "payments": 40, "reliability": 90, "schema": 64, "security": 82, "transparency": 85 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": 0, "verdict": "Free plan with no card, at 30 requests a minute and 1,000 a day on gpt-oss. Four model shutdown dates between 2026-07-17 and 2026-09-21, with no stated minimum notice.", "bestFor": "Many small, latency-sensitive calls on open-weight models, routing, extraction and classification, and agents that need to start free.", "strengths": [ "Free plan with no card, at 30 requests a minute and 1,000 a day on gpt-oss", "Per-model limits published, `x-ratelimit-*` headers on every response and `retry-after` on 429", "API keys scoped to a project, with per-project request limits, model permissions, request logs and documented rotation", "No retention by default and zero retention as a self-serve setting, and server errors aren't charged", "Strict structured outputs, a half-price Batch API and a 99.9% SLA on the enterprise Performance Tier" ], "weaknesses": [ "Four model shutdown dates between 2026-07-17 and 2026-09-21, with no stated minimum notice", "131,072-token context on every self-serve model", "No OpenAPI document, and the SDK metadata carries no spec URL", "The changelog's newest entry is 18 April 2026; shutdowns since then appear only on the deprecations page", "The deprecations page still names qwen/qwen3.6-27b as a Llama 3.3 70B replacement, and that model shut down on 2026-09-14" ], "agentNotes": [ "Call `/models` at start-up. Four model ids stopped working between July and September", "Read `retry-after` on a 429 and the `x-ratelimit-remaining-tokens` header before the next call", "Free plan allows 8,000 tokens a minute on gpt-oss, so keep prompts small or batch them", "Don't build on Qwen 3.8 27B. It's a preview and previews can go at short notice", "Treat a 498 as Flex capacity and retry later; server errors aren't billed" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 8, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 74.5 } ], "editorialScores": { "ergonomics": 80, "maintenance": 72, "payments": 40, "reliability": 90, "schema": 64, "security": 82, "transparency": 71 }, "provenanceScore": 98, "checked": { "categories": { "ergonomics": "2026-11-01", "maintenance": "2026-11-01", "payments": "2026-11-01", "reliability": "2026-11-01", "schema": "2026-11-01", "security": "2026-11-01", "transparency": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-10-17", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install groq # or: npm i groq-sdk", "http": "curl https://api.groq.com/openai/v1/chat/completions \\\n -H \"Authorization: Bearer $GROQ_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"openai/gpt-oss-120b\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/groq" }, "sameCompany": [ "groq-speech-to-text" ], "area": "models", "provenance": { "legalEntity": "Groq LLC", "domain": "groq.com", "domainRegistered": "2007-07-22", "domainNote": "groq.com was registered in 2007, before Groq existed. Customers in the EEA contract with Groq UK Limited.", "endpointOnVendorDomain": true, "terms": "https://console.groq.com/docs/legal/services-agreement", "privacy": "https://groq.com/privacy-policy", "statusPage": "https://groqstatus.com", "changelog": "https://console.groq.com/docs/changelog", "securityTxt": "valid", "checked": "2026-09-26", "score": 98 }, "requirements": { "setupChecked": true, "complianceChecked": true, "card": "paid-only", "freeTier": true, "minutes": 3, "credential": "api-key", "hosting": [ "US", "global" ], "euResidency": "unknown", "trainsOnData": "no", "zeroRetention": "yes", "certifications": [ "SOC 2 Type II", "GDPR" ], "dpa": true, "selfHost": "no" }, "pageJsonUrl": "https://www.anchorterminal.com/tools/groq.json", "live": { "slug": "groq", "probe": { "target": "https://api.groq.com/openai/v1", "method": "get", "lastAt": "2026-11-07T19:22:10.423334051Z", "lastOk": true, "lastStatus": 404, "lastMs": 229, "authRequired": false, "uptime24h": 100, "uptime30d": 99.99, "p50ms24h": 186, "p95ms24h": 260, "samples24h": 238, "samples30d": 7920, "days": [ { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 250, "ok": 250 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 246 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 252 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 253 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 251, "ok": 251 }, { "date": "2026-10-29", "probes": 248, "ok": 248 }, { "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": 197, "ok": 197 } ] }, "vendorStatus": { "page": "https://groqstatus.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-07T19:22:13.954501753Z" }, "versions": [ { "registry": "github", "name": "groq/groq-python", "version": "v1.7.0", "released": "2026-08-26", "seenAt": "2026-11-06T23:31:31.809829055Z" }, { "registry": "npm", "name": "groq-sdk", "version": "1.6.0", "seenAt": "2026-11-06T23:31:31.775529408Z" }, { "registry": "pypi", "name": "groq", "version": "1.7.0", "released": "2026-08-26", "seenAt": "2026-11-06T23:31:31.611879739Z" } ], "githubStars": 621, "npmWeekly": 1222883, "pypiWeekly": 4097095, "securityTxt": { "url": "https://groq.com/.well-known/security.txt", "state": "valid", "checkedAt": "2026-11-07T16:00:16.152471691Z" }, "llmsTxt": { "url": "https://console.groq.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:41.146613733Z" }, "domain": { "domain": "groq.com", "registered": "2007-07-22", "source": "https://rdap.verisign.com/com/v1/domain/groq.com", "checkedAt": "2026-11-01T14:20:57.072575938Z" }, "pages": [ { "url": "https://console.groq.com/docs/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-10-08T18:16:39.832959533Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "ac2cb86ef05a" }, { "url": "https://console.groq.com/docs/deprecations", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:01:57.602612544Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "396a91396e58" }, { "url": "https://groq.com/pricing", "kind": "pricing", "status": 304, "checkedAt": "2026-11-07T14:10:39.551314733Z", "changedAt": "2026-10-09T18:39:57.403860983Z", "fingerprint": "5d2ffa871aea" }, { "url": "https://groq.com/privacy-policy", "kind": "privacy", "status": 304, "checkedAt": "2026-10-08T18:20:46.366514564Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "05dc8da7bc78" }, { "url": "https://console.groq.com/docs/legal/services-agreement", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:16:44.104843177Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "a0b93cf713d4" } ], "models": { "openai/gpt-oss-120b": { "openrouterId": "openai/gpt-oss-120b", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "text" ], "promptCaching": true, "contextTokens": 131072, "maxOutput": 65536, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "openai/gpt-oss-20b": { "openrouterId": "openai/gpt-oss-20b", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "text" ], "promptCaching": true, "contextTokens": 131072, "maxOutput": 117964, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "qwen/qwen3.8-27b": { "openrouterId": "qwen/qwen3.8-27b", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "image", "text", "video" ], "promptCaching": true, "contextTokens": 1024000, "maxOutput": 235929, "checkedAt": "2026-11-06T23:49:25.225778715Z" } }, "github": { "repo": "groq/groq-python", "checkedAt": "2026-11-02T08:53:10.526782877Z", "stars": 622, "archived": false, "issuesEnabled": false, "pushedAt": "2026-09-04T20:45:45Z", "openIssues": 0, "openPRs": 1, "issuesOpened90d": 0, "issuesClosed90d": 0, "prsMerged90d": 2, "ci": { "branch": "main", "runs": 41, "success": 31, "failure": 2, "other": 8, "latest": "skipped", "latestAt": "2026-09-04T20:45:23Z" } }, "updatedAt": "2026-11-07T19:22:10.423334051Z" } }, "answer": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against GroqCloud's 74.5 (BB), and leads in 6 of 7 scored categories.", "b": { "slug": "vertex-ai", "name": "Vertex AI (Gemini Enterprise Agent Platform)", "vendor": "Google Cloud", "vendorUrl": "https://cloud.google.com/products/gemini-enterprise-agent-platform", "kind": "model", "category": "inference", "summary": "Google Cloud's enterprise API for Gemini models and Model Garden partner and open models, including Claude, Llama and Qwen, on the platform formerly called Vertex AI, with regional endpoints, provisioned throughput and IAM access.", "url": "https://www.anchorterminal.com/tools/vertex-ai", "markdownUrl": "https://www.anchorterminal.com/tools/vertex-ai.