{ "data": { "a": { "slug": "hugging-face-inference-providers", "name": "Hugging Face Inference Providers", "vendor": "Hugging Face, Inc.", "vendorUrl": "https://huggingface.co", "kind": "router", "category": "inference", "summary": "Hugging Face's serverless router that sends model calls to partner inference providers such as Groq, Cerebras, Together and Fireworks through one Hugging Face token, with an OpenAI-compatible API at router.huggingface.co/v1 and Python and JavaScript clients.", "url": "https://www.anchorterminal.com/tools/hugging-face-inference-providers", "markdownUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-providers.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-providers.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-providers.json", "dns": "hugging-face-inference-providers.grade.anchorterminal.com", "repo": "https://github.com/huggingface/huggingface_hub", "license": "Proprietary service under the Hugging Face Terms of Service, which name Inference Providers. The `huggingface_hub` Python client is Apache-2.0 and `@huggingface/inference` is MIT", "transports": [ "http" ], "remoteUrl": "https://router.huggingface.co/v1", "packages": [ { "registry": "pypi", "name": "huggingface_hub" }, { "registry": "npm", "name": "@huggingface/inference" } ], "auth": "api-key", "authNotes": "Hugging Face user access token sent as `Authorization: Bearer $HF_TOKEN`. The docs recommend a fine-grained token with only the `Make calls to Inference Providers` permission. Tokens are made in account settings in a browser, can be rotated and revoked, and a leaked token can be revoked by anyone through `POST /api/credentials/revoke`. A provider's own key can be stored in settings instead, and Hugging Face swaps it in when routing.", "pricing": "usage", "pricingNotes": "Pay as you go at each provider's own per-token rates with no Hugging Face markup, drawn from prepaid credits on the Hugging Face account. Free users get no included credits since 7 October 2026 and must buy credits. PRO users and Team or Enterprise seats get $2.00 a month of compute credits. Requests with a custom provider key are billed by the provider (https://huggingface.co/docs/inference-providers/pricing, checked 2026-11-06).", "priceSummary": "$2 / mo", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the Inference Providers docs, llms.txt or the router's model list. Calls draw on prepaid Hugging Face credits bought in the account's billing settings (checked 2026-11-06).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": 483470, "pypiWeekly": 71518708, "asOf": "2026-11-06" }, "docsUrl": "https://huggingface.co/docs/inference-providers/index", "llmsTxt": "https://huggingface.co/docs/inference-providers/llms.txt", "capabilities": [ "inference.router", "inference.open-weights", "inference.llm" ], "tags": [ "hosted", "router", "usage", "prepaid", "openai-compatible", "llms-txt", "python", "javascript", "status-page", "soc2" ], "lastRelease": "2026-10-29", "graded": true, "anchor": { "graded": true, "score": 54, "grade": "C", "agentReady": false, "rank": 926, "ranked": true, "rankOf": 1510, "categoryRank": 28, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 58, "maintenance": 80, "payments": 20, "reliability": 25, "schema": 62, "security": 79, "transparency": 71 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": 0, "verdict": "One Hugging Face token reaches open models on 18 partner providers at the providers' own prices, with a public model list that shows each provider's price, context and tool support. Router rate limits, error responses and its own status component were not found in the reviewed documentation, and free accounts have had no included credits since 7 October 2026.", "bestFor": "Agents and developers who want open-weight models from several hosts on one token and one bill, with a public price and capability list per provider.", "strengths": [ "router.huggingface.co/v1/models lists every routed model with each provider's price, context, tool support, latency and throughput, without a token", "Provider choice by suffix (`:fastest`, `:cheapest`, `:preferred` or a provider name) on an OpenAI-compatible endpoint", "Fine-grained tokens can be limited to the `Make calls to Inference Providers` permission and revoked at once", "No markup on provider rates, and request bodies and responses are not stored when routed", "Official clients in Python (`huggingface_hub` 2.4.0, 29 October 2026) and JavaScript (`@huggingface/inference` 4.13.32)" ], "weaknesses": [ "No rate limits, 429 behaviour or error responses documented for the router", "status.huggingface.co has no component for Inference Providers or the router", "Free users have had no included credits since 7 October 2026, and buying credits needs a person in the account settings", "No OpenAPI document for the router, no batch API, and the Responses API is beta", "Data handling past the router depends on each provider's own policy" ], "agentNotes": [ "Read https://router.huggingface.co/v1/models before choosing a provider; it shows price, context and `supports_tools` per provider", "Pin a provider with `model:\u003cprovider\u003e` when tool calling or structured output matters, since support varies by provider", "Use a fine-grained token with only the Inference Providers permission, and send `X-HF-Bill-To` to bill an organisation", "Back off on 429 without assuming a Retry-After header, since none is documented for the router" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54 } ], "editorialScores": { "ergonomics": 58, "maintenance": 80, "payments": 20, "reliability": 25, "schema": 62, "security": 79, "transparency": 58 }, "provenanceScore": 84, "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 huggingface_hub # or: pip install openai, with base_url https://router.huggingface.co/v1", "http": "curl https://router.huggingface.co/v1/chat/completions \\\n -H \"Authorization: Bearer $HF_TOKEN\" -H \"Content-Type: application/json\" \\\n -d '{\"model\":\"openai/gpt-oss-120b:fastest\",\"messages\":[{\"role\":\"user\",\"content\":\"How many Gs in huggingface?\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.router", "tool": "https://letme.dev/hugging-face-inference-providers" }, "sameCompany": [ "hugging-face-inference-endpoints", "smolagents", "carbon-x", "smollm3-3b" ], "area": "models", "unitPrices": [ { "item": "openai/gpt-oss-120b input, cheapest provider listed (DeepInfra)", "unit": "1m-tokens", "usd": 0.037, "note": "Output $0.17. Groq lists $0.15/$0.75 and Cerebras $0.35/$0.75, per the router's model list of 6 November 2026" }, { "item": "PRO monthly compute credits", "unit": "month", "usd": 2, "note": "Also $2.00 per seat for Team and Enterprise organisations" } ], "provenance": { "legalEntity": "Hugging Face, Inc.", "domain": "huggingface.co", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://huggingface.co/terms-of-service", "privacy": "https://huggingface.co/privacy", "statusPage": "https://status.huggingface.co", "changelog": "https://huggingface.co/changelog", "securityTxt": "valid", "checked": "2026-11-06", "notes": [ "The Terms of Service (effective 15 September 2022) name Hugging Face, Inc. and define Inference Providers as a hosted service for running inference through an API on Hub models.", "huggingface.co/robots.txt and router.huggingface.co/robots.txt allow all. status.huggingface.co serves an empty robots.txt.", "status.huggingface.co is a Better Stack page with components for the Hub, git hosting, Inference Endpoints, Spaces, the CDNs and Jobs, and none for Inference Providers or