{ "data": { "a": { "slug": "sambanova", "name": "SambaCloud", "vendor": "SambaNova Systems, Inc.", "vendorUrl": "https://sambanova.ai", "kind": "model", "category": "inference", "summary": "SambaCloud is SambaNova's hosted inference API for open-weight models running on its own RDU processors. It answers OpenAI-style chat, completions and Responses calls and Anthropic-style Messages calls, with Python and TypeScript SDKs.", "url": "https://www.anchorterminal.com/tools/sambanova", "markdownUrl": "https://www.anchorterminal.com/tools/sambanova.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/sambanova.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/sambanova.json", "dns": "sambanova.grade.anchorterminal.com", "repo": "https://github.com/sambanova/sambanova-python", "license": "Proprietary service under the SambaCloud Terms of Service. The SDKs and the OpenAPI document are Apache-2.0", "transports": [ "http" ], "remoteUrl": "https://api.sambanova.ai/v1", "packages": [ { "registry": "pypi", "name": "sambanova" }, { "registry": "npm", "name": "sambanova" } ], "auth": "api-key", "authNotes": "Self-serve Bearer key from the SambaCloud console at https://cloud.sambanova.ai/apis after a browser sign-up. The Messages routes also take the same key in `x-api-key`. Up to 25 keys per user, shown once, with no scopes.", "pricing": "freemium", "pricingNotes": "Free tier with no payment method, at 20 requests a minute, 20 requests a day and 200,000 tokens a day per model. Linking a card moves the account to the Developer tier, billed per token through Stripe, from $0.22 in and $0.59 out per 1M tokens on gpt-oss-120b (https://cloud.sambanova.ai/plans/pricing, checked 2026-10-08).", "priceSummary": "from $0.22 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs index, the OpenAPI document or the pricing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 2, "npmWeekly": 76, "pypiWeekly": 6129, "asOf": "2026-10-08" }, "docsUrl": "https://docs.sambanova.ai/docs/en/get-started/overview", "rateLimitsUrl": "https://docs.sambanova.ai/docs/en/models/rate-limits", "llmsTxt": "https://docs.sambanova.ai/docs/llms.txt", "openapi": "https://raw.githubusercontent.com/sambanova/sambanova-inference-api-spec/refs/heads/main/openapi.documented.json", "capabilities": [ "inference.llm", "inference.open-weights", "inference.fast" ], "tags": [ "hosted", "model", "open-weights", "free-tier", "no-card", "openapi", "llms-txt", "openai-compatible", "anthropic-compatible", "python", "typescript", "status-page", "soc2" ], "lastRelease": "2026-10-27", "graded": true, "anchor": { "graded": true, "score": 64.9, "grade": "B", "agentReady": false, "rank": 446, "ranked": true, "rankOf": 1510, "categoryRank": 18, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 67, "maintenance": 77, "payments": 40, "reliability": 85, "schema": 81, "security": 61, "transparency": 73 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": -5, "negativeNotes": [ "2026-10-08. The models and rate-limit pages listed `MiniMax-M2.7` as a production model a week after its 1 October removal from SambaCloud, and the July 2026 release notes say `Mistral-Large-3-675B-Instruct-2512` was promoted to production. Neither was on the pricing page or in `/v1/models`, and the deprecations page has no entry for Mistral Large 3. Two points, since a keyed call was not made to confirm the ids fail; the docs couldn't be re-read on 1 November (https://docs.sambanova.ai/docs/en/models/sambacloud-models, https://api.sambanova.ai/v1/models).", "2026-10-27, -3. Python SDK 1.14.0 and TypeScript SDK 1.11.0, 1.x minor releases filed under `Features`, removed the documented `do_sample` parameter from the create methods and deleted the audio transcription and translation streaming types, so existing Python callers passing either now get a TypeError. Neither changelog, README nor commit gives notice, and 1.13.0 carried no deprecation warning. Read as box-api's unannounced breaking changes in minor SDK releases, the two packages folded into one term. The docs' release notes and deprecations page couldn't be read on 1 November (bot check); a dated notice there would lift it (https://github.com/sambanova/sambanova-python/commit/8b7cc8b44a82726ff1e759c65f4541adbebb464e, https://github.com/sambanova/sambanova-typescript/commit/128023023eb6ec3b63cabe1c45f0941eb9ba4b8d)" ], "verdict": "Per-token prices are readable without a login, a free tier needs no card, and no incident has been posted since 25 June 2026. A Data Processing Addendum now names 24 sub-processors. Production models get two to three weeks' notice before removal, 13 model ids left between March and June 2026, and the product page's privacy claim contradicts the terms.", "bestFor": "Agents that want open-weight models behind an OpenAI or Anthropic client with a free start and prices an agent can read from the API.", "strengths": [ "Public OpenAPI 3.1.1 document for all 10 operations, plus `llms.txt` and a Markdown copy of every docs page", "Per-token prices for every model on a pricing page that needs no login, and `GET /v1/models` answered without a key on 8 October 2026", "Free tier with no payment method at 20 requests a minute, 20 requests a day and 200,000 tokens a day per model", "One key works with the OpenAI client, the Anthropic client (`/v1/messages`, `x-api-key`) and SambaNova's own SDKs", "Status page with a component per model and no incident posted since 25 June 2026" ], "weaknesses": [ "Production models get a notice of two to three weeks, and 13 model ids were removed between 9 March and 9 June 2026", "The docs listed `MiniMax-M2.7` as a production model on 8 October 2026, a week after it left SambaCloud", "`strict: true` on a JSON schema is accepted and has no effect, and the July 2026 notes record failing structured output on DeepSeek-V3.2", "Keys carry no scopes or project limits, and no key rotation guidance was found in the reviewed documentation", "The privacy policy dates from May 2023, and the product page says prompts are never collected while the DPA keeps them up to 30 days" ], "agentNotes": [ "Call `GET https://api.sambanova.ai/v1/models` at start-up and use only ids it returns. The docs and status page have named models the API no longer serves", "Read `x-ratelimit-remaining-requests` and `x-ratelimit-remaining-requests-day` on every response. The free tier allows 20 requests a day per model", "Treat 429 `queue_full` and 503 `maintenance` as retryable after a delay, and 410 `model_deprecated` as a signal to change model", "Validate JSON output yourself. Schema enforcement is best effort and `strict: true` changes nothing", "Drop `do_sample` before moving to Python SDK 1.14.0 or TypeScript 1.11.0. Those releases removed it" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.9 } ], "editorialScores": { "ergonomics": 67, "maintenance": 77, "payments": 40, "reliability": 85, "schema": 81, "security": 61, "transparency": 63 }, "provenanceScore": 82, "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": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install sambanova # or: npm install sambanova", "http": "curl https://api.sambanova.ai/v1/chat/completions \\\n -H \"Authorization: Bearer $SAMBANOVA_API_KEY\" -H \"Content-Type: application/json\" \\\n -d '{\"model\":\"gpt-oss-120b\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/sambanova" }, "area": "models", "provenance": { "legalEntity": "SambaNova Systems, Inc.", "domain": "sambanova.ai", "domainRegistered": "2017-12-18", "endpointOnVendorDomain": true, "terms": "https://sambanova.ai/cloud-end-user-license-agreement", "privacy": "https://sambanova.ai/privacy-policy", "statusPage": "https://status.sambanova.ai", "changelog": "https://docs.sambanova.ai/docs/en/release-notes/sambacloud", "securityTxt": "none", "checked": "2026-11-01", "notes": [ "The SambaCloud Terms of Service name SambaNova Systems, Inc., a Delaware corporation at 2460 N. First St, Suite #100, San Jose, CA 95131, and define the Service as the SambaCloud platform. The page carries no date. The legal index says it was last updated on 5 February 2026.", "The privacy policy is the company's only one. Its last revision is dated 27 May 2023, it covers the website, communications and related services, and it does not name SambaCloud or API inputs.", "A Data Processing Addendum (undated, with a sub-processor list) is linked from https://sambanova.ai/legal-agreements on 1 November 2026. No SLA or acceptable use policy is linked.", "https://sambanova.ai/.well-known/security.txt returns 404 (1 November 2026).", "RDAP for sambanova.ai gives a registration date of 2017-12-18.", "The API answers at api.sambanova.ai and the console at cloud.sambanova.ai.", "docs.sambanova.ai, which holds the changelog, answered robots.txt with a bot check on 1 November 2026; the changelog URL is kept from 8 October." ], "score": 82 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/sambanova.json", "live": { "slug": "sambanova", "probe": { "target": "https://api.sambanova.ai/v1", "method": "get", "lastAt": "2026-11-07T19:22:30.891327584Z", "lastOk": true, "lastStatus": 405, "lastMs": 408, "authRequired": false, "uptime24h": 100, "uptime30d": 99.63, "p50ms24h": 430, "p95ms24h": 4167, "samples24h": 238, "samples30d": 7299, "days": [ { "date": "2026-10-09", "probes": 169, "ok": 169 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 244 }, { "date": "2026-10-13", "probes": 248, "ok": 247 }, { "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": 252 }, { "date": "2026-10-18", "probes": 256, "ok": 254 }, { "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": 250 }, { "date": "2026-10-25", "probes": 250, "ok": 249 }, { "date": "2026-10-26", "probes": 252, "ok": 251 }, { "date": "2026-10-27", "probes": 249, "ok": 248 }, { "date": "2026-10-28", "probes": 250, "ok": 249 }, { "date": "2026-10-29", "probes": 249, "ok": 249 }, { "date": "2026-10-30", "probes": 249, "ok": 247 }, { "date": "2026-10-31", "probes": 251, "ok": 246 }, { "date": "2026-11-01", "probes": 249, "ok": 248 }, { "date": "2026-11-02", "probes": 250, "ok": 247 }, { "date": "2026-11-03", "probes": 248, "ok": 247 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 238 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "vendorStatus": { "page": "https://status.sambanova.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-07T19:23:41.509744411Z" }, "versions": [ { "registry": "github", "name": "sambanova/sambanova-python", "version": "v1.14.0", "released": "2026-10-27", "seenAt": "2026-11-07T00:02:53.850481118Z" }, { "registry": "npm", "name": "sambanova", "version": "1.11.0", "seenAt": "2026-11-07T00:02:52.055877697Z" }, { "registry": "pypi", "name": "sambanova", "version": "1.14.0", "released": "2026-10-27", "seenAt": "2026-11-07T00:02:51.843088379Z" } ], "githubStars": 2, "npmWeekly": 184, "pypiWeekly": 6594, "securityTxt": { "url": "https://sambanova.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:00:51.384515092Z" }, "llmsTxt": { "url": "https://docs.sambanova.ai/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:33:52.353741246Z" }, "domain": { "domain": "sambanova.ai", "registered": "2017-12-18", "source": "https://rdap.identitydigital.services/rdap/domain/sambanova.ai", "checkedAt": "2026-11-01T14:19:26.553450436Z" }, "pages": [ { "url": "https://docs.sambanova.ai/docs/en/release-notes/sambacloud", "kind": "changelog", "status": 200, "checkedAt": "2026-11-07T14:07:53.11787435Z", "changedAt": "2026-11-07T14:07:53.11787435Z", "fingerprint": "639e478e95cf" }, { "url": "https://docs.sambanova.ai/docs/en/models/deprecations", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:07:50.629389308Z", "changedAt": "2026-11-07T14:07:50.629389308Z", "fingerprint": "c32bdc3792a4" }, { "url": "https://cloud.sambanova.ai/plans/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-07T14:01:14.694665067Z", "changedAt": "2026-11-04T08:16:38.36121287Z", "fingerprint": "e3fafef6d55a" }, { "url": "https://sambanova.ai/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:18:57.305436961Z", "changedAt": "2026-10-31T06:20:00.89176957Z", "fingerprint": "c3c533d959fb" }, { "url": "https://sambanova.ai/cloud-end-user-license-agreement", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:18:55.127900045Z", "changedAt": "2026-10-31T06:19:58.778199001Z", "fingerprint": "3a3a2cdc3ec3" } ], "github": { "repo": "sambanova/sambanova-python", "checkedAt": "2026-11-02T09:23:31.903210325Z", "stars": 2, "archived": false, "issuesEnabled": false, "pushedAt": "2026-10-27T18:08:31Z", "openIssues": 0, "openPRs": 0, "issuesOpened90d": 0, "issuesClosed90d": 0, "prsMerged90d": 5, "ci": { "branch": "main", "runs": 50, "success": 28, "failure": 0, "other": 22, "latest": "skipped", "latestAt": "2026-10-27T18:08:25Z" } }, "updatedAt": "2026-11-07T19:23:41.509744411Z" } }, "answer": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against SambaCloud's 64.9 (B), 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": 36, "category": "security", "leads": "vertex-ai", "name": "Security \u0026 auth", "reasons": { "a": "Model reading. Bearer keys from the console, up to 25 per user and shown once. No scopes, project limits or rotation guidance were found, and the key travels in a header, never in the URL (18 of 30). The terms let SambaNova access, store and process Customer Content solely to supply the service and for no other purposes, which rules out training without naming it (18 of 20). The DPA's Annex I, read on 1 November, keeps prompts, inputs and completions no more than 30 days for security, fraud and