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/vertex-ai.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/vertex-ai.json", "dns": "vertex-ai.grade.anchorterminal.com", "repo": "https://github.com/googleapis/python-genai", "license": "Proprietary service under the Google Cloud Platform terms. Partner models carry their providers' terms. The Google Gen AI SDKs are Apache-2.0", "transports": [ "http" ], "remoteUrl": "https://aiplatform.googleapis.com/v1", "packages": [ { "registry": "pypi", "name": "google-genai" }, { "registry": "npm", "name": "@google/genai" }, { "registry": "pypi", "name": "google-cloud-aiplatform" } ], "auth": "mixed", "authNotes": "OAuth 2.0 bearer tokens from application default credentials or service accounts, with IAM roles on a Google Cloud project, which the docs recommend for production. Google Cloud API keys, including express mode keys (Preview) that need no project or location in the URL, are recommended for testing. The OpenAI-compatible endpoint accepts only Google Cloud auth.", "pricing": "usage", "pricingNotes": "Per-token prices per model, with global and higher non-global rates and Standard, Priority and Flex/Batch options. Gemini 4 Argon costs $4/$20 per million, with 50% credits back through 2026-11-30. Gemini 3.8 Flash costs $0.75/$3.75 through 2026-12-31, then $1.50/$7.50. Claude Opus 5.5 $4/$20. Batch and Flex half price on Gemini. Only requests that return 200 are charged. Express mode gives new users 90 days free with no billing details (https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing).", "priceSummary": "from $0.30 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 on the pricing page or in the express mode docs (checked 2026-11-06). Access needs a Google account and, after the express trial, a Cloud billing account.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": 20917051, "asOf": "2026-11-06" }, "docsUrl": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/start", "rateLimitsUrl": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/standard-paygo", "openapi": "https://aiplatform.googleapis.com/$discovery/rest?version=v1", "capabilities": [ "inference.llm", "inference.open-weights" ], "tags": [ "official", "hosted", "model", "open-weights", "enterprise", "usage-priced", "free-tier", "no-card", "python", "typescript", "status-page" ], "lastRelease": "2026-11-05", "graded": true, "anchor": { "graded": true, "score": 88.7, "grade": "AA", "agentReady": true, "rank": 2, "ranked": true, "rankOf": 1510, "categoryRank": 1, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 97, "maintenance": 91, "payments": 40, "reliability": 99, "schema": 94, "security": 97, "transparency": 90 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": 0, "verdict": "Gemini, Claude and open models through one Google Cloud API with IAM, regional endpoints, schema-guaranteed JSON and a 99.5 per cent SLA, and no Vertex incident on the Cloud status page in the 90 days to 6 November 2026. Gemini 4 Argon and 3.8 Flash have 12-month availability, but short-term Flash models get a 95 per cent SLA.", "bestFor": "Google Cloud customers who need Gemini with residency, VPC-SC, provisioned throughput and an SLA, or Claude under a Google bill.", "strengths": [ "No incident naming Vertex or Gemini on status.cloud.google.com between 8 July and 6 October 2026", "Public discovery document for the v1 API (revision 20260930) with 1,128 methods and 1,493 schemas", "JSON output follows a supplied `response_schema` when `response_mime_type` is `application/json`", "Express mode gives new users 90 days free with an API key and no billing details", "Only requests that return 200 are charged", "Gemini 4 Argon, GA since 13 October 2026, has a 1,048,576-token unified limit at $4/$20 per million tokens" ], "weaknesses": [ "The SLA drops from 99.5 to 95 per cent for short-term availability models, Gemini 3.7 and 3.6 Flash among them", "Gemini 3.6 Flash, released 21 July 2026, retires on 19 November 2026, and 3.7 Flash on 28 January 2027", "Zero retention needs an exception request, and flagged prompts may be kept up to 90 days", "The Interactions API stores prompts and responses unless `store` is set to false", "No llms.txt on the docs site, and the docs mix the Vertex AI and Agent Platform names", "The model versions table still listed Gemini 2.5 Pro, Flash and Flash-Lite as available after their 20 October 2026 retirement date" ], "agentNotes": [ "Call the `global` location on aiplatform.googleapis.com unless data residency requires a Region. The 429 guide says it spreads load and reduces errors", "Back off exponentially on 429 `Resource exhausted`. On Standard PayGo it means shared capacity is busy, not a fixed quota", "Send `response_schema` with `response_mime_type: application/json`. JSON mode alone doesn't guarantee valid output", "Set `store` to false on Interactions API calls. It defaults to true and keeps prompts and responses", "Pin `gemini-4-argon` or `gemini-3.8-flash`, both in the 12-month tier, rather than 3.7 or 3.6 Flash, which retire on 28 January 2027 and 19 November 2026" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "AA", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 88.7 } ], "editorialScores": { "ergonomics": 97, "maintenance": 91, "payments": 40, "reliability": 99, "schema": 94, "security": 97, "transparency": 83 }, "provenanceScore": 97, "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 google-genai # or: npm i @google/genai", "http": "curl -X POST \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n \"https://aiplatform.googleapis.com/v1/projects/${GOOGLE_CLOUD_PROJECT}/locations/global/publishers/google/models/gemini-3.8-flash:generateContent\" \\\n -d '{\"contents\":{\"role\":\"user\",\"parts\":{\"text\":\"Explain how AI works in a few words\"}}}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/vertex-ai" }, "sameCompany": [ "gemini-api", "gemini-embedding", "vertex-ai-tuning", "cloud-run-gpus", "google-model-armor", "google-imagen", "google-veo", "google-lyria", "google-speech-to-text", "google-cloud-tts", "gemini-live", "google-adk", "google-memory-bank", "google-secret-manager", "google-cloud-document-ai", "google-weather-api", "chrome-devtools-mcp", "google-maps-platform", "google-cloud-translation", "google-calendar-api", "firebase-cloud-messaging", "google-cloud-storage", "google-drive-api", "gemini-enterprise", "gemini-cli", "google-search-console", "google-ads-api", "google-forms", "google-sheets-api", "gmail-api", "google-people-api" ], "area": "models", "unitPrices": [ { "item": "Grounding with Google Search over 5,000 a month (Gemini 3)", "unit": "1k-requests", "usd": 14 } ], "provenance": { "legalEntity": "Google LLC", "domain": "google.com", "domainRegistered": "1997-09-15", "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.", "endpointOnVendorDomain": true, "terms": "https://cloud.google.com/terms", "privacy": "https://policies.google.com/privacy", "statusPage": "https://status.cloud.google.com", "changelog": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/release-notes", "securityTxt": "valid", "checked": "2026-11-06", "notes": [ "The product runs under the Google Cloud Platform terms, whose contracting Google entity depends on the billing country, as for vertex-ai-tuning.", "www.google.com/.well-known/security.txt expires on 2030-04-01 (read 2026-11-06).", "docs.cloud.google.com has no llms.txt (404 on 6 October 2026), but each docs page answers as Markdown when `.md.txt` is added to its URL." ], "score": 97 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/vertex-ai.json", "live": { "slug": "vertex-ai", "probe": { "target": "https://aiplatform.googleapis.com/v1", "method": "get", "lastAt": "2026-11-07T19:22:43.041024218Z", "lastOk": true, "lastStatus": 404, "lastMs": 664, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 118, "p95ms24h": 752, "samples24h": 210, "samples30d": 210, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 13 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "versions": [ { "registry": "github", "name": "googleapis/python-genai", "version": "v3.0.0", "released": "2026-11-06", "seenAt": "2026-11-07T00:15:33.519504561Z" }, { "registry": "npm", "name": "@google/genai", "version": "3.0.0", "seenAt": "2026-11-07T00:15:31.639429892Z" }, { "registry": "pypi", "name": "google-cloud-aiplatform", "version": "2.7.0", "released": "2026-10-28", "seenAt": "2026-11-07T00:15:32.521225598Z" }, { "registry": "pypi", "name": "google-genai", "version": "3.0.0", "released": "2026-11-06", "seenAt": "2026-11-07T00:15:31.485691571Z" } ], "githubStars": 4057, "npmWeekly": 31989360, "pypiWeekly": 38431674, "securityTxt": { "url": "https://google.com/.well-known/security.txt", "state": "valid", "expires": "2030-04-01T00:00:00z", "checkedAt": "2026-11-07T16:01:22.068349931Z" }, "pages": [ { "url": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:01:16.257808105Z", "changedAt": "2026-11-07T14:01:16.257808105Z", "fingerprint": "a4fe253bd23f" } ], "updatedAt": "2026-11-07T19:22:43.041024218Z" } }, "edges": [ { "by": 30, "category": "schema", "leads": "vertex-ai", "name": "Schema \u0026 documentation", "reasons": { "a": "No OpenAPI document in the docs, and the SDK's .stats.yml carries only an endpoint count of 17, no spec URL (0). llms.txt at console.groq.com/llms.txt (10). Reference not read in full this run (15 of 20). Structured outputs have a Strict mode as well as Best-effort (15). The errors page lists 15 status codes with recovery advice, including 498 for Flex capacity and 424 for remote MCP auth, and the `error` object with `message` and `type` (14 of 15). The changelog at console.groq.com/docs/changelog, read on 1 November, is dated, but its newest entry is 18 April 2026 and it doesn't record the four shutdowns since July, which only the deprecations page carries (10 of 15).", "b": "Model reading. Keyless discovery document for the Agent Platform API v1, revision 20260930, with 1,128 methods and 1,493 schemas (25). No llms.txt, but every docs page answers as Markdown at `.md.txt`, counted as for vertex-ai-tuning (10). Model cards give limits, capabilities and retirement dates, and the 3.8 Flash card says when to use 3.7 Flash instead, but the docs mix the Vertex AI and Agent Platform names, and the OpenAI page tells new users to call the Gemini Developer API instead (16 of 20). Structured output with `response_schema` and `response_mime_type: application/json` always follows the schema per the docs, and complex schemas fail with a 400 (15). Examples on every page and a 429 guide, but we found no full error code list for generateContent (13 of 15). Versioned v1 and v1beta1 APIs and release notes with entries on most working days, newest 5 November 2026 (15)." }, "scores": { "a": 64, "b": 94 } }, { "by": 19, "category": "maintenance", "leads": "vertex-ai", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading. Meta's Muse Spark Open went into limited preview on GroqCloud on 15 October 2026, per Groq's announcement, 17 days before this check, and the models page lists it as an enterprise model (30). The newest shutdown is still `groq/compound` and `compound-mini` on 21 September. The changelog's newest entry is 18 April 2026, the Python SDK 1.7.0 (tagged 25 August) and TypeScript SDK 1.6.0 are still the newest tags, with commits to 4 September and no release since, and the deprecations page has no announcement after 24 August. Production models get an email and a migration path, previews may go at short notice, and no minimum period is stated. Llama 3.1 8B and 3.3 70B got 60 days on the free and developer tiers, Compound 28 days with no replacement named (4 of 12). Three shutdown dates in the 90 days to 1 November, 16 August, 14 September and 21 September. Three in 90 days is still frequent churn (0 of 8). A dated changelog with an RSS feed and a community forum exist, but the changelog's newest entry is 18 April 2026; support replies not checked (10 of 15). SDK issue replies not sampled, since GitHub's API refused our shell (5 of 10). Python SDK 1.7.0 and TypeScript SDK 1.6.0, both published on 26 August 2026 and still the newest tags on 1 November (15). CI, release-please and lock-file vulnerability fixes in 1.7.0 (8 of 10). 30+4+0+10+5+15+8 = 72.", "b": "Model reading. Gemini Omni 1.2 Flash GA on 5 November, Grok 4.7 GA on 27 October, Gemini 4 Argon GA on 13 October and Claude Haiku 5.5 on 7 October 2026 (30). Stable models stay at least 12 months, and since 6 October Gemini 4 Argon and Gemini 3.8 Flash are in that tier, while 3.7 and 3.6 Flash remain short-term with at least 45 days' notice once a date is set (10 of 12, up from 8). Gemini 2.5 Pro, Flash and Flash-Lite were due to retire on 20 October, three models on one date after more than 12 months, with replacements named. We didn't check partner model retirements (6 of 8, down from 8). Dated release notes on most working days, newest 5 November, and Cloud Customer Care (15). python-genai replies not sampled this run (5 of 10). Current SDKs, google-genai 3.0.0 on 6 November and `@google/genai` 3.0.0, and google-cloud-aiplatform 2.7.0 on 28 October 2026 (15). Supported runtimes stated, Python 3.10 or later and Node 20 or later, per the 6 October check (10). Unchanged at 91." }, "scores": { "a": 72, "b": 91 } }, { "by": 17, "category": "ergonomics", "leads": "vertex-ai", "name": "Agent ergonomics", "reasons": { "a": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use documented, parallel calls and forced choice not checked (15 of 20). Strict structured outputs (15). Automatic prompt caching at 50% off cached input, with a 2-hour cache life, on gpt-oss-20b, gpt-oss-120b and gpt-oss-safeguard-20b only (10 of 15). 131,072-token context on every self-serve model; only MiniMax M2.7, a preview on enterprise pricing, reaches 196,608 (5 of 15). Batch at half price (10). Official SDKs for Python and JavaScript (10). Errors page with codes, recovery advice and a typed error object (15).", "b": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with `FunctionCallingConfig` mode ANY and `allowedFunctionNames` to force a named tool (20). Schema-guaranteed JSON output (15). Implicit and explicit context caching, cached input at 0.1x, $0.075 per million on 3.8 Flash through 31 December (15). 1,048,576-token context on 3.8 Flash, a unified 1,048,576-token input and output limit on Gemini 4 Argon, and 1,000,000 on Claude Opus 5.5 (15). Flex/Batch at half price on Gemini (10). Gen AI SDKs for Python and JavaScript among others, and the OpenAI libraries work with Google Cloud auth (10). 429 handling documented per quota framework, with standard Google status codes elsewhere (12 of 15)." }, "scores": { "a": 80, "b": 97 } }, { "by": 15, "category": "security", "leads": "vertex-ai", "name": "Security \u0026 auth", "reasons": { "a": "Model reading. Bearer keys scoped to a project, with custom request limits per project and model permissions at organisation and project level. The security onboarding page advises rotating keys periodically and revoking them at once if compromise is suspected (30). The services agreement, read first-hand on 6 October and not re-read on 17 October, says Groq is not permitted to use inputs or outputs for training unless the customer grants permission (20). No retention by default, up to 30 days for reliability and abuse monitoring, and zero retention as a setting any customer can turn on in Data Controls, per the data page (15). Request logs, usage per project and a read-only Reader role (12 of 15). groq.com's security.txt holds a Contact line and nothing else, and the trust centre at trust.groq.com is a Vanta page that renders only with JavaScript, so certifications and any bug bounty are unchecked (5 of 20).", "b": "Model reading. OAuth 2.0 with IAM roles and service accounts, which the docs recommend for production, with API keys for testing. No key in a query string found in the docs we read (30). The training restriction in the Service Specific Terms says Google won't use customer data to train or fine-tune any model without permission, GA and pre-GA alike (20). Default in-memory caching for 24 hours that can be disabled, flagged prompts logged up to 90 days unless an abuse-monitoring exception is granted or the