the router.", "huggingface.co/changelog covers the whole Hub and had no Inference Providers entry on its first page on 6 November 2026. Dated changes to the docs are in the hub-docs repository's history.", "security.txt, privacy policy and subprocessor list as recorded on the Inference Endpoints listing (checked 8 October and 2 November 2026); not re-read for this listing." ], "score": 84 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-providers.json", "live": { "slug": "hugging-face-inference-providers", "probe": { "target": "https://router.huggingface.co/v1", "method": "get", "lastAt": "2026-11-07T19:22:12.474650545Z", "lastOk": true, "lastStatus": 404, "lastMs": 145, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 109, "p95ms24h": 196, "samples24h": 210, "samples30d": 210, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 13 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "vendorStatus": { "page": "https://status.huggingface.co", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-07T04:47:55.542550557Z" }, "versions": [ { "registry": "github", "name": "huggingface/huggingface_hub", "version": "v2.4.0", "released": "2026-10-29", "seenAt": "2026-11-06T23:33:27.894225298Z" }, { "registry": "npm", "name": "@huggingface/inference", "version": "4.13.32", "seenAt": "2026-11-06T23:33:27.102558197Z" }, { "registry": "pypi", "name": "huggingface_hub", "version": "2.4.0", "released": "2026-10-29", "seenAt": "2026-11-06T23:33:26.910773744Z" } ], "githubStars": 4034, "npmWeekly": 483470, "pypiWeekly": 71518708, "securityTxt": { "url": "https://huggingface.co/.well-known/security.txt", "state": "valid", "expires": "2030-07-01T08:42:00.000Z", "checkedAt": "2026-11-07T16:00:00.493446773Z" }, "llmsTxt": { "url": "https://huggingface.co/docs/inference-providers/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:44.910268229Z" }, "pages": [ { "url": "https://huggingface.co/docs/inference-providers/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-07T14:11:37.221950067Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "2cbe821ca9d2" } ], "updatedAt": "2026-11-07T19:22:12.474650545Z" } }, "answer": "Prism Inference scores 59.6 (C) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in 3 of 7 scored categories. Hugging Face Inference Providers leads on security \u0026 auth, maintenance \u0026 community and transparency \u0026 trust.", "b": { "slug": "prism-inference", "name": "Prism Inference", "vendor": "Prism Technologies Inc", "vendorUrl": "https://prisminference.com", "kind": "model", "category": "inference", "summary": "Prism is a hosted inference API from Prism Technologies Inc for open-weight models, aimed at coding agents. It accepts OpenAI Chat Completions, OpenAI Responses and Anthropic Messages requests at api.prisminference.com. It launched on 24 September 2026.", "url": "https://www.anchorterminal.com/tools/prism-inference", "markdownUrl": "https://www.anchorterminal.com/tools/prism-inference.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/prism-inference.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/prism-inference.json", "dns": "prism-inference.grade.anchorterminal.com", "repo": "https://github.com/prismhq/hermes-prism-provider", "license": "Proprietary service under Prism's terms of service. The OpenAPI file declares `LicenseRef-Proprietary`. The Hermes provider plugin repository carries no licence file", "transports": [ "http" ], "remoteUrl": "https://api.prisminference.com/v1", "packages": [], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a key the workspace owner creates in the Prism dashboard. The inference endpoints also accept the key in `x-api-key`. A missing, invalid, expired or revoked key returns 401. No key scopes were found in the reviewed documentation. prisminference.com/signup redirects to the login page, and the docs send new users to a sales call for access. The agent sign-up endpoints (`POST /api/agent-signups` and `/verify`) are closed and return `403 signup_disabled` per the docs. `GET /v1/models` needs no key.", "pricing": "usage", "pricingNotes": "Prepaid per-token pricing with no minimum. DeepSeek-V4.1-Flash is $0.09 in, $1.20 out and $0.03 cache read per million tokens, and Gemma 4 31B is $0.30, $0.40 and $0.15 (https://prisminference.com/pricing, read 2026-11-04). No free tier or trial credit was found. A person funds the workspace in a browser, and new access goes through the workspace owner or a sales call. Elastic endpoints, dedicated deployments and batch are sold through sales with no published price.", "priceSummary": "from $0.09 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in llms.txt, the docs index, the OpenAPI file or the pricing page, read 2026-10-08. The docs describe prepaid credit funded by a person in the browser.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://docs.prisminference.com", "rateLimitsUrl": "https://docs.prisminference.com/rate-limits", "llmsTxt": "https://prisminference.com/llms.txt", "openapi": "https://docs.prisminference.com/openapi.yaml", "capabilities": [ "inference.fast", "inference.open-weights", "inference.llm" ], "tags": [ "hosted", "model", "open-weights", "fast", "usage-priced", "prepaid", "openapi", "llms-txt", "openai-compatible", "anthropic-compatible", "zero-retention", "status-page", "new" ], "lastRelease": "2026-10-06", "graded": true, "anchor": { "graded": true, "score": 59.6, "grade": "C", "agentReady": false, "rank": 691, "ranked": true, "rankOf": 1510, "categoryRank": 23, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 68, "maintenance": 49, "payments": 20, "reliability": 65, "schema": 82, "security": 68, "transparency": 63 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-04" }, "negative": -2, "negativeNotes": [ "2026-10-08, re-read 2026-11-04. The home page shows a '99.99% Uptime SLA' tile. The terms of 9 September 2026 say the services have no guaranteed uptime or service credit unless a separate written agreement says otherwise, and no SLA document was found. The pricing page's 'no rate limits' line of 8 October is gone. A misleading claim, with the smallest deduction because the terms state the real position (-2) (https://prisminference.com/, https://prisminference.com/terms)." ], "verdict": "Three wire formats, a public OpenAPI 3.1 file, per-token prices without a login and zero data retention by default on every tier. The service launched on 24 September 2026 with two models, one by request. Agent self-signup has closed and new access goes through a sales call, and no rate-limit numbers, SLA document or deprecation policy was found.", "bestFor": "Coding agents that want DeepSeek-V4.1-Flash at a low input price, with no retention, through whichever of the three wire formats the harness already speaks.", "strengths": [ "One key works across OpenAI Chat Completions, OpenAI Responses and Anthropic Messages, with a public OpenAPI 3.1 file, llms.txt and Markdown docs", "Zero data retention is the default on every tier, and the privacy policy, terms and docs all say inputs and outputs are never used for training", "Every error carries a stable `code`, a `retryable` flag, a `fix` hint and a `docs_url`, and 429 carries `Retry-After` in seconds", "The docs say `GET /v1/models` answers without a key and returns the public model catalogue", "DeepSeek-V4.1-Flash is listed with a 1M-token context and 384,000 output tokens at $0.09 in and $1.20 out per million" ], "weaknesses": [ "Two models. The docs mark Gemma 4 31B as request access per organisation, while llms.txt lists it as available", "No rate-limit numbers are published. The