abuse monitoring. No zero-retention option was found in the pages read, and the product page says SambaCloud never sees or collects prompts, against that retention and terms that grant a right to store content and to use query logs (10 of 15, between deepseek-api's 5 and siliconflow's 6 with no period stated and the 15 groq, mistral-api and xai-api take for 30 days with a zero-retention route). The console has a Usage page. No request logs or audit log were found in the docs (5 of 15). The trust centre states SOC 2 Type 2 and ISO 27001. No security.txt, disclosure policy or bug bounty was found, and the SDK's SECURITY.md sends service reports to a general address (10 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": 61, "b": 97 } }, { "by": 30, "category": "ergonomics", "leads": "vertex-ai", "name": "Agent ergonomics", "reasons": { "a": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with `tool_choice` to force a function, on six listed models, and a July 2026 fix for tool calls that were buffered when streaming (17 of 20). `json_object` and `json_schema` output, best effort only, with a known failure on DeepSeek-V3.2 recorded in July 2026 (8 of 15). Automatic prompt caching on the MiniMax models only, with cached input on MiniMax-M3.1-Flash at a tenth of the input price on the 1 November pricing page, a 4,096-token minimum and no guaranteed persistence (10 of 15). 131,072-token context on four of six models, 1M on the MiniMax-M3 preview, and output capped at 7,168 tokens on DeepSeek and 3,072 on Llama 3.3 70B (8 of 15). No batch API found in the docs index or the OpenAPI document (0 of 10). Official SDKs for Python and TypeScript, and the OpenAI and Anthropic clients work with a changed base URL (10). Errors carry `type`, `param`, `code` and `request_id` (14 of 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": 67, "b": 97 } }, { "by": 17, "category": "transparency", "leads": "vertex-ai", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed service with clear terms that name the entity and the governing law, and Apache-2.0 SDKs and OpenAPI document (15). The privacy policy is still dated 27 May 2023 and doesn't name SambaCloud or API inputs. A Data Processing Addendum, part of the Terms of Service, is now linked from the legal page (undated). It limits processing of customer personal data to the agreement, its Annex I says prompts, inputs and completions are processed transiently and kept no more than 30 days for security, fraud and abuse monitoring, then deleted, and it deletes customer personal data within 30 days of termination and from backups within 90. The product page's claim, re-read on 1 November, that SambaCloud 'never sees or collects any of your data or user prompts' sits beside that 30-day retention and terms that allow storing content and using query logs (16 of 30, as novita-ai's stated retention periods, below mistral-api's 18 where the DPA and other pages agree). A deprecations page with notice and removal dates back to April 2025, recording MiniMax-M2.7's 1 October removal with a 1 September notice per the 27 October read, though nothing for Mistral Large 3, per the 8 October check (14 of 20). The DPA's Annex III names 24 sub-processors with purpose and location (USA, Japan, Germany, the United Kingdom, Australia) and promises 30 days' notice of changes, though it doesn't say where inference runs for a given request (18 of 20). 15+16+14+18 = 63.", "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": 73, "b": 90 } }, { "by": 14, "category": "reliability", "leads": "vertex-ai", "name": "Reliability", "reasons": { "a": "Hosted reading. Status page at status.sambanova.ai on Statuspage, with components for the API gateway, the Playground and each model, read from the page itself on 1 November (20). Its history.atom feed, read on 1 November (the 8 October reading came from the /api/ path, which robots.txt disallows, and is struck), has nothing newer than an 8-minute Playground login fault on 25 June 2026, so nothing is posted for the window from 3 August (30). Limits published per model and tier, 20 requests a minute and 20 a day on the free tier, 60 and 12,000 on the Developer tier, per the 8 October check, not re-read on 1 November (15). `x-ratelimit-*` headers on every response, the error table says to retry `queue_full` after a delay, and the SDKs retry 429 and server errors twice with backoff, per the 8 October check. No `Retry-After` header was found in the docs or the OpenAPI document (10 of 15). No SLA found on the legal page, re-read on 1 November, or in the terms (0). Three production models are generally available, the rest previews, per the 8 October check (10). 20+30+15+10+0+10 = 85.", "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": 85, "b": 99 } }, { "by": 14, "category": "maintenance", "leads": "vertex-ai", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading. The OpenAPI document and both SDKs (Python 1.14.0, TypeScript 1.11.0) were updated on 27 October 2026, adding `MiniMax-M3.1-Flash`, which is on the pricing page on 1 November (30). Production models get a notice of at least two to three weeks. The recorded removals to June had 14 days or less, and MiniMax-M2.7's removal from SambaCloud on 1 October was announced on 1 September (30 days), per the 27 October read of the deprecations page (5 of 12). One removal in the window from 3 August (MiniMax-M2.7) after 13 model ids left between 9 March and 9 June 2026 (4 of 8). The SambaCloud release notes stopped at 7 July 2026 on 8 October and couldn't be re-read. The community forum had staff replies, per the 8 October check (10 of 15). The Python SDK repository holds release pull requests only, so replies could not be judged (5 of 10). Python 1.14.0 and TypeScript 1.11.0, both 27 October 2026 (15). CI, release-please, a lock file and gitleaks scanning (8 of 10). 30+5+4+10+5+15+8 = 77.", "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": 77, "b": 91 } }, { "by": 13, "category": "schema", "leads": "vertex-ai", "name": "Schema \u0026 documentation", "reasons": { "a": "Public OpenAPI 3.1.1 document, still labelled version 1.2.0 though its content changed on 27 October 2026, with 10 operations and 165 schemas (25). `llms.txt` at docs.sambanova.ai/docs/llms.txt and a Markdown copy of each page, per the 8 October check (docs.sambanova.ai answered robots.txt with a bot check on 1 November, so it wasn't read) (10). 718 descriptions in the document, with pages for function calling, Responses, Messages and caching. When not to use a route is rarely stated (15 of 20). Typed parameters with 150 enums, and request bodies now declare `additionalProperties: false`, but JSON schema output is best effort and `strict: true` has no effect (10 of 15). Code examples in Python and TypeScript, and an errors page with 13 codes and a recovery suggestion for each, per the 8 October check (13 of 15). The spec is versioned in its repository, but the version label didn't move with the 27 October changes, the newest SambaCloud release note seen is 7 July 2026, and the models page disagreed with `/v1/models` on 8 October (8 of 15). 