customer has a Cloud Master Agreement, the Interactions API storing by default, and Claude Fable 5 and Mythos 5 requiring 30-day retention shared with Anthropic (12 of 15, a judgement call matching the Fable exception on the Anthropic and Bedrock listings). Cloud Audit Logs, with Data Access logs off until enabled, and optional request-response logging to BigQuery (15). google.com security.txt valid to 2030-04-01, the Google VRP, and Generative AI on the platform in ISO 27001, 27017 and 27018 and SOC 1, 2 and 3 scope per the 1 October services-in-scope check for vertex-ai-tuning, not re-read on 6 October (20)." }, "scores": { "a": 82, "b": 97 } }, { "by": 9, "category": "reliability", "leads": "vertex-ai", "name": "Reliability", "reasons": { "a": "Status page at groqstatus.com on incident.io, with components for the API, each production and preview model and the website (20). The page's RSS feed and the incident pages it links, read on 1 November, list three incidents in the 90-day window, all in October, and nothing since 12 October apart from a console maintenance on 18 October. On 10 October the enterprise-only preview MiniMax M2.7 had failed requests from data centre hardware issues from 10:09 to 16:14 UTC. The incident's header and impact record say degraded performance, while its Identified update set the model to partial outage and warned of failed requests. On 12 October console.groq.com returned 500 errors on metrics, /models and key creation from 03:17 to 03:38, and the vendor said inference requests were not affected. On 12 October MiniMax M2.7 had a partial outage from 18:17 to 18:36. None took down a core API for an hour, since the six-hour incident hit one enterprise preview model that the status page describes as for evaluation only, so minor incidents only (20). A stricter reading would count it as a major and give 10. Limits published per model, 30 requests a minute, 1,000 a day and 8,000 tokens a minute on gpt-oss for the free plan, re-read on 1 November (15). 429 carries `retry-after`, `x-ratelimit-*` headers come on every response, and the errors page says to wait before retrying a 503 and that a 502 may clear on retry (15). The Performance Tier, enterprise only, lists a 99.9% availability SLA with details in the offline agreement, per the 6 October check, not re-read on 1 November (10). gpt-oss and Whisper models are production, Qwen 3.8 27B, MiniMax M2.7 and Muse Spark Open are previews (10). 20+20+15+15+10+10 = 90.", "b": "Google Cloud status dashboard with per-product history (20). Its incident feed, read on 6 November, has no incident naming Vertex Gemini API, Gemini or the Agent Platform in the 90 days. The regional incidents of 20 August, 1 September and 20 October (europe-west2) and the Cloud Storage incident of 8 and 9 October didn't list it among affected products (30). Standard PayGo publishes usage tiers by 30-day spend with baseline throughput, 2,000,000 to 50,000,000 tokens a minute for Flash models and 500,000 to 10,000,000 for Pro, plus quota tables for other methods (15). A 429 page says to use the global endpoint and truncated exponential backoff, and a retry-strategy page (15). SLA of 99.5 per cent a month on generateContent and streamGenerateContent, 95 per cent for short-term availability models, and a latency SLO on Provisioned Throughput. The 429 page says pay-as-you-go 429s don't count toward the SLA error rate (10). Gemini 4 Argon reached GA on 13 October and Gemini 3.8 Flash moved to the 12-month tier, and the v1 API, 3.5 Flash-Lite and Claude Opus 5.5 are GA, but express mode was Preview at the 6 October check (9 of 10, up from 8). Up from 98 on 6 October." }, "scores": { "a": 90, "b": 99 } }, { "by": 5, "category": "transparency", "leads": "vertex-ai", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed service with clear terms, SDKs Apache-2.0 (15). The data page agrees with the listing on retention and adds detail, batch files kept 30 days unless deleted, fine-tuning data kept until the customer deletes it, and SCCs for transfers. The services agreement bars training on inputs and outputs, and the DPA gives 15 days' notice of sub-processor changes to subscribers, both read on 6 October. The agreement extends zero retention to 'Eligible Customers' where the data page says all customers may enable it (26 of 30). Deprecations page with announcement and shutdown dates (20). All customer data in Google Cloud buckets in the US, per the data page. The DPA points to a sub-processor list at trust.groq.com/subprocessors, which needs JavaScript and wasn't read (10 of 20).", "b": "Closed service under the Google Cloud terms, with the Gen AI SDKs under Apache-2.0 and a public discovery document (18, as for Bedrock's open service models). The zero data retention and abuse monitoring pages agree with the Cloud terms and give periods for each case, 24 hours in memory, up to 90 days for flagged prompts, 30 days for Advanced AI models, 3 days for Search grounding logs and 30 days for Maps grounding, and say which can't be switched off (27 of 30). A dated model versions table with release and retirement dates and a stated 12-month or 45-day policy, but on 6 November it still listed Gemini 2.5 Pro, Flash and Flash-Lite as available with a 20 October 2026 retirement date and left them out of the retired table (18 of 20, down from 20). The Cloud subprocessor list with locations, per the 1 October vertex-ai-tuning check, regional and multi-region endpoints, and per-model ML processing locations on the partner model cards (20)." }, "scores": { "a": 85, "b": 90 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Groq", "b": "Google Cloud", "name": "Vendor" }, { "a": "https://api.groq.com/openai/v1", "b": "https://aiplatform.googleapis.com/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "OAuth or key", "name": "Auth" }, { "a": "Freemium", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0 (SDKs)", "b": "Proprietary service under the Google Cloud Platform terms. Partner models carry their providers' terms. The Google Gen AI SDKs are Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-10-15", "b": "2026-11-05", "name": "Last release" }, { "a": "2026-06-22", "b": "2026-09-02", "name": "Terms last updated" }, { "a": "2025-11-12", "b": "2026-10-01", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "yes", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "619 stars", "b": "20.9M PyPI/wk", "name": "Popularity" }, { "a": "3.5/5 (8)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against GroqCloud's 74.5 (BB), and leads in 6 of 7 scored categories.", "question": "Which is better for AI agents, GroqCloud or Vertex AI (Gemini Enterprise Agent Platform)?" }, { "answer": "GroqCloud needs an API key. Vertex AI (Gemini Enterprise Agent Platform) takes an API key or an OAuth sign-in.", "question": "Do GroqCloud and Vertex AI (Gemini Enterprise Agent Platform) need an API key?" }, { "answer": "Yes. GroqCloud has a hosted endpoint at https://api.groq.com/openai/v1 and Vertex AI (Gemini Enterprise Agent Platform) at https://aiplatform.googleapis.com/v1.", "question": "Can an agent call GroqCloud and Vertex AI (Gemini Enterprise Agent Platform) without installing anything?