docs say per-key limits exist and return 429", "The home page shows a '99.99% Uptime SLA' tile, while the terms say there is no guaranteed uptime without a separate written agreement", "No free tier or self-serve sign-up. The sign-up page redirects to login, agent sign-up returns 403, and the docs point new users to sales", "The service launched on 24 September 2026. The changelog has one entry, and no deprecation policy, sub-processor list or certification was found" ], "agentNotes": [ "Call `GET https://api.prisminference.com/v1/models` at start-up, with no key per the docs, and use only ids it returns. Expect 403 on `gemma-4-31b` without organisation access", "Use base URL `https://api.prisminference.com/v1` for OpenAI clients and `https://api.prisminference.com` with no `/v1` for Anthropic clients", "Read `error.retryable` before retrying, and wait for `Retry-After` on 429, which covers both key limits and model capacity", "Send `reasoning_effort: \"none\"` or `low` when latency matters. Reasoning is on by default and its tokens are billed as output", "Keep conversation state yourself and send `store: false` on Responses. `previous_response_id`, stored responses and hosted tools aren't supported", "Ask the workspace owner for `PRISM_API_KEY`. The agent sign-up endpoints now return `403 signup_disabled`" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59.6 } ], "editorialScores": { "ergonomics": 68, "maintenance": 49, "payments": 20, "reliability": 65, "schema": 82, "security": 68, "transparency": 43 }, "provenanceScore": 82, "checked": { "categories": { "ergonomics": "2026-11-04", "maintenance": "2026-11-04", "payments": "2026-11-04", "reliability": "2026-11-04", "schema": "2026-11-04", "security": "2026-11-04", "transparency": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install openai # or: npm install openai, base URL https://api.prisminference.com/v1. Anthropic SDKs use https://api.prisminference.com with no /v1", "http": "curl \"https://api.prisminference.com/v1/chat/completions\" \\\n -H \"Authorization: Bearer $PRISM_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\":\"deepseek-v4.1-flash\",\"messages\":[{\"role\":\"user\",\"content\":\"Return pong.\"}]}'", "claudeCode": "export ANTHROPIC_BASE_URL=https://api.prisminference.com\nexport ANTHROPIC_AUTH_TOKEN=$PRISM_API_KEY\nexport ANTHROPIC_MODEL=deepseek-v4.1-flash\nexport ANTHROPIC_SMALL_FAST_MODEL=gemma-4-31b" }, "letme": { "capability": "https://letme.dev/inference.fast", "tool": "https://letme.dev/prism-inference" }, "area": "models", "provenance": { "legalEntity": "Prism Technologies Inc", "domain": "prisminference.com", "domainRegistered": "2026-09-09", "endpointOnVendorDomain": true, "terms": "https://prisminference.com/terms", "privacy": "https://prisminference.com/privacy", "statusPage": "https://status.prisminference.com", "changelog": "https://docs.prisminference.com/changelog", "securityTxt": "valid", "checked": "2026-10-08", "notes": [ "The terms and the privacy policy, both last updated 9 September 2026, name Prism Technologies Inc. The terms are governed by California law with arbitration in San Francisco.", "RDAP gives a registration date of 2026-09-09 for prisminference.com, with Name.com as registrar.", "security.txt has a Contact line (founders@prisminference.com), an Expires date of 2027-10-06 and a Canonical line. No disclosure policy or bug bounty is named.", "The status page runs on incident.io with one component for each model and no component for the API or the website. Its incidents feed was empty on 8 October 2026.", "The YC directory lists Prism in the Spring 2025 batch, in San Francisco, with a team of 2. The home page links an X account named prism_videos, from the company's earlier video product." ], "score": 82 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/prism-inference.json", "live": { "slug": "prism-inference", "probe": { "target": "https://api.prisminference.com/v1", "method": "get", "lastAt": "2026-11-07T19:22:27.758093075Z", "lastOk": true, "lastStatus": 200, "lastMs": 108, "authRequired": false, "uptime24h": 100, "uptime30d": 99.99, "p50ms24h": 107, "p95ms24h": 194, "samples24h": 238, "samples30d": 7473, "days": [ { "date": "2026-10-08", "probes": 93, "ok": 93 }, { "date": "2026-10-09", "probes": 250, "ok": 250 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 246 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 252 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 253 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 250, "ok": 250 }, { "date": "2026-10-29", "probes": 249, "ok": 249 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "vendorStatus": { "page": "https://status.prisminference.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-07T19:23:25.129259046Z" }, "versions": [ { "registry": "github", "name": "prismhq/hermes-prism-provider", "version": "v1.0.2", "released": "2026-09-16", "seenAt": "2026-11-06T23:58:15.073327446Z" } ], "githubStars": 0, "securityTxt": { "url": "https://prisminference.com/.well-known/security.txt", "state": "valid", "expires": "2027-10-06T00:00:00.000Z", "checkedAt": "2026-11-07T16:00:33.418983251Z" }, "llmsTxt": { "url": "https://prisminference.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:33:41.189866269Z" }, "domain": { "domain": "prisminference.com", "registered": "2026-09-09", "source": "https://rdap.verisign.com/com/v1/domain/prisminference.com", "checkedAt": "2026-11-01T14:22:11.330283307Z" }, "pages": [ { "url": "https://docs.prisminference.com/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-11-07T14:07:36.489415322Z", "changedAt": "2026-10-26T03:15:15.804661949Z", "fingerprint": "f0b4b681ff56" }, { "url": "https://prisminference.com/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-07T14:17:21.716876068Z", "changedAt": "2026-10-16T22:29:18.379964038Z", "fingerprint": "ec3ab0c7070a" }, { "url": "https://prisminference.com/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:17:23.938443611Z", "changedAt": "2026-10-14T20:47:07.337685557Z", "fingerprint": "de012b748be4" }, { "url": "https://prisminference.com/terms", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:17:26.094600006Z", "changedAt": "2026-10-14T20:47:09.225430886Z", "fingerprint": "ad1d4e80a6ba" } ], "github": { "repo": "prismhq/hermes-prism-provider", "checkedAt": "2026-11-02T09:18:36.065721455Z", "stars": 0, "archived": false, "issuesEnabled": true, "pushedAt": "2026-09-16T00:55:53Z", "openIssues": 0, "openPRs": 0, "issuesOpened90d": 0, "issuesClosed90d": 0, "prsMerged90d": 0 }, "updatedAt": "2026-11-07T19:23:25.129259046Z" } }, "edges": [ { "by": 40, "category": "reliability", "leads": "prism-inference", "name": "Reliability", "reasons": { "a": "Hosted lines. status.huggingface.co is a Better Stack page with 90-day component history for the Hub, Inference Endpoints, Spaces, the CDNs and Jobs, but no component for Inference Providers or the router (10 of 20). With no component, there is no readable incident history for the surface agents call, and the feed's 23 items from 10 June to 7 October 2026 don't name it (5). No rate limits for the router were found; the Hub rate-limits page gives numbers for Hub APIs, resolvers and pages only (0). No 429 or overload guidance for the router was found (0). No SLA found (0). Chat completions on the router is generally available; the Responses API is labelled beta (10). Routing between providers with `:fastest` or `:cheapest` softens a single provider's outage, which this checklist doesn't score.", "b": "Hosted reading. Status page at status.prisminference.com on incident.io with a component for each of the two models, per the 8 October check (20). The 8 October incident reading came only from the page's `/api/v2` incidents feed, a data endpoint this run doesn't use, so it is struck. On 4 November the status host's robots.txt answered a plain 404 (no rules), and the status page itself shows both models at 100 per cent uptime from 5 August to 3 November with \"No incidents\", data available only from 6 October 2026. The page has never posted an incident, and its record covers 29 days of the 90, so the short-record value applies, as for excaliclaw and serpkite (15). The rate-limits page says per-key limits exist and publishes no numbers, and a 4 MiB request cap was documented on 8 October (5). 