25+10+15+10+13+8 = 81.", "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": 81, "b": 94 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "SambaNova Systems, Inc.", "b": "Google Cloud", "name": "Vendor" }, { "a": "https://api.sambanova.ai/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": "Proprietary service under the SambaCloud Terms of Service. The SDKs and the OpenAPI document are Apache-2.0", "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": "2026-10-27", "b": "2026-11-05", "name": "Last release" }, { "a": "no date given", "b": "2026-09-02", "name": "Terms last updated" }, { "a": "2023-05-27", "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": "2 stars, 76 npm/wk, 6.1k PyPI/wk", "b": "20.9M PyPI/wk", "name": "Popularity" }, { "a": "3.5/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against SambaCloud's 64.9 (B), and leads in 6 of 7 scored categories.", "question": "Which is better for AI agents, SambaCloud or Vertex AI (Gemini Enterprise Agent Platform)?" }, { "answer": "SambaCloud needs an API key. Vertex AI (Gemini Enterprise Agent Platform) takes an API key or an OAuth sign-in.", "question": "Do SambaCloud and Vertex AI (Gemini Enterprise Agent Platform) need an API key?" }, { "answer": "Yes. SambaCloud has a hosted endpoint at https://api.sambanova.ai/v1 and Vertex AI (Gemini Enterprise Agent Platform) at https://aiplatform.googleapis.com/v1.", "question": "Can an agent call SambaCloud and Vertex AI (Gemini Enterprise Agent Platform) without installing anything?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Agents that want open-weight models behind an OpenAI or Anthropic client with a free start and prices an agent can read from the API.", "slug": "sambanova", "watchFor": "Production models get a notice of two to three weeks, and 13 model ids were removed between 9 March and 9 June 2026" }, { "aheadOn": [ "Reliability, 99 against 85", "Schema \u0026 documentation, 94 against 81", "Agent ergonomics, 97 against 67", "Security \u0026 auth, 97 against 61", "Maintenance \u0026 community, 91 against 77", "Transparency \u0026 trust, 90 against 73" ], "also": [ "Agent-ready, a grade of BB or better", "No incidents deducted, where SambaCloud loses 5 points for them" ], "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": { "sambanova": [ { "causes": [ "evidence", "event" ], "date": "2026-11-01", "delta": -1.5, "from": 66.4, "fromGrade": "B", "to": 64.9, "toGrade": "B" }, { "causes": [ "correction" ], "date": "2026-10-28", "delta": -0.1, "from": 66.5, "fromGrade": "B", "to": 66.4, "toGrade": "B" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.1, "from": 66.4, "fromGrade": "B", "to": 66.5, "toGrade": "B" } ], "vertex-ai": null }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-sambanova.json", "title": "Amazon Bedrock vs SambaCloud", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-sambanova" }, { "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-sambanova.json", "title": "Claude API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-sambanova" }, { "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-sambanova.json", "title": "Antseed vs SambaCloud", "url": "https://www.anchorterminal.com/compare/antseed-vs-sambanova" }, { "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-sambanova.json", "title": "Azure OpenAI in Foundry Models vs SambaCloud", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-vertex-ai.json", "title": "Azure OpenAI in Foundry Models vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-sambanova.json", "title": "BlockRun.AI vs SambaCloud", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-vertex-ai.json", "title": "BlockRun.AI vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-sambanova.json", "title": "Cerebras Inference vs SambaCloud", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-vertex-ai.json", "title": "Cerebras Inference vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-sambanova.json", "title": "Cloudflare AI Gateway vs SambaCloud", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-vertex-ai.json", "title": "Cloudflare AI Gateway vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-sambanova.json", "title": "Cloudflare Workers AI vs SambaCloud", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-vertex-ai.json", "title": "Cloudflare Workers AI vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-sambanova.json", "title": "Cohere Chat API (Command models) vs SambaCloud", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-vertex-ai.json", "title": "Cohere Chat API (Command models) vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-sambanova.json", "title": "DeepInfra vs SambaCloud", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-vertex-ai.json", "title": "DeepInfra vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-sambanova.json", "title": "DeepSeek API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-sambanova" }, { "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-sambanova.json", "title": "Eden AI vs SambaCloud", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-sambanova" }, { "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-sambanova.json", "title": "Glimpse vs SambaCloud", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-sambanova" }, { "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-sambanova.json", "title": "Fireworks AI Inference vs SambaCloud", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-sambanova" }, { "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-sambanova.json", "title": "Gemini Developer API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-sambanova" }, { "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-sambanova.json", "title": "GroqCloud vs SambaCloud", "url": "https://www.anchorterminal.com/compare/groq-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-vertex-ai.json", "title": "GroqCloud vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/groq-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-sambanova.json", "title": "Hugging Face Inference Providers vs SambaCloud", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-sambanova" }, { "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-sambanova.json", "title": "Inkling 2 Small vs SambaCloud", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-sambanova" }, { "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-sambanova.json", "title": "Jais 3 vs