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Many small, latency-sensitive calls on open-weight models, routing, extraction and classification, and agents that need to start free.", "slug": "groq", "watchFor": "Four model shutdown dates between 2026-07-17 and 2026-09-21, with no stated minimum notice" }, { "aheadOn": [ "Reliability, 99 against 90", "Schema \u0026 documentation, 94 against 64", "Agent ergonomics, 97 against 80", "Security \u0026 auth, 97 against 82", "Maintenance \u0026 community, 91 against 72", "Transparency \u0026 trust, 90 against 85" ], "also": null, "goodFor": "Google Cloud customers who need Gemini with residency, VPC-SC, provisioned throughput and an SLA, or Claude under a Google bill.", "slug": "vertex-ai", "watchFor": "The SLA drops from 99.5 to 95 per cent for short-term availability models, Gemini 3.7 and 3.6 Flash among them" } ], "history": { "groq": [ { "causes": [ "reliability", "evidence" ], "date": "2026-10-17", "delta": -1.1, "from": 75.6, "fromGrade": "BB", "to": 74.5, "toGrade": "BB" }, { "causes": [ "method" ], "date": "2026-10-08", "delta": -0.1, "from": 75.7, "fromGrade": "BB", "to": 75.6, "toGrade": "BB" } ], "vertex-ai": null }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-groq.json", "title": "Amazon Bedrock vs GroqCloud", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-vertex-ai.json", "title": "Amazon Bedrock vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-groq.json", "title": "Claude API vs GroqCloud", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-vertex-ai.json", "title": "Claude API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-groq.json", "title": "Antseed vs GroqCloud", "url": "https://www.anchorterminal.com/compare/antseed-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-vertex-ai.json", "title": "Antseed vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/antseed-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-groq.json", "title": "Azure OpenAI in Foundry Models vs GroqCloud", "url": 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(Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-groq.json", "title": "DeepSeek API vs GroqCloud", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-vertex-ai.json", "title": "DeepSeek API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-groq.json", "title": "Eden AI vs GroqCloud", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-vertex-ai.json", "title": "Eden AI vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-groq.json", "title": "Glimpse vs GroqCloud", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-vertex-ai.json", "title": "Glimpse vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-groq.json", "title": "Fireworks AI Inference vs GroqCloud", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-vertex-ai.json", "title": "Fireworks AI Inference vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-groq.json", "title": "Gemini Developer API vs GroqCloud", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-vertex-ai.json", "title": "Gemini Developer API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-hugging-face-inference-providers.json", "title": "GroqCloud vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/groq-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-inkling-2-small.json", "title": "GroqCloud vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/groq-vs-inkling-2-small" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-jais-3.json", "title": "GroqCloud vs Jais 3", "url": "https://www.anchorterminal.com/compare/groq-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-meta-model-api.json", "title": "GroqCloud vs Meta Model API", "url": "https://www.anchorterminal.com/compare/groq-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-minimax-api.json", "title": "GroqCloud vs MiniMax API", "url": "https://www.anchorterminal.com/compare/groq-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-mistral-api.json", "title": "GroqCloud vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/groq-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-muse-spark-open.json", "title": "GroqCloud vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/groq-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-nemotron-3-5-super-vl.json", "title": "GroqCloud vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/groq-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-novita-ai.json", "title": "GroqCloud vs Novita AI", "url": "https://www.anchorterminal.com/compare/groq-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-openai-api.json", "title": "GroqCloud vs OpenAI API", "url": "https://www.anchorterminal.com/compare/groq-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-openrouter.json", "title": "GroqCloud vs OpenRouter", "url": "https://www.anchorterminal.com/compare/groq-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-prism-inference.json", "title": "GroqCloud vs Prism Inference", "url": "https://www.anchorterminal.com/compare/groq-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-ramp-router.json", "title": "GroqCloud vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/groq-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-reflection-beam.json", "title": "GroqCloud vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/groq-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-routerplus.json", "title": "GroqCloud vs RouterPlus", "url": "https://www.anchorterminal.com/compare/groq-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-sambanova.json", "title": "GroqCloud vs SambaCloud", "url": "https://www.anchorterminal.com/compare/groq-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-siliconflow.json", "title": "GroqCloud vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/groq-vs-siliconflow" }, { "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/groq-vs-stepfun-api.json", "title": "GroqCloud vs StepFun API", "url": "https://www.anchorterminal.com/compare/groq-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-swe-2-flash.json", "title": "GroqCloud vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/groq-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-tencent-tokenhub.json", "title": "GroqCloud vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/groq-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-together-inference.json", "title": "GroqCloud vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/groq-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-vercel-ai-gateway.json", "title": "GroqCloud vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/groq-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-xai-api.json", "title": "GroqCloud vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/groq-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-vertex-ai.json", "title": "Hugging Face Inference Providers vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-vertex-ai.json", "title": "Inkling 2 Small vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-vertex-ai.json", "title": "Jais 3 vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/jais-3-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-vertex-ai.json", "title": "Meta Model API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-vertex-ai.json", "title": "MiniMax API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-vertex-ai.json", "title": "Mistral AI API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-vertex-ai.json", "title": "Muse Spark Open vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/novita-ai-vs-vertex-ai.json", "title": "Novita AI vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/novita-ai-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-vertex-ai.json", "title": "OpenAI API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/openai-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/openrouter-vs-vertex-ai.json", "title": "OpenRouter vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/openrouter-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-vertex-ai.json", "title": "Prism Inference vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/ramp-router-vs-vertex-ai.json", "title": "Router.com (Ramp Router) vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/ramp-router-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/reflection-beam-vs-vertex-ai.json", "title": "Beam 1.0 vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/routerplus-vs-vertex-ai.json", "title": "RouterPlus vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/routerplus-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-vertex-ai.json", "title": "SambaCloud vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/sambanova-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai.json", "title": "SiliconFlow vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai" }, { "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/stepfun-api-vs-vertex-ai.json", "title": "StepFun API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/stepfun-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/swe-2-flash-vs-vertex-ai.json", "title": "SWE-2 Flash vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/swe-2-flash-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/tencent-tokenhub-vs-vertex-ai.json", "title": "Tencent