429 carries `Retry-After` in seconds, errors carry a `retryable` flag, and the docs give bounded exponential backoff with jitter and rules for retrying interrupted streams (15). No published SLA. The terms of 9 September 2026 say the services have no guaranteed uptime or service credit without a separate written agreement, although the home page still shows a '99.99% Uptime SLA' tile (0). The serverless API isn't labelled beta or preview, though the OpenAPI file is at version 0.1.0 (10). 20+15+5+15+0+10 = 65." }, "scores": { "a": 25, "b": 65 } }, { "by": 31, "category": "maintenance", "leads": "hugging-face-inference-providers", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading. huggingface_hub 2.4.0 tagged on 29 October 2026 and the Inference Providers docs updated daily by an automated job, last on 6 November (30). huggingface_hub 2.2.0, 2.3.0 and 2.4.0 and @huggingface/inference 4.13.28 to 4.13.32 in the 90 days (20). No dedicated changelog; the Hub has a forum and Discord, and GitHub issue replies were not read (8 of 15). Current official SDKs in Python and JavaScript (15). The Python client supports Python 3.10 and later, per the Inference Endpoints check; CI not read (7 of 10).", "b": "Model reading. Gemma 4 31B was added to the catalogue on 6 October 2026, 29 days before this check, and nothing newer is dated in the changelog or on the blog, whose newest post is of 9 September (30). No notice period is stated. The terms promise advance notice of a material breaking change 'when reasonably practicable', and no model has been retired (3 of 12). No model shutdowns since launch (8 of 8). A dated changelog with one entry, a Discord server and a support booking link, none of which we tested for replies (8 of 15). No SDK repository to sample. The Hermes provider plugin on GitHub still has three commits, all on 15 September 2026 (0 of 10). No official SDKs (0 of 15) and no packages to assess (0 of 10). 30+3+8+8+0 = 49." }, "scores": { "a": 80, "b": 49 } }, { "by": 20, "category": "schema", "leads": "prism-inference", "name": "Schema \u0026 documentation", "reasons": { "a": "No OpenAPI document for the router (openapi.json paths answer 404). The task reference pages are generated from typed JSON Schema specs in huggingface.js, and the router's /v1/models gives a machine-readable catalogue, so 12 of 25. llms.txt for the Inference Providers docs and a Markdown twin of every page via `.md` (10). Guides say what each task and policy is for, with a quick setup page for coding agents (14 of 20). Parameter tables with types for chat completion, though `response_format` and `tool_choice` show as unknown types (9 of 15). Examples in Python, JavaScript and curl on every task page, but no documented error responses for the router (9 of 15). No changelog of its own; the Hub changelog doesn't cover it and dated changes are only in the hub-docs history (8 of 15).", "b": "A public OpenAPI 3.1 file covering ten paths, including chat completions, responses, messages and models (25). llms.txt on both the site and the docs, a fuller llms-full.txt, and every docs page served as Markdown (10). Reference pages state what each endpoint is for and what isn't supported, such as stored responses and hosted tools, and the models page has a 'use when' column (15 of 20). `model` and `messages` are required, with ranges on `temperature` and `top_p` and an enum for reasoning effort, but `tool_choice` is untyped and `response_format` and the request body allow any extra properties (10 of 15). Examples in curl, TypeScript and Python, and an errors page with 13 status codes and five named codes with fixes (14 of 15). Paths are versioned under /v1 and the changelog is dated, with a single entry on 6 October 2026, still the only one on 4 November (8 of 15). 25+10+15+10+14+8 = 82." }, "scores": { "a": 62, "b": 82 } }, { "by": 11, "category": "security", "leads": "hugging-face-inference-providers", "name": "Security \u0026 auth", "reasons": { "a": "Model reading. Fine-grained user access tokens that can be limited to the `Make calls to Inference Providers` permission, rotated and revoked, plus a public revocation endpoint for leaked tokens, and organisation approval of fine-grained tokens on paid plans (30). Hugging Face says it doesn't store user data for training, but data handling past the router follows each provider's policy, with no per-request filter to exclude providers that train or retain (12 of 20). Request bodies and responses aren't stored when routed and debug logs are kept up to 30 days without user data or tokens, with provider retention outside that statement (10 of 15). Usage by model and provider in account settings, an Enterprise usage API, and Team and Enterprise admins can disable providers and set a spending limit (13 of 15). SOC 2 Type 2 for the Hub, of which the docs say Inference Providers is a feature, and security.txt valid to 2030 per the Inference Endpoints check; no bug bounty found (14 of 20).", "b": "Model reading. Bearer keys, also accepted in `x-api-key`, issued by the workspace owner from the dashboard, with 401 for revoked or expired keys. No scopes or per-key limits were found in the reviewed documentation (20 of 30). The privacy policy, the terms, the docs and the security policy all say inputs and outputs are never used to train, fine-tune or evaluate models (20). Zero data retention is the default on every tier with no setting to turn on (15). The docs mention usage in the dashboard, which we did not see, and no audit log was found (5 of 15). security.txt is valid until 6 October 2027 with a contact address, and a security policy dated 13 October 2026 asks for vulnerability reports by email to the founders' address. No bug bounty, SOC 2 or ISO 27001 was found. robotomail, with a valid security.txt and reports by email and nothing more, holds 7 (8 of 20). 20+20+15+5+8 = 68." }, "scores": { "a": 79, "b": 68 } }, { "by": 10, "category": "ergonomics", "leads": "prism-inference", "name": "Agent ergonomics", "reasons": { "a": "Router reading of the model checklist (tool use, structured output, caching, context, batch, SDKs, errors), as for openrouter. Tool calling through the OpenAI-compatible API with `tool_choice`, support flagged per provider in /v1/models (15 of 20). Structured output documented, support flagged per provider and false on some (10 of 15). No prompt caching guidance found, though the Enterprise usage API counts cached input tokens (3 of 15). Routes to models with up to 1,048,576 tokens of context per the model list, a judgement call since a router has no default model (15). No batch API (0 of 10). Official clients in Python and JavaScript, plus any OpenAI SDK (10). Errors not documented (5 of 15).", "b": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). OpenAI function tools, Anthropic tool-use blocks, `tool_choice` and `parallel_tool_calls` on Responses are documented (18 of 20). JSON mode and strict JSON Schema on Chat Completions, and `text.format` on Responses (15). A cache-read price is published for both models, $0.03 against $0.09 input on DeepSeek-V4.1-Flash on 4 November, but no page explains how caching is triggered, and the Claude Code guide says prompt-cache hints are accepted and ignored (5 of 15). 