SambaCloud", "url": "https://www.anchorterminal.com/compare/jais-3-vs-sambanova" }, { "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-sambanova.json", "title": "Meta Model API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-sambanova" }, { "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-sambanova.json", "title": "MiniMax API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-sambanova" }, { "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-sambanova.json", "title": "Mistral AI API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-sambanova" }, { "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-sambanova.json", "title": "Muse Spark Open vs SambaCloud", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-sambanova" }, { "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-sambanova.json", "title": "Novita AI vs SambaCloud", "url": "https://www.anchorterminal.com/compare/novita-ai-vs-sambanova" }, { "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-sambanova.json", "title": "OpenAI API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/openai-api-vs-sambanova" }, { "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-sambanova.json", "title": "OpenRouter vs SambaCloud", "url": "https://www.anchorterminal.com/compare/openrouter-vs-sambanova" }, { "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-sambanova.json", "title": "Router.com (Ramp Router) vs SambaCloud", "url": "https://www.anchorterminal.com/compare/ramp-router-vs-sambanova" }, { "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-sambanova.json", "title": "Beam 1.0 vs SambaCloud", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-sambanova" }, { "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-sambanova.json", "title": "RouterPlus vs SambaCloud", "url": "https://www.anchorterminal.com/compare/routerplus-vs-sambanova" }, { "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-siliconflow.json", "title": "SambaCloud vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/sambanova-vs-siliconflow" }, { "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/sambanova-vs-stepfun-api.json", "title": "SambaCloud vs StepFun API", "url": "https://www.anchorterminal.com/compare/sambanova-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-swe-2-flash.json", "title": "SambaCloud vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/sambanova-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-tencent-tokenhub.json", "title": "SambaCloud vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/sambanova-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-together-inference.json", "title": "SambaCloud vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/sambanova-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-vercel-ai-gateway.json", "title": "SambaCloud vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/sambanova-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-xai-api.json", "title": "SambaCloud vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/sambanova-vs-xai-api" }, { "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-sambanova.json", "title": "Nemotron 3.5 Super VL vs SambaCloud", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-sambanova" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-sambanova.json", "title": "Prism Inference vs SambaCloud", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-sambanova" } ], "panel": null, "scores": [ { "by": 14, "edge": "vertex-ai", "key": "reliability", "name": "Reliability", "sambanova": 85, "vertex-ai": 99, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 13, "edge": "vertex-ai", "key": "schema", "name": "Schema \u0026 documentation", "sambanova": 81, "vertex-ai": 94, "weight": 13 }, { "by": 30, "edge": "vertex-ai", "key": "ergonomics", "name": "Agent ergonomics", "sambanova": 67, "vertex-ai": 97, "weight": 13 }, { "by": 36, "edge": "vertex-ai", "key": "security", "name": "Security \u0026 auth", "sambanova": 61, "vertex-ai": 97, "weight": 14 }, { "by": 0, "edge": "", "key": "payments", "name": "Payments \u0026 pricing", "sambanova": 40, "vertex-ai": 40, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 14, "edge": "vertex-ai", "key": "maintenance", "name": "Maintenance \u0026 community", "sambanova": 77, "vertex-ai": 91, "weight": 7 }, { "by": 17, "edge": "vertex-ai", "key": "transparency", "name": "Transparency \u0026 trust", "sambanova": 73, "vertex-ai": 90, "weight": 7 } ], "summary": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against SambaCloud's 64.9 (B), and leads in 6 of 7 scored categories. Both do llm inference.", "tasks": [ { "a": "$2.39", "b": "$22.03", "cheaper": "sambanova", "task": "Research a product and write a sourced report", "what": "gpt-oss-120b on SambaCloud, gemini-3.1-pro-preview on Vertex AI (Gemini Enterprise Agent Platform)" }, { "a": "$0.53", "b": "$5.09", "cheaper": "sambanova", "task": "Build a small four-page website", "what": "gpt-oss-120b on SambaCloud, gemini-3.1-pro-preview on Vertex AI (Gemini Enterprise Agent Platform)" }, { "a": "$0.12", "b": "$1.09", "cheaper": "sambanova", "task": "Find and fix a bug in a large codebase", "what": "gpt-oss-120b on SambaCloud, gemini-3.1-pro-preview on Vertex AI (Gemini Enterprise Agent Platform)" } ], "tutorials": [], "verdicts": { "sambanova": "Per-token prices are readable without a login, a free tier needs no card, and no incident has been posted since 25 June 2026. A Data Processing Addendum now names 24 sub-processors. Production models get two to three weeks' notice before removal, 13 model ids left between March and June 2026, and the product page's privacy claim contradicts the terms.", "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/sambanova-vs-vertex-ai", "json": "https://www.anchorterminal.com/compare/sambanova-vs-vertex-ai.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/sambanova-vs-vertex-ai.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/sambanova-vs-vertex-ai.min.md" }, "markdown": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against SambaCloud's 64.9 (B), and leads in 6 of 7 scored categories. Both do llm inference.\n\n- SambaCloud: grade B, 64.9/100, rank #446 of 1510. Markdown https://www.anchorterminal.com/tools/sambanova.md · JSON https://www.anchorterminal.com/api/v1/tools/sambanova.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### SambaCloud (B)\n\nGood for: Agents that want open-weight models behind an OpenAI or Anthropic client with a free start and prices an agent can read from the API.