Cloud TokenHub vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/tencent-tokenhub-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/together-inference-vs-vertex-ai.json", "title": "Together AI Inference vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/together-inference-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/vercel-ai-gateway-vs-vertex-ai.json", "title": "Vercel AI Gateway vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/vercel-ai-gateway-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/vertex-ai-vs-xai-api.json", "title": "Vertex AI (Gemini Enterprise Agent Platform) vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/vertex-ai-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-vertex-ai.json", "title": "Nemotron 3.5 Super VL vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-vertex-ai" } ], "panel": null, "scores": [ { "by": 9, "edge": "vertex-ai", "groq": 90, "key": "reliability", "name": "Reliability", "vertex-ai": 99, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 30, "edge": "vertex-ai", "groq": 64, "key": "schema", "name": "Schema \u0026 documentation", "vertex-ai": 94, "weight": 13 }, { "by": 17, "edge": "vertex-ai", "groq": 80, "key": "ergonomics", "name": "Agent ergonomics", "vertex-ai": 97, "weight": 13 }, { "by": 15, "edge": "vertex-ai", "groq": 82, "key": "security", "name": "Security \u0026 auth", "vertex-ai": 97, "weight": 14 }, { "by": 0, "edge": "", "groq": 40, "key": "payments", "name": "Payments \u0026 pricing", "vertex-ai": 40, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 19, "edge": "vertex-ai", "groq": 72, "key": "maintenance", "name": "Maintenance \u0026 community", "vertex-ai": 91, "weight": 7 }, { "by": 5, "edge": "vertex-ai", "groq": 85, "key": "transparency", "name": "Transparency \u0026 trust", "vertex-ai": 90, "weight": 7 } ], "summary": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against GroqCloud's 74.5 (BB), and leads in 6 of 7 scored categories. Both do llm inference.", "tasks": [ { "a": "$0.85", "b": "$22.03", "cheaper": "groq", "task": "Research a product and write a sourced report", "what": "openai/gpt-oss-120b on GroqCloud, gemini-3.1-pro-preview on Vertex AI (Gemini Enterprise Agent Platform)" }, { "a": "$0.21", "b": "$5.09", "cheaper": "groq", "task": "Build a small four-page website", "what": "openai/gpt-oss-120b on GroqCloud, gemini-3.1-pro-preview on Vertex AI (Gemini Enterprise Agent Platform)" }, { "a": "$0.05", "b": "$1.09", "cheaper": "groq", "task": "Find and fix a bug in a large codebase", "what": "openai/gpt-oss-120b on GroqCloud, gemini-3.1-pro-preview on Vertex AI (Gemini Enterprise Agent Platform)" } ], "tutorials": [], "verdicts": { "groq": "Free plan with no card, at 30 requests a minute and 1,000 a day on gpt-oss. Four model shutdown dates between 2026-07-17 and 2026-09-21, with no stated minimum notice.", "vertex-ai": "Gemini, Claude and open models through one Google Cloud API with IAM, regional endpoints, schema-guaranteed JSON and a 99.5 per cent SLA, and no Vertex incident on the Cloud status page in the 90 days to 6 November 2026. Gemini 4 Argon and 3.8 Flash have 12-month availability, but short-term Flash models get a 95 per cent SLA." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/groq-vs-vertex-ai", "json": "https://www.anchorterminal.com/compare/groq-vs-vertex-ai.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/groq-vs-vertex-ai.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/groq-vs-vertex-ai.min.md" }, "markdown": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against GroqCloud's 74.5 (BB), and leads in 6 of 7 scored categories. Both do llm inference.\n\n- GroqCloud: grade BB, 74.5/100, rank #81 of 1510. Markdown https://www.anchorterminal.com/tools/groq.md · JSON https://www.anchorterminal.com/api/v1/tools/groq.json\n- Vertex AI (Gemini Enterprise Agent Platform): grade AA, 88.7/100, rank #2 of 1510. Markdown https://www.anchorterminal.com/tools/vertex-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/vertex-ai.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### GroqCloud (BB)\n\nGood for: Many small, latency-sensitive calls on open-weight models, routing, extraction and classification, and agents that need to start free.\n\nWatch for: Four model shutdown dates between 2026-07-17 and 2026-09-21, with no stated minimum notice\n\n### Vertex AI (Gemini Enterprise Agent Platform) (AA)\n\nGood for: Google Cloud customers who need Gemini with residency, VPC-SC, provisioned throughput and an SLA, or Claude under a Google bill.\n\nAhead on:\n- Reliability, 99 against 90\n- Schema \u0026 documentation, 94 against 64\n- Agent ergonomics, 97 against 80\n- Security \u0026 auth, 97 against 82\n- Maintenance \u0026 community, 91 against 72\n- Transparency \u0026 trust, 90 against 85\n\nWatch for: The SLA drops from 99.5 to 95 per cent for short-term availability models, Gemini 3.7 and 3.6 Flash among them\n\n\n## Why the edge\n\n### Schema \u0026 documentation: Vertex AI (Gemini Enterprise Agent Platform) +30\n\n- GroqCloud (64): No OpenAPI document in the docs, and the SDK's .stats.yml carries only an endpoint count of 17, no spec URL (0). llms.txt at console.groq.com/llms.txt (10). Reference not read in full this run (15 of 20). Structured outputs have a Strict mode as well as Best-effort (15). The errors page lists 15 status codes with recovery advice, including 498 for Flex capacity and 424 for remote MCP auth, and the `error` object with `message` and `type` (14 of 15). The changelog at console.groq.com/docs/changelog, read on 1 November, is dated, but its newest entry is 18 April 2026 and it doesn't record the four shutdowns since July, which only the deprecations page carries (10 of 15).\n- Vertex AI (Gemini Enterprise Agent Platform) (94): Model reading. Keyless discovery document for the Agent Platform API v1, revision 20260930, with 1,128 methods and 1,493 schemas (25). No llms.txt, but every docs page answers as Markdown at `.md.txt`, counted as for vertex-ai-tuning (10). Model cards give limits, capabilities and retirement dates, and the 3.8 Flash card says when to use 3.7 Flash instead, but the docs mix the Vertex AI and Agent Platform names, and the OpenAI page tells new users to call the Gemini Developer API instead (16 of 20). Structured output with `response_schema` and `response_mime_type: application/json` always follows the schema per the docs, and complex schemas fail with a 400 (15). Examples on every page and a 429 guide, but we found no full error code list for generateContent (13 of 15). Versioned v1 and v1beta1 APIs and release notes with entries on most working days, newest 5 November 2026 (15).\n\n### Maintenance \u0026 community: Vertex AI (Gemini Enterprise Agent Platform) +19\n\n- GroqCloud (72): Model reading. Meta's Muse Spark Open went into limited preview on GroqCloud on 15 October 2026, per Groq's announcement, 17 days before this check, and the models page lists it as an enterprise model (30). The newest shutdown is still `groq/compound` and `compound-mini` on 21 September. The changelog's newest entry is 18 April 2026, the Python SDK 1.7.0 (tagged 25 August) and TypeScript SDK 1.6.0 are still the newest tags, with commits to 4 September and no release since, and the deprecations page has no announcement after 24 August. Production models get an email and a migration path, previews may go at short notice, and no minimum period is stated. Llama 3.1 8B and 3.3 70B got 60 days on the free and developer tiers, Compound 28 days with no replacement named (4 of 12). Three shutdown dates in the 90 days to 1 November, 16 August, 14 September and 21 September. Three in 90 days is still frequent churn (0 of 8). A dated changelog with an RSS feed and a community forum exist, but the changelog's newest entry is 18 April 2026; support replies not checked (10 of 15). SDK issue replies not sampled, since GitHub's API refused our shell (5 of 10). Python SDK 1.7.0 and TypeScript SDK 1.6.0, both published on 26 August 2026 and still the newest tags on 1 November (15). CI, release-please and lock-file vulnerability fixes in 1.7.0 (8 of 10). 30+4+0+10+5+15+8 = 72.