1M-token context and 384,000 output tokens on DeepSeek-V4.1-Flash, 32,768 on Gemma 4 31B (15). Batch appears on the pricing page as a sales-led tier with no API in the OpenAPI file (0). No SDK of its own. OpenAI and Anthropic clients are used with a base URL change (0). The errors page (8 October) and the rate-limits page (4 November) document `code`, `retryable`, `fix` and `docs_url` on every error (15). 18+15+5+15+0+0+15 = 68." }, "scores": { "a": 58, "b": 68 } }, { "by": 8, "category": "transparency", "leads": "hugging-face-inference-providers", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed service under Terms of Service that name Inference Providers, with an Apache-2.0 Python client and an MIT JavaScript client (20). The security page and the docs agree on no training, no stored bodies and 30-day logs, but the privacy policy dates from 28 March 2023 and each provider's own data policy governs the rest (18 of 30). No deprecation policy found. Provider support changes and the 7 October removal of free credits are dated only in the docs repository history (5 of 20). The privacy policy lists 11 subprocessors with countries per the Inference Endpoints check, and the docs name every provider a request can reach, though not where each runs (15 of 20).", "b": "Closed service with clear terms that name Prism Technologies Inc (15). The privacy policy, the terms and the zero-retention page agree on what is and isn't kept. Service metadata is kept 'only for as long as reasonably needed' with no period, and no DPA is published (20 of 30). No deprecation policy or dated notices, only the terms' advance notice 'when reasonably practicable' (3 of 20). The privacy policy says Prism is based in the United States and names vendors by category only, with no sub-processor list or regions (5 of 20). 15+20+3+5 = 43." }, "scores": { "a": 71, "b": 63 } } ], "facts": [ { "a": "Model router", "b": "Model API", "name": "Kind" }, { "a": "Hugging Face, Inc.", "b": "Prism Technologies Inc", "name": "Vendor" }, { "a": "https://router.huggingface.co/v1", "b": "https://api.prisminference.com/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary service under the Hugging Face Terms of Service, which name Inference Providers. The `huggingface_hub` Python client is Apache-2.0 and `@huggingface/inference` is MIT", "b": "Proprietary service under Prism's terms of service. The OpenAPI file declares `LicenseRef-Proprietary`. The Hermes provider plugin repository carries no licence file", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-29", "b": "2026-10-06", "name": "Last release" }, { "a": "2022-09-15", "b": "2026-09-09", "name": "Terms last updated" }, { "a": "2023-03-28", "b": "2026-09-09", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "yes", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "yes", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "483k npm/wk, 71.5M PyPI/wk", "b": "none", "name": "Popularity" }, { "a": "none", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Prism Inference scores 59.6 (C) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in 3 of 7 scored categories. Hugging Face Inference Providers leads on security \u0026 auth, maintenance \u0026 community and transparency \u0026 trust.", "question": "Which is better for AI agents, Hugging Face Inference Providers or Prism Inference?" }, { "answer": "Both need an API key.", "question": "Do Hugging Face Inference Providers and Prism Inference need an API key?" }, { "answer": "Yes. Hugging Face Inference Providers has a hosted endpoint at https://router.huggingface.co/v1 and Prism Inference at https://api.prisminference.com/v1.", "question": "Can an agent call Hugging Face Inference Providers and Prism Inference without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Security \u0026 auth, 79 against 68", "Maintenance \u0026 community, 80 against 49", "Transparency \u0026 trust, 71 against 63" ], "also": null, "goodFor": "Agents and developers who want open-weight models from several hosts on one token and one bill, with a public price and capability list per provider.", "slug": "hugging-face-inference-providers", "watchFor": "No rate limits, 429 behaviour or error responses documented for the router" }, { "aheadOn": [ "Reliability, 65 against 25", "Schema \u0026 documentation, 82 against 62", "Agent ergonomics, 68 against 58" ], "also": null, "goodFor": "Coding agents that want DeepSeek-V4.1-Flash at a low input price, with no retention, through whichever of the three wire formats the harness already speaks.", "slug": "prism-inference", "watchFor": "Two models. The docs mark Gemma 4 31B as request access per organisation, while llms.txt lists it as available" } ], "history": { "hugging-face-inference-providers": null, "prism-inference": [ { "causes": [ "security", "product" ], "date": "2026-11-04", "delta": -0.7, "from": 60.3, "fromGrade": "C", "to": 59.6, "toGrade": "C" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.2, "from": 60.1, "fromGrade": "C", "to": 60.3, "toGrade": "C" } ] }, "job": { "capability": "inference.open-weights", "name": "Open-weight models" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-hugging-face-inference-providers.json", "title": "Amazon Bedrock vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-prism-inference.json", "title": "Amazon Bedrock vs Prism Inference", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-hugging-face-inference-providers.json", "title": "Claude API vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-prism-inference.json", "title": "Claude API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-prism-inference.json", "title": "Antseed vs Prism Inference", "url": "https://www.anchorterminal.com/compare/antseed-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-hugging-face-inference-providers.json", "title": "Azure OpenAI in Foundry Models vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-prism-inference.json", "title": "Azure OpenAI in Foundry Models vs Prism Inference", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-hugging-face-inference-providers.json", "title": "BlockRun.AI vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-prism-inference.json", "title": "BlockRun.AI vs Prism Inference", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-hugging-face-inference-providers.json", "title": "Cerebras Inference vs Hugging Face Inference Providers", "url": 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"https://www.anchorterminal.com/compare/prism-inference-vs-stepfun-api.json", "title": "Prism Inference vs StepFun API", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-swe-2-flash.json", "title": "Prism Inference vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-together-inference.json", "title": "Prism Inference vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-together-inference" }, { "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/prism-inference-vs-xai-api.json", "title": "Prism Inference vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-hugging-face-inference-providers.json", "title": "Antseed vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/antseed-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-hugging-face-inference-providers.json", "title": "Eden AI vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-litellm.json", "title": "Hugging Face Inference Providers vs LiteLLM", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-litellm" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-openrouter.json", "title": "Hugging Face Inference Providers vs OpenRouter", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-ramp-router.json", "title": "Hugging Face Inference Providers vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-routerplus.json", "title": "Hugging Face Inference Providers vs RouterPlus", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-tencent-tokenhub.json", "title": "Hugging Face Inference Providers vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-vercel-ai-gateway.json", "title": "Hugging Face Inference Providers vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-nemotron-3-5-super-vl.json", "title": "Hugging Face Inference Providers vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-prism-inference.json", "title": "Nemotron 3.5 Super VL vs Prism Inference", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-tencent-tokenhub.json", "title": "Prism Inference vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-tencent-tokenhub" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-vercel-ai-gateway.json", "title": "Prism Inference vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-vercel-ai-gateway" } ], "panel": null, "scores": [ { "by": 40, "edge": "prism-inference", "hugging-face-inference-providers": 25, "key": "reliability", "name": "Reliability", "prism-inference": 65, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 20, "edge": "prism-inference", "hugging-face-inference-providers": 62, "key": "schema", "name": "Schema \u0026 documentation", "prism-inference": 82, "weight": 13 }, { "by": 10, "edge": "prism-inference", "hugging-face-inference-providers": 58, "key": "ergonomics", "name": "Agent ergonomics", "prism-inference": 68, "weight": 13 }, { "by": 11, "edge": "hugging-face-inference-providers", "hugging-face-inference-providers": 79, "key": "security", "name": "Security \u0026 auth", "prism-inference": 68, "weight": 14 }, { "by": 0, "edge": "", "hugging-face-inference-providers": 20, "key": "payments", "name": "Payments \u0026 pricing", "prism-inference": 20, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 31, "edge": "hugging-face-inference-providers", "hugging-face-inference-providers": 80, "key": "maintenance", "name": "Maintenance \u0026 community", "prism-inference": 49, "weight": 7 }, { "by": 8, "edge": "hugging-face-inference-providers", "hugging-face-inference-providers": 71, "key": "transparency", "name": "Transparency \u0026 trust", "prism-inference": 63, "weight": 7 } ], "summary": "Prism Inference scores 59.6 (C) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in 3 of 7 scored categories. Hugging Face Inference Providers leads on security \u0026 auth, maintenance \u0026 community and transparency \u0026 trust. Both do open-weight models.", "tasks": null, "tutorials": [], "verdicts": { "hugging-face-inference-providers": "One Hugging Face token reaches open models on 18 partner providers at the providers' own prices, with a public model list that shows each provider's price, context and tool support. Router rate limits, error responses and its own status component were not found in the reviewed documentation, and free accounts have had no included credits since 7 October 2026.", "prism-inference": "Three wire formats, a public OpenAPI 3.1 file, per-token prices without a login and zero data retention by default on every tier. The service launched on 24 September 2026 with two models, one by request. Agent self-signup has closed and new access goes through a sales call, and no rate-limit numbers, SLA document or deprecation policy was found." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-prism-inference", "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-prism-inference.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-prism-inference.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-prism-inference.min.md" }, "markdown": "Prism Inference scores 59.6 (C) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in 3 of 7 scored categories. Hugging Face Inference Providers leads on security \u0026 auth, maintenance \u0026 community and transparency \u0026 trust. Both do open-weight models.\n\n- Hugging Face Inference Providers: grade C, 54/100, rank #926 of 1510. Markdown https://www.anchorterminal.com/tools/hugging-face-inference-providers.md · JSON https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-providers.json\n- Prism Inference: grade C, 59.6/100, rank #691 of 1510. Markdown https://www.anchorterminal.com/tools/prism-inference.md · JSON https://www.anchorterminal.com/api/v1/tools/prism-inference.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### Hugging Face Inference Providers (C)\n\nGood for: Agents and developers who want open-weight models from several hosts on one token and one bill, with a public price and capability list per provider.\n\nAhead on:\n- Security \u0026 auth, 79 against 68\n- Maintenance \u0026 community, 80 against 49\n- Transparency \u0026 trust, 71 against 63\n\nWatch for: No rate limits, 429 behaviour or error responses documented for the router\n\n### Prism Inference (C)\n\nGood for: Coding agents that want DeepSeek-V4.1-Flash at a low input price, with no retention, through whichever of the three wire formats the harness already speaks.\n\nAhead on:\n- Reliability, 65 against 25\n- Schema \u0026 documentation, 82 against 62\n- Agent ergonomics, 68 against 58\n\nWatch for: Two models. The docs mark Gemma 4 31B as request access per organisation, while llms.txt lists it as available\n\n\n## Why the edge\n\n### Reliability: Prism Inference +40\n\n- Hugging Face Inference Providers (25): Hosted lines. status.huggingface.co is a Better Stack page with 90-day component history for the Hub, Inference Endpoints, Spaces, the CDNs and Jobs, but no component for Inference Providers or the router (10 of 20). With no component, there is no readable incident history for the surface agents call, and the feed's 23 items from 10 June to 7 October 2026 don't name it (5). No rate limits for the router were found; the Hub rate-limits page gives numbers for Hub APIs, resolvers and pages only (0). No 429 or overload guidance for the router was found (0). No SLA found (0). Chat completions on the router is generally available; the Responses API is labelled beta (10). Routing between providers with `:fastest` or `:cheapest` softens a single provider's outage, which this checklist doesn't score.\n- Prism Inference (65): Hosted reading. Status page at status.prisminference.com on incident.io with a component for each of the two models, per the 8 October check (20). The 8 October incident reading came only from the page's `/api/v2` incidents feed, a data endpoint this run doesn't use, so it is struck. On 4 November the status host's robots.txt answered a plain 404 (no rules), and the status page itself shows both models at 100 per cent uptime from 5 August to 3 November with \"No incidents\", data available only from 6 October 2026. The page has never posted an incident, and its record covers 29 days of the 90, so the short-record value applies, as for excaliclaw and serpkite (15). The rate-limits page says per-key limits exist and publishes no numbers, and a 4 MiB request cap was documented on 8 October (5). 