\n\nWatch for: Production models get a notice of two to three weeks, and 13 model ids were removed between 9 March and 9 June 2026\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 85\n- Schema \u0026 documentation, 94 against 81\n- Agent ergonomics, 97 against 67\n- Security \u0026 auth, 97 against 61\n- Maintenance \u0026 community, 91 against 77\n- Transparency \u0026 trust, 90 against 73\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- No incidents deducted, where SambaCloud loses 5 points for them\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### Security \u0026 auth: Vertex AI (Gemini Enterprise Agent Platform) +36\n\n- SambaCloud (61): Model reading. Bearer keys from the console, up to 25 per user and shown once. No scopes, project limits or rotation guidance were found, and the key travels in a header, never in the URL (18 of 30). The terms let SambaNova access, store and process Customer Content solely to supply the service and for no other purposes, which rules out training without naming it (18 of 20). The DPA's Annex I, read on 1 November, keeps prompts, inputs and completions no more than 30 days for security, fraud and abuse monitoring. No zero-retention option was found in the pages read, and the product page says SambaCloud never sees or collects prompts, against that retention and terms that grant a right to store content and to use query logs (10 of 15, between deepseek-api's 5 and siliconflow's 6 with no period stated and the 15 groq, mistral-api and xai-api take for 30 days with a zero-retention route). The console has a Usage page. No request logs or audit log were found in the docs (5 of 15). The trust centre states SOC 2 Type 2 and ISO 27001. No security.txt, disclosure policy or bug bounty was found, and the SDK's SECURITY.md sends service reports to a general address (10 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### Agent ergonomics: Vertex AI (Gemini Enterprise Agent Platform) +30\n\n- SambaCloud (67): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with `tool_choice` to force a function, on six listed models, and a July 2026 fix for tool calls that were buffered when streaming (17 of 20). `json_object` and `json_schema` output, best effort only, with a known failure on DeepSeek-V3.2 recorded in July 2026 (8 of 15). Automatic prompt caching on the MiniMax models only, with cached input on MiniMax-M3.1-Flash at a tenth of the input price on the 1 November pricing page, a 4,096-token minimum and no guaranteed persistence (10 of 15). 131,072-token context on four of six models, 1M on the MiniMax-M3 preview, and output capped at 7,168 tokens on DeepSeek and 3,072 on Llama 3.3 70B (8 of 15). No batch API found in the docs index or the OpenAPI document (0 of 10). Official SDKs for Python and TypeScript, and the OpenAI and Anthropic clients work with a changed base URL (10). Errors carry `type`, `param`, `code` and `request_id` (14 of 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### Transparency \u0026 trust: Vertex AI (Gemini Enterprise Agent Platform) +17\n\n- SambaCloud (73): Closed service with clear terms that name the entity and the governing law, and Apache-2.0 SDKs and OpenAPI document (15). The privacy policy is still dated 27 May 2023 and doesn't name SambaCloud or API inputs. A Data Processing Addendum, part of the Terms of Service, is now linked from the legal page (undated). It limits processing of customer personal data to the agreement, its Annex I says prompts, inputs and completions are processed transiently and kept no more than 30 days for security, fraud and abuse monitoring, then deleted, and it deletes customer personal data within 30 days of termination and from backups within 90. The product page's claim, re-read on 1 November, that SambaCloud 'never sees or collects any of your data or user prompts' sits beside that 30-day retention and terms that allow storing content and using query logs (16 of 30, as novita-ai's stated retention periods, below mistral-api's 18 where the DPA and other pages agree). A deprecations page with notice and removal dates back to April 2025, recording MiniMax-M2.7's 1 October removal with a 1 September notice per the 27 October read, though nothing for Mistral Large 3, per the 8 October check (14 of 20). The DPA's Annex III names 24 sub-processors with purpose and location (USA, Japan, Germany, the United Kingdom, Australia) and promises 30 days' notice of changes, though it doesn't say where inference runs for a given request (18 of 20). 15+16+14+18 = 63.\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### Reliability: Vertex AI (Gemini Enterprise Agent Platform) +14\n\n- SambaCloud (85): Hosted reading. Status page at status.sambanova.ai on Statuspage, with components for the API gateway, the Playground and each model, read from the page itself on 1 November (20). Its history.atom feed, read on 1 November (the 8 October reading came from the /api/ path, which robots.txt disallows, and is struck), has nothing newer than an 8-minute Playground login fault on 25 June 2026, so nothing is posted for the window from 3 August (30). Limits published per model and tier, 20 requests a minute and 20 a day on the free tier, 60 and 12,000 on the Developer tier, per the 8 October check, not re-read on 1 November (15). `x-ratelimit-*` headers on every response, the error table says to retry `queue_full` after a delay, and the SDKs retry 429 and server errors twice with backoff, per the 8 October check. No `Retry-After` header was found in the docs or the OpenAPI document (10 of 15). No SLA found on the legal page, re-read on 1 November, or in the terms (0). Three production models are generally available, the rest previews, per the 8 October check (10). 20+30+15+10+0+10 = 85.\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### Maintenance \u0026 community: Vertex AI (Gemini Enterprise Agent Platform) +14\n\n- SambaCloud (77): Model reading. The OpenAPI document and both SDKs (Python 1.14.0, TypeScript 1.11.0) were updated on 27 October 2026, adding `MiniMax-M3.1-Flash`, which is on the pricing page on 1 November (30). Production models get a notice of at least two to three weeks. The recorded removals to June had 14 days or less, and MiniMax-M2.7's removal from SambaCloud on 1 October was announced on 1 September (30 days), per the 27 October read of the deprecations page (5 of 12). One removal in the window from 3 August (MiniMax-M2.7) after 13 model ids left between 9 March and 9 June 2026 (4 of 8). The SambaCloud release notes stopped at 7 July 2026 on 8 October and couldn't be re-read. The community forum had staff replies, per the 8 October check (10 of 15). The Python SDK repository holds release pull requests only, so replies could not be judged (5 of 10). Python 1.14.0 and TypeScript 1.11.0, both 27 October 2026 (15). CI, release-please, a lock file and gitleaks scanning (8 of 10). 30+5+4+10+5+15+8 = 77.