\n- Vertex AI (Gemini Enterprise Agent Platform) (91): Model reading. Gemini Omni 1.2 Flash GA on 5 November, Grok 4.7 GA on 27 October, Gemini 4 Argon GA on 13 October and Claude Haiku 5.5 on 7 October 2026 (30). Stable models stay at least 12 months, and since 6 October Gemini 4 Argon and Gemini 3.8 Flash are in that tier, while 3.7 and 3.6 Flash remain short-term with at least 45 days' notice once a date is set (10 of 12, up from 8). Gemini 2.5 Pro, Flash and Flash-Lite were due to retire on 20 October, three models on one date after more than 12 months, with replacements named. We didn't check partner model retirements (6 of 8, down from 8). Dated release notes on most working days, newest 5 November, and Cloud Customer Care (15). python-genai replies not sampled this run (5 of 10). Current SDKs, google-genai 3.0.0 on 6 November and `@google/genai` 3.0.0, and google-cloud-aiplatform 2.7.0 on 28 October 2026 (15). Supported runtimes stated, Python 3.10 or later and Node 20 or later, per the 6 October check (10). Unchanged at 91.\n\n### Agent ergonomics: Vertex AI (Gemini Enterprise Agent Platform) +17\n\n- GroqCloud (80): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use documented, parallel calls and forced choice not checked (15 of 20). Strict structured outputs (15). Automatic prompt caching at 50% off cached input, with a 2-hour cache life, on gpt-oss-20b, gpt-oss-120b and gpt-oss-safeguard-20b only (10 of 15). 131,072-token context on every self-serve model; only MiniMax M2.7, a preview on enterprise pricing, reaches 196,608 (5 of 15). Batch at half price (10). Official SDKs for Python and JavaScript (10). Errors page with codes, recovery advice and a typed error object (15).\n- Vertex AI (Gemini Enterprise Agent Platform) (97): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with `FunctionCallingConfig` mode ANY and `allowedFunctionNames` to force a named tool (20). Schema-guaranteed JSON output (15). Implicit and explicit context caching, cached input at 0.1x, $0.075 per million on 3.8 Flash through 31 December (15). 1,048,576-token context on 3.8 Flash, a unified 1,048,576-token input and output limit on Gemini 4 Argon, and 1,000,000 on Claude Opus 5.5 (15). Flex/Batch at half price on Gemini (10). Gen AI SDKs for Python and JavaScript among others, and the OpenAI libraries work with Google Cloud auth (10). 429 handling documented per quota framework, with standard Google status codes elsewhere (12 of 15).\n\n### Security \u0026 auth: Vertex AI (Gemini Enterprise Agent Platform) +15\n\n- GroqCloud (82): Model reading. Bearer keys scoped to a project, with custom request limits per project and model permissions at organisation and project level. The security onboarding page advises rotating keys periodically and revoking them at once if compromise is suspected (30). The services agreement, read first-hand on 6 October and not re-read on 17 October, says Groq is not permitted to use inputs or outputs for training unless the customer grants permission (20). No retention by default, up to 30 days for reliability and abuse monitoring, and zero retention as a setting any customer can turn on in Data Controls, per the data page (15). Request logs, usage per project and a read-only Reader role (12 of 15). groq.com's security.txt holds a Contact line and nothing else, and the trust centre at trust.groq.com is a Vanta page that renders only with JavaScript, so certifications and any bug bounty are unchecked (5 of 20).\n- Vertex AI (Gemini Enterprise Agent Platform) (97): Model reading. OAuth 2.0 with IAM roles and service accounts, which the docs recommend for production, with API keys for testing. No key in a query string found in the docs we read (30). The training restriction in the Service Specific Terms says Google won't use customer data to train or fine-tune any model without permission, GA and pre-GA alike (20). Default in-memory caching for 24 hours that can be disabled, flagged prompts logged up to 90 days unless an abuse-monitoring exception is granted or the customer has a Cloud Master Agreement, the Interactions API storing by default, and Claude Fable 5 and Mythos 5 requiring 30-day retention shared with Anthropic (12 of 15, a judgement call matching the Fable exception on the Anthropic and Bedrock listings). Cloud Audit Logs, with Data Access logs off until enabled, and optional request-response logging to BigQuery (15). google.com security.txt valid to 2030-04-01, the Google VRP, and Generative AI on the platform in ISO 27001, 27017 and 27018 and SOC 1, 2 and 3 scope per the 1 October services-in-scope check for vertex-ai-tuning, not re-read on 6 October (20).\n\n### Reliability: Vertex AI (Gemini Enterprise Agent Platform) +9\n\n- GroqCloud (90): Status page at groqstatus.com on incident.io, with components for the API, each production and preview model and the website (20). The page's RSS feed and the incident pages it links, read on 1 November, list three incidents in the 90-day window, all in October, and nothing since 12 October apart from a console maintenance on 18 October. On 10 October the enterprise-only preview MiniMax M2.7 had failed requests from data centre hardware issues from 10:09 to 16:14 UTC. The incident's header and impact record say degraded performance, while its Identified update set the model to partial outage and warned of failed requests. On 12 October console.groq.com returned 500 errors on metrics, /models and key creation from 03:17 to 03:38, and the vendor said inference requests were not affected. On 12 October MiniMax M2.7 had a partial outage from 18:17 to 18:36. None took down a core API for an hour, since the six-hour incident hit one enterprise preview model that the status page describes as for evaluation only, so minor incidents only (20). A stricter reading would count it as a major and give 10. Limits published per model, 30 requests a minute, 1,000 a day and 8,000 tokens a minute on gpt-oss for the free plan, re-read on 1 November (15). 429 carries `retry-after`, `x-ratelimit-*` headers come on every response, and the errors page says to wait before retrying a 503 and that a 502 may clear on retry (15). The Performance Tier, enterprise only, lists a 99.9% availability SLA with details in the offline agreement, per the 6 October check, not re-read on 1 November (10). gpt-oss and Whisper models are production, Qwen 3.8 27B, MiniMax M2.7 and Muse Spark Open are previews (10). 20+20+15+15+10+10 = 90.\n- Vertex AI (Gemini Enterprise Agent Platform) (99): Google Cloud status dashboard with per-product history (20). Its incident feed, read on 6 November, has no incident naming Vertex Gemini API, Gemini or the Agent Platform in the 90 days. The regional incidents of 20 August, 1 September and 20 October (europe-west2) and the Cloud Storage incident of 8 and 9 October didn't list it among affected products (30). Standard PayGo publishes usage tiers by 30-day spend with baseline throughput, 2,000,000 to 50,000,000 tokens a minute for Flash models and 500,000 to 10,000,000 for Pro, plus quota tables for other methods (15). A 429 page says to use the global endpoint and truncated exponential backoff, and a retry-strategy page (15). SLA of 99.5 per cent a month on generateContent and streamGenerateContent, 95 per cent for short-term availability models, and a latency SLO on Provisioned Throughput. The 429 page says pay-as-you-go 429s don't count toward the SLA error rate (10). Gemini 4 Argon reached GA on 13 October and Gemini 3.8 Flash moved to the 12-month tier, and the v1 API, 3.5 Flash-Lite and Claude Opus 5.5 are GA, but express mode was Preview at the 6 October check (9 of 10, up from 8). Up from 98 on 6 October.\n\n### Transparency \u0026 trust: Vertex AI (Gemini Enterprise Agent Platform) +5\n\n- GroqCloud (85): Closed service with clear terms, SDKs Apache-2.0 (15). The data page agrees with the listing on retention and adds detail, batch files kept 30 days unless deleted, fine-tuning data kept until the customer deletes it, and SCCs for transfers. The services agreement bars training on inputs and outputs, and the DPA gives 15 days' notice of sub-processor changes to subscribers, both read on 6 October. The agreement extends zero retention to 'Eligible Customers' where the data page says all customers may enable it (26 of 30). Deprecations page with announcement and shutdown dates (20). All customer data in Google Cloud buckets in the US, per the data page. The DPA points to a sub-processor list at trust.groq.com/subprocessors, which needs JavaScript and wasn't read (10 of 20).