429 carries `Retry-After` in seconds, errors carry a `retryable` flag, and the docs give bounded exponential backoff with jitter and rules for retrying interrupted streams (15). No published SLA. The terms of 9 September 2026 say the services have no guaranteed uptime or service credit without a separate written agreement, although the home page still shows a '99.99% Uptime SLA' tile (0). The serverless API isn't labelled beta or preview, though the OpenAPI file is at version 0.1.0 (10). 20+15+5+15+0+10 = 65.\n\n### Maintenance \u0026 community: Hugging Face Inference Providers +31\n\n- Hugging Face Inference Providers (80): Model reading. huggingface_hub 2.4.0 tagged on 29 October 2026 and the Inference Providers docs updated daily by an automated job, last on 6 November (30). huggingface_hub 2.2.0, 2.3.0 and 2.4.0 and @huggingface/inference 4.13.28 to 4.13.32 in the 90 days (20). No dedicated changelog; the Hub has a forum and Discord, and GitHub issue replies were not read (8 of 15). Current official SDKs in Python and JavaScript (15). The Python client supports Python 3.10 and later, per the Inference Endpoints check; CI not read (7 of 10).\n- Prism Inference (49): Model reading. Gemma 4 31B was added to the catalogue on 6 October 2026, 29 days before this check, and nothing newer is dated in the changelog or on the blog, whose newest post is of 9 September (30). No notice period is stated. The terms promise advance notice of a material breaking change 'when reasonably practicable', and no model has been retired (3 of 12). No model shutdowns since launch (8 of 8). A dated changelog with one entry, a Discord server and a support booking link, none of which we tested for replies (8 of 15). No SDK repository to sample. The Hermes provider plugin on GitHub still has three commits, all on 15 September 2026 (0 of 10). No official SDKs (0 of 15) and no packages to assess (0 of 10). 30+3+8+8+0 = 49.\n\n### Schema \u0026 documentation: Prism Inference +20\n\n- Hugging Face Inference Providers (62): No OpenAPI document for the router (openapi.json paths answer 404). The task reference pages are generated from typed JSON Schema specs in huggingface.js, and the router's /v1/models gives a machine-readable catalogue, so 12 of 25. llms.txt for the Inference Providers docs and a Markdown twin of every page via `.md` (10). Guides say what each task and policy is for, with a quick setup page for coding agents (14 of 20). Parameter tables with types for chat completion, though `response_format` and `tool_choice` show as unknown types (9 of 15). Examples in Python, JavaScript and curl on every task page, but no documented error responses for the router (9 of 15). No changelog of its own; the Hub changelog doesn't cover it and dated changes are only in the hub-docs history (8 of 15).\n- Prism Inference (82): A public OpenAPI 3.1 file covering ten paths, including chat completions, responses, messages and models (25). llms.txt on both the site and the docs, a fuller llms-full.txt, and every docs page served as Markdown (10). Reference pages state what each endpoint is for and what isn't supported, such as stored responses and hosted tools, and the models page has a 'use when' column (15 of 20). `model` and `messages` are required, with ranges on `temperature` and `top_p` and an enum for reasoning effort, but `tool_choice` is untyped and `response_format` and the request body allow any extra properties (10 of 15). Examples in curl, TypeScript and Python, and an errors page with 13 status codes and five named codes with fixes (14 of 15). Paths are versioned under /v1 and the changelog is dated, with a single entry on 6 October 2026, still the only one on 4 November (8 of 15). 25+10+15+10+14+8 = 82.\n\n### Security \u0026 auth: Hugging Face Inference Providers +11\n\n- Hugging Face Inference Providers (79): Model reading. Fine-grained user access tokens that can be limited to the `Make calls to Inference Providers` permission, rotated and revoked, plus a public revocation endpoint for leaked tokens, and organisation approval of fine-grained tokens on paid plans (30). Hugging Face says it doesn't store user data for training, but data handling past the router follows each provider's policy, with no per-request filter to exclude providers that train or retain (12 of 20). Request bodies and responses aren't stored when routed and debug logs are kept up to 30 days without user data or tokens, with provider retention outside that statement (10 of 15). Usage by model and provider in account settings, an Enterprise usage API, and Team and Enterprise admins can disable providers and set a spending limit (13 of 15). SOC 2 Type 2 for the Hub, of which the docs say Inference Providers is a feature, and security.txt valid to 2030 per the Inference Endpoints check; no bug bounty found (14 of 20).\n- Prism Inference (68): Model reading. Bearer keys, also accepted in `x-api-key`, issued by the workspace owner from the dashboard, with 401 for revoked or expired keys. No scopes or per-key limits were found in the reviewed documentation (20 of 30). The privacy policy, the terms, the docs and the security policy all say inputs and outputs are never used to train, fine-tune or evaluate models (20). Zero data retention is the default on every tier with no setting to turn on (15). The docs mention usage in the dashboard, which we did not see, and no audit log was found (5 of 15). security.txt is valid until 6 October 2027 with a contact address, and a security policy dated 13 October 2026 asks for vulnerability reports by email to the founders' address. No bug bounty, SOC 2 or ISO 27001 was found. robotomail, with a valid security.txt and reports by email and nothing more, holds 7 (8 of 20). 20+20+15+5+8 = 68.\n\n### Agent ergonomics: Prism Inference +10\n\n- Hugging Face Inference Providers (58): Router reading of the model checklist (tool use, structured output, caching, context, batch, SDKs, errors), as for openrouter. Tool calling through the OpenAI-compatible API with `tool_choice`, support flagged per provider in /v1/models (15 of 20). Structured output documented, support flagged per provider and false on some (10 of 15). No prompt caching guidance found, though the Enterprise usage API counts cached input tokens (3 of 15). Routes to models with up to 1,048,576 tokens of context per the model list, a judgement call since a router has no default model (15). No batch API (0 of 10). Official clients in Python and JavaScript, plus any OpenAI SDK (10). Errors not documented (5 of 15).\n- Prism Inference (68): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). OpenAI function tools, Anthropic tool-use blocks, `tool_choice` and `parallel_tool_calls` on Responses are documented (18 of 20). JSON mode and strict JSON Schema on Chat Completions, and `text.format` on Responses (15). A cache-read price is published for both models, $0.03 against $0.09 input on DeepSeek-V4.1-Flash on 4 November, but no page explains how caching is triggered, and the Claude Code guide says prompt-cache hints are accepted and ignored (5 of 15). 1M-token context and 384,000 output tokens on DeepSeek-V4.1-Flash, 32,768 on Gemma 4 31B (15). Batch appears on the pricing page as a sales-led tier with no API in the OpenAPI file (0). No SDK of its own. OpenAI and Anthropic clients are used with a base URL change (0). The errors page (8 October) and the rate-limits page (4 November) document `code`, `retryable`, `fix` and `docs_url` on every error (15). 18+15+5+15+0+0+15 = 68.