\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### Schema \u0026 documentation: Vertex AI (Gemini Enterprise Agent Platform) +13\n\n- SambaCloud (81): Public OpenAPI 3.1.1 document, still labelled version 1.2.0 though its content changed on 27 October 2026, with 10 operations and 165 schemas (25). `llms.txt` at docs.sambanova.ai/docs/llms.txt and a Markdown copy of each page, per the 8 October check (docs.sambanova.ai answered robots.txt with a bot check on 1 November, so it wasn't read) (10). 718 descriptions in the document, with pages for function calling, Responses, Messages and caching. When not to use a route is rarely stated (15 of 20). Typed parameters with 150 enums, and request bodies now declare `additionalProperties: false`, but JSON schema output is best effort and `strict: true` has no effect (10 of 15). Code examples in Python and TypeScript, and an errors page with 13 codes and a recovery suggestion for each, per the 8 October check (13 of 15). The spec is versioned in its repository, but the version label didn't move with the 27 October changes, the newest SambaCloud release note seen is 7 July 2026, and the models page disagreed with `/v1/models` on 8 October (8 of 15). 25+10+15+10+13+8 = 81.\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## Score by category\n\n| Category | Weight | SambaCloud | Vertex AI (Gemini Enterprise Agent Platform) | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 85 | 99 | Vertex AI (Gemini Enterprise Agent Platform) +14 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 81 | 94 | Vertex AI (Gemini Enterprise Agent Platform) +13 |\n| Agent ergonomics | 13% (16.2 this run) | 67 | 97 | Vertex AI (Gemini Enterprise Agent Platform) +30 |\n| Security \u0026 auth | 14% (17.5 this run) | 61 | 97 | Vertex AI (Gemini Enterprise Agent Platform) +36 |\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) | 77 | 91 | Vertex AI (Gemini Enterprise Agent Platform) +14 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 73 | 90 | Vertex AI (Gemini Enterprise Agent Platform) +17 |\n| Negative events | ≤15 | -5 | 0 | |\n| **Total** | | **64.9 · B** | **88.7 · AA** | |\n\n## Facts side by side\n\n| Fact | SambaCloud | Vertex AI (Gemini Enterprise Agent Platform) |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | SambaNova Systems, Inc. | Google Cloud |\n| Hosted endpoint | `https://api.sambanova.ai/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 | Proprietary service under the SambaCloud Terms of Service. The SDKs and the OpenAPI document are Apache-2.0 | 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 | 2026-10-27 | 2026-11-05 |\n| Terms last updated | no date given | 2026-09-02 |\n| Privacy policy last updated | 2023-05-27 | 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 | 2 stars, 76 npm/wk, 6.1k PyPI/wk | 20.9M PyPI/wk |\n| Agent reviews | 3.5/5 (4) | none |\n\n## What the same task costs\n\n| Task | SambaCloud | Vertex AI (Gemini Enterprise Agent Platform) |\n| --- | --- | --- |\n| Research a product and write a sourced report | $2.39 | $22.03 |\n| Build a small four-page website | $0.53 | $5.09 |\n| Find and fix a bug in a large codebase | $0.12 | $1.09 |\n\n## How the scores have moved\n\n### SambaCloud\n\n- -1.5 on 1 November 2026: new evidence, negative event\n- -0.1 on 28 October 2026: correction\n- +0.1 on 27 October 2026: checklist\n\n### Vertex AI (Gemini Enterprise Agent Platform)\n\nNo score change since it was listed.\n\n## Verdicts\n\n**SambaCloud.** Per-token prices are readable without a login, a free tier needs no card, and no incident has been posted since 25 June 2026. A Data Processing Addendum now names 24 sub-processors. Production models get two to three weeks' notice before removal, 13 model ids left between March and June 2026, and the product page's privacy claim contradicts the terms.\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### SambaCloud\n\n1. Call `GET https://api.sambanova.ai/v1/models` at start-up and use only ids it returns. The docs and status page have named models the API no longer serves\n2. Read `x-ratelimit-remaining-requests` and `x-ratelimit-remaining-requests-day` on every response. The free tier allows 20 requests a day per model\n3. Treat 429 `queue_full` and 503 `maintenance` as retryable after a delay, and 410 `model_deprecated` as a signal to change model\n4. Validate JSON output yourself. Schema enforcement is best effort and `strict: true` changes nothing\n5. Drop `do_sample` before moving to Python SDK 1.14.0 or TypeScript 1.11.0. Those releases removed it\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, SambaCloud or Vertex AI (Gemini Enterprise Agent Platform)?\n\nVertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against SambaCloud's 64.9 (B), and leads in 6 of 7 scored categories.\n\n### Do SambaCloud and Vertex AI (Gemini Enterprise Agent Platform) need an API key?\n\nSambaCloud 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 SambaCloud and Vertex AI (Gemini Enterprise Agent Platform) without installing anything?\n\nYes. SambaCloud has a hosted endpoint at https://api.sambanova.ai/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/sambanova-vs-vertex-ai.json, and with the fewest tokens: https://www.anchorterminal.com/compare/sambanova-vs-vertex-ai.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"sambanova\", \"b\": \"vertex-ai\"}`. From a terminal: `anchor compare sambanova vertex-ai`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/sambanova.json and https://www.anchorterminal.com/api/v1/tools/vertex-ai.json\n\n## Other comparisons with SambaCloud or Vertex AI (Gemini Enterprise Agent Platform)\n\n- [Amazon Bedrock vs SambaCloud](https://www.anchorterminal.com/compare/amazon-bedrock-vs-sambanova.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 SambaCloud](https://www.anchorterminal.com/compare/anthropic-api-vs-sambanova.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 SambaCloud](https://www.anchorterminal.com/compare/antseed-vs-sambanova.