\n- Vertex AI (Gemini Enterprise Agent Platform) (90): Closed service under the Google Cloud terms, with the Gen AI SDKs under Apache-2.0 and a public discovery document (18, as for Bedrock's open service models). The zero data retention and abuse monitoring pages agree with the Cloud terms and give periods for each case, 24 hours in memory, up to 90 days for flagged prompts, 30 days for Advanced AI models, 3 days for Search grounding logs and 30 days for Maps grounding, and say which can't be switched off (27 of 30). A dated model versions table with release and retirement dates and a stated 12-month or 45-day policy, but on 6 November it still listed Gemini 2.5 Pro, Flash and Flash-Lite as available with a 20 October 2026 retirement date and left them out of the retired table (18 of 20, down from 20). The Cloud subprocessor list with locations, per the 1 October vertex-ai-tuning check, regional and multi-region endpoints, and per-model ML processing locations on the partner model cards (20).\n\n## Score by category\n\n| Category | Weight | GroqCloud | Vertex AI (Gemini Enterprise Agent Platform) | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 90 | 99 | Vertex AI (Gemini Enterprise Agent Platform) +9 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 64 | 94 | Vertex AI (Gemini Enterprise Agent Platform) +30 |\n| Agent ergonomics | 13% (16.2 this run) | 80 | 97 | Vertex AI (Gemini Enterprise Agent Platform) +17 |\n| Security \u0026 auth | 14% (17.5 this run) | 82 | 97 | Vertex AI (Gemini Enterprise Agent Platform) +15 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 40 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 72 | 91 | Vertex AI (Gemini Enterprise Agent Platform) +19 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 85 | 90 | Vertex AI (Gemini Enterprise Agent Platform) +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **74.5 · BB** | **88.7 · AA** | |\n\n## Facts side by side\n\n| Fact | GroqCloud | Vertex AI (Gemini Enterprise Agent Platform) |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Groq | Google Cloud |\n| Hosted endpoint | `https://api.groq.com/openai/v1` | `https://aiplatform.googleapis.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | OAuth or key |\n| Pricing | Freemium | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDKs) | Proprietary service under the Google Cloud Platform terms. Partner models carry their providers' terms. The Google Gen AI SDKs are Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-10-15 | 2026-11-05 |\n| Terms last updated | 2026-06-22 | 2026-09-02 |\n| Privacy policy last updated | 2025-11-12 | 2026-10-01 |\n| Customer content may train models | not found in the text | yes |\n| Terms restrict automated access | not found in the text | not found in the text |\n| Terms restrict benchmarking | yes | not found in the text |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | not found in the text | not found in the text |\n| Popularity | 619 stars | 20.9M PyPI/wk |\n| Agent reviews | 3.5/5 (8) | none |\n\n## What the same task costs\n\n| Task | GroqCloud | Vertex AI (Gemini Enterprise Agent Platform) |\n| --- | --- | --- |\n| Research a product and write a sourced report | $0.85 | $22.03 |\n| Build a small four-page website | $0.21 | $5.09 |\n| Find and fix a bug in a large codebase | $0.05 | $1.09 |\n\n## How the scores have moved\n\n### GroqCloud\n\n- -1.1 on 17 October 2026: reliability, new evidence\n- -0.1 on 8 October 2026: checklist\n\n### Vertex AI (Gemini Enterprise Agent Platform)\n\nNo score change since it was listed.\n\n## Verdicts\n\n**GroqCloud.** Free plan with no card, at 30 requests a minute and 1,000 a day on gpt-oss. Four model shutdown dates between 2026-07-17 and 2026-09-21, with no stated minimum notice.\n\n**Vertex AI (Gemini Enterprise Agent Platform).** Gemini, Claude and open models through one Google Cloud API with IAM, regional endpoints, schema-guaranteed JSON and a 99.5 per cent SLA, and no Vertex incident on the Cloud status page in the 90 days to 6 November 2026. Gemini 4 Argon and 3.8 Flash have 12-month availability, but short-term Flash models get a 95 per cent SLA.\n\n## Before you call either\n\n### GroqCloud\n\n1. Call `/models` at start-up. Four model ids stopped working between July and September\n2. Read `retry-after` on a 429 and the `x-ratelimit-remaining-tokens` header before the next call\n3. Free plan allows 8,000 tokens a minute on gpt-oss, so keep prompts small or batch them\n4. Don't build on Qwen 3.8 27B. It's a preview and previews can go at short notice\n5. Treat a 498 as Flex capacity and retry later; server errors aren't billed\n\n### Vertex AI (Gemini Enterprise Agent Platform)\n\n1. Call the `global` location on aiplatform.googleapis.com unless data residency requires a Region. The 429 guide says it spreads load and reduces errors\n2. Back off exponentially on 429 `Resource exhausted`. On Standard PayGo it means shared capacity is busy, not a fixed quota\n3. Send `response_schema` with `response_mime_type: application/json`. JSON mode alone doesn't guarantee valid output\n4. Set `store` to false on Interactions API calls. It defaults to true and keeps prompts and responses\n5. Pin `gemini-4-argon` or `gemini-3.8-flash`, both in the 12-month tier, rather than 3.7 or 3.6 Flash, which retire on 28 January 2027 and 19 November 2026\n\n## Questions\n\n### Which is better for AI agents, GroqCloud or Vertex AI (Gemini Enterprise Agent Platform)?\n\nVertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against GroqCloud's 74.5 (BB), and leads in 6 of 7 scored categories.\n\n### Do GroqCloud and Vertex AI (Gemini Enterprise Agent Platform) need an API key?\n\nGroqCloud needs an API key. Vertex AI (Gemini Enterprise Agent Platform) takes an API key or an OAuth sign-in.\n\n### Can an agent call GroqCloud and Vertex AI (Gemini Enterprise Agent Platform) without installing anything?\n\nYes. GroqCloud has a hosted endpoint at https://api.groq.com/openai/v1 and Vertex AI (Gemini Enterprise Agent Platform) at https://aiplatform.googleapis.com/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/groq-vs-vertex-ai.json, and with the fewest tokens: https://www.anchorterminal.com/compare/groq-vs-vertex-ai.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"groq\", \"b\": \"vertex-ai\"}`. From a terminal: `anchor compare groq vertex-ai`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/groq.json and https://www.anchorterminal.com/api/v1/tools/vertex-ai.json\n\n## Other comparisons with GroqCloud or Vertex AI (Gemini Enterprise Agent Platform)\n\n- [Amazon Bedrock vs GroqCloud](https://www.anchorterminal.com/compare/amazon-bedrock-vs-groq.md)\n- [Amazon Bedrock vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/amazon-bedrock-vs-vertex-ai.md)\n- [Claude API vs GroqCloud](https://www.anchorterminal.com/compare/anthropic-api-vs-groq.md)\n- [Claude API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/anthropic-api-vs-vertex-ai.md)\n- [Antseed vs GroqCloud](https://www.anchorterminal.com/compare/antseed-vs-groq.md)\n- [Antseed vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/antseed-vs-vertex-ai.md)\n- [Azure OpenAI in Foundry Models vs 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Platform)](https://www.anchorterminal.com/compare/swe-2-flash-vs-vertex-ai.md)\n- [Tencent Cloud TokenHub vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/tencent-tokenhub-vs-vertex-ai.md)\n- [Together AI Inference vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/together-inference-vs-vertex-ai.md)\n- [Vercel AI Gateway vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/vercel-ai-gateway-vs-vertex-ai.md)\n- [Vertex AI (Gemini Enterprise Agent Platform) vs xAI API (Grok)](https://www.anchorterminal.com/compare/vertex-ai-vs-xai-api.md)\n- [Nemotron 3.5 Super VL vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-vertex-ai.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 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