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 SambaCloud](https://www.anchorterminal.com/compare/azure-openai-vs-sambanova.md)\n- [Azure OpenAI in Foundry Models vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/azure-openai-vs-vertex-ai.md)\n- [BlockRun.AI vs SambaCloud](https://www.anchorterminal.com/compare/blockrun-ai-vs-sambanova.md)\n- [BlockRun.AI vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/blockrun-ai-vs-vertex-ai.md)\n- [Cerebras Inference vs SambaCloud](https://www.anchorterminal.com/compare/cerebras-inference-vs-sambanova.md)\n- [Cerebras Inference vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/cerebras-inference-vs-vertex-ai.md)\n- [Cloudflare AI Gateway vs SambaCloud](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-sambanova.md)\n- [Cloudflare AI Gateway vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-vertex-ai.md)\n- [Cloudflare Workers AI vs SambaCloud](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-sambanova.md)\n- [Cloudflare Workers AI vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-vertex-ai.md)\n- [Cohere Chat API (Command models) vs SambaCloud](https://www.anchorterminal.com/compare/cohere-chat-vs-sambanova.md)\n- [Cohere Chat API (Command models) vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/cohere-chat-vs-vertex-ai.md)\n- [DeepInfra vs SambaCloud](https://www.anchorterminal.com/compare/deepinfra-vs-sambanova.md)\n- [DeepInfra vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/deepinfra-vs-vertex-ai.md)\n- [DeepSeek API vs SambaCloud](https://www.anchorterminal.com/compare/deepseek-api-vs-sambanova.md)\n- [DeepSeek API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/deepseek-api-vs-vertex-ai.md)\n- [Eden AI vs SambaCloud](https://www.anchorterminal.com/compare/eden-ai-vs-sambanova.md)\n- [Eden AI vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/eden-ai-vs-vertex-ai.md)\n- [Glimpse vs SambaCloud](https://www.anchorterminal.com/compare/elorian-glimpse-vs-sambanova.md)\n- [Glimpse vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/elorian-glimpse-vs-vertex-ai.md)\n- [Fireworks AI Inference vs SambaCloud](https://www.anchorterminal.com/compare/fireworks-inference-vs-sambanova.md)\n- [Fireworks AI Inference vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/fireworks-inference-vs-vertex-ai.md)\n- [Gemini Developer API vs SambaCloud](https://www.anchorterminal.com/compare/gemini-api-vs-sambanova.md)\n- [Gemini Developer API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/gemini-api-vs-vertex-ai.md)\n- [GroqCloud vs SambaCloud](https://www.anchorterminal.com/compare/groq-vs-sambanova.md)\n- [GroqCloud vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/groq-vs-vertex-ai.md)\n- [Hugging Face Inference Providers vs SambaCloud](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-sambanova.md)\n- [Hugging Face Inference Providers vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-vertex-ai.md)\n- [Inkling 2 Small vs SambaCloud](https://www.anchorterminal.com/compare/inkling-2-small-vs-sambanova.md)\n- [Inkling 2 Small vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/inkling-2-small-vs-vertex-ai.md)\n- [Jais 3 vs SambaCloud](https://www.anchorterminal.com/compare/jais-3-vs-sambanova.md)\n- [Jais 3 vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/jais-3-vs-vertex-ai.md)\n- [Meta Model API vs SambaCloud](https://www.anchorterminal.com/compare/meta-model-api-vs-sambanova.md)\n- [Meta Model API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/meta-model-api-vs-vertex-ai.md)\n- [MiniMax API vs SambaCloud](https://www.anchorterminal.com/compare/minimax-api-vs-sambanova.md)\n- [MiniMax API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/minimax-api-vs-vertex-ai.md)\n- [Mistral AI API vs SambaCloud](https://www.anchorterminal.com/compare/mistral-api-vs-sambanova.md)\n- [Mistral AI API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/mistral-api-vs-vertex-ai.md)\n- [Muse Spark Open vs SambaCloud](https://www.anchorterminal.com/compare/muse-spark-open-vs-sambanova.md)\n- [Muse Spark Open vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/muse-spark-open-vs-vertex-ai.md)\n- [Novita AI vs SambaCloud](https://www.anchorterminal.com/compare/novita-ai-vs-sambanova.md)\n- [Novita AI vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/novita-ai-vs-vertex-ai.md)\n- [OpenAI API vs SambaCloud](https://www.anchorterminal.com/compare/openai-api-vs-sambanova.md)\n- [OpenAI API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/openai-api-vs-vertex-ai.md)\n- [OpenRouter vs SambaCloud](https://www.anchorterminal.com/compare/openrouter-vs-sambanova.md)\n- [OpenRouter vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/openrouter-vs-vertex-ai.md)\n- [Prism Inference vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/prism-inference-vs-vertex-ai.md)\n- [Router.com (Ramp Router) vs SambaCloud](https://www.anchorterminal.com/compare/ramp-router-vs-sambanova.md)\n- [Router.com (Ramp Router) vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/ramp-router-vs-vertex-ai.md)\n- [Beam 1.0 vs SambaCloud](https://www.anchorterminal.com/compare/reflection-beam-vs-sambanova.md)\n- [Beam 1.0 vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/reflection-beam-vs-vertex-ai.md)\n- [RouterPlus vs SambaCloud](https://www.anchorterminal.com/compare/routerplus-vs-sambanova.md)\n- [RouterPlus vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/routerplus-vs-vertex-ai.md)\n- [SambaCloud vs SiliconFlow](https://www.anchorterminal.com/compare/sambanova-vs-siliconflow.md)\n- [SambaCloud vs Simplismart](https://www.anchorterminal.com/compare/sambanova-vs-simplismart.md)\n- [SambaCloud vs StepFun API](https://www.anchorterminal.com/compare/sambanova-vs-stepfun-api.md)\n- [SambaCloud vs SWE-2 Flash](https://www.anchorterminal.com/compare/sambanova-vs-swe-2-flash.md)\n- [SambaCloud vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/sambanova-vs-tencent-tokenhub.md)\n- [SambaCloud vs Together AI Inference](https://www.anchorterminal.com/compare/sambanova-vs-together-inference.md)\n- [SambaCloud vs Vercel AI Gateway](https://www.anchorterminal.com/compare/sambanova-vs-vercel-ai-gateway.md)\n- [SambaCloud vs xAI API (Grok)](https://www.anchorterminal.com/compare/sambanova-vs-xai-api.md)\n- [SiliconFlow vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai.md)\n- [Simplismart vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/simplismart-vs-vertex-ai.md)\n- [StepFun API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/stepfun-api-vs-vertex-ai.md)\n- [SWE-2 Flash vs Vertex AI (Gemini Enterprise Agent 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 SambaCloud](https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-sambanova.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- [Prism Inference vs SambaCloud](https://www.anchorterminal.com/compare/prism-inference-vs-sambanova.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": "SambaCloud vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "" } ], "description": "Vertex AI scores 88.7 (AA) to SambaCloud's 64.9 (B) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "SambaCloud B 64.9", "Vertex AI (Gemini Enterprise Agent Platform) AA 88.7", "scores" ], "h1": "SambaCloud vs Vertex AI (Gemini Enterprise Agent Platform)", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/sambanova-vs-vertex-ai", "published": "2026-10-01", "section": "tools", "title": "SambaCloud vs Vertex AI for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-11-07", "url": "https://www.anchorterminal.com/compare/sambanova-vs-vertex-ai" }, "tokens": { "markdown": 8000, "slim": 4180 }, "version": 1 }