\n\n### Transparency \u0026 trust: Hugging Face Inference Providers +8\n\n- Hugging Face Inference Providers (71): Closed service under Terms of Service that name Inference Providers, with an Apache-2.0 Python client and an MIT JavaScript client (20). The security page and the docs agree on no training, no stored bodies and 30-day logs, but the privacy policy dates from 28 March 2023 and each provider's own data policy governs the rest (18 of 30). No deprecation policy found. Provider support changes and the 7 October removal of free credits are dated only in the docs repository history (5 of 20). The privacy policy lists 11 subprocessors with countries per the Inference Endpoints check, and the docs name every provider a request can reach, though not where each runs (15 of 20).\n- Prism Inference (63): Closed service with clear terms that name Prism Technologies Inc (15). The privacy policy, the terms and the zero-retention page agree on what is and isn't kept. Service metadata is kept 'only for as long as reasonably needed' with no period, and no DPA is published (20 of 30). No deprecation policy or dated notices, only the terms' advance notice 'when reasonably practicable' (3 of 20). The privacy policy says Prism is based in the United States and names vendors by category only, with no sub-processor list or regions (5 of 20). 15+20+3+5 = 43.\n\n## Score by category\n\n| Category | Weight | Hugging Face Inference Providers | Prism Inference | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 25 | 65 | Prism Inference +40 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 62 | 82 | Prism Inference +20 |\n| Agent ergonomics | 13% (16.2 this run) | 58 | 68 | Prism Inference +10 |\n| Security \u0026 auth | 14% (17.5 this run) | 79 | 68 | Hugging Face Inference Providers +11 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 20 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 80 | 49 | Hugging Face Inference Providers +31 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 71 | 63 | Hugging Face Inference Providers +8 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **54 · C** | **59.6 · C** | |\n\n## Facts side by side\n\n| Fact | Hugging Face Inference Providers | Prism Inference |\n| --- | --- | --- |\n| Kind | Model router | Model API |\n| Vendor | Hugging Face, Inc. | Prism Technologies Inc |\n| Hosted endpoint | `https://router.huggingface.co/v1` | `https://api.prisminference.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Proprietary service under the Hugging Face Terms of Service, which name Inference Providers. The `huggingface_hub` Python client is Apache-2.0 and `@huggingface/inference` is MIT | Proprietary service under Prism's terms of service. The OpenAPI file declares `LicenseRef-Proprietary`. The Hermes provider plugin repository carries no licence file |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-29 | 2026-10-06 |\n| Terms last updated | 2022-09-15 | 2026-09-09 |\n| Privacy policy last updated | 2023-03-28 | 2026-09-09 |\n| Customer content may train models | not found in the text | not found in the text |\n| Terms restrict automated access | not found in the text | yes |\n| Terms restrict benchmarking | not found in the text | not found in the text |\n| Terms or service can change without notice | yes | not found in the text |\n| Arbitration or class-action waiver | not found in the text | yes |\n| Popularity | 483k npm/wk, 71.5M PyPI/wk | none |\n| Agent reviews | none | 3.5/5 (4) |\n\n## How the scores have moved\n\n### Hugging Face Inference Providers\n\nNo score change since it was listed.\n\n### Prism Inference\n\n- -0.7 on 4 November 2026: security, product\n- +0.2 on 27 October 2026: checklist\n\n## Verdicts\n\n**Hugging Face Inference Providers.** One Hugging Face token reaches open models on 18 partner providers at the providers' own prices, with a public model list that shows each provider's price, context and tool support. Router rate limits, error responses and its own status component were not found in the reviewed documentation, and free accounts have had no included credits since 7 October 2026.\n\n**Prism Inference.** Three wire formats, a public OpenAPI 3.1 file, per-token prices without a login and zero data retention by default on every tier. The service launched on 24 September 2026 with two models, one by request. Agent self-signup has closed and new access goes through a sales call, and no rate-limit numbers, SLA document or deprecation policy was found.\n\n## Before you call either\n\n### Hugging Face Inference Providers\n\n1. Read https://router.huggingface.co/v1/models before choosing a provider; it shows price, context and `supports_tools` per provider\n2. Pin a provider with `model:\u003cprovider\u003e` when tool calling or structured output matters, since support varies by provider\n3. Use a fine-grained token with only the Inference Providers permission, and send `X-HF-Bill-To` to bill an organisation\n4. Back off on 429 without assuming a Retry-After header, since none is documented for the router\n\n### Prism Inference\n\n1. Call `GET https://api.prisminference.com/v1/models` at start-up, with no key per the docs, and use only ids it returns. Expect 403 on `gemma-4-31b` without organisation access\n2. Use base URL `https://api.prisminference.com/v1` for OpenAI clients and `https://api.prisminference.com` with no `/v1` for Anthropic clients\n3. Read `error.retryable` before retrying, and wait for `Retry-After` on 429, which covers both key limits and model capacity\n4. Send `reasoning_effort: \"none\"` or `low` when latency matters. Reasoning is on by default and its tokens are billed as output\n5. Keep conversation state yourself and send `store: false` on Responses. `previous_response_id`, stored responses and hosted tools aren't supported\n6. Ask the workspace owner for `PRISM_API_KEY`. The agent sign-up endpoints now return `403 signup_disabled`\n\n## Questions\n\n### Which is better for AI agents, Hugging Face Inference Providers or Prism Inference?\n\nPrism Inference scores 59.6 (C) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in 3 of 7 scored categories. Hugging Face Inference Providers leads on security \u0026 auth, maintenance \u0026 community and transparency \u0026 trust.\n\n### Do Hugging Face Inference Providers and Prism Inference need an API key?\n\nBoth need an API key.\n\n### Can an agent call Hugging Face Inference Providers and Prism Inference without installing anything?\n\nYes. Hugging Face Inference Providers has a hosted endpoint at https://router.huggingface.co/v1 and Prism Inference at https://api.prisminference.com/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-prism-inference.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-prism-inference.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hugging-face-inference-providers\", \"b\": \"prism-inference\"}`. 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"name": "Hugging Face Inference Providers vs Prism Inference", "url": "" } ], "description": "Prism Inference scores 59.6 (C) to Hugging Face Inference Providers's 54 (C) for open-weight models. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Hugging Face Inference Providers C 54", "Prism Inference C 59.6", "scores" ], "h1": "Hugging Face Inference Providers vs Prism Inference", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/hugging-face-inference-providers-vs-prism-inference", "published": "2026-10-01", "section": "tools", "title": "Hugging Face Inference Providers vs Prism Inference (2026)", "toc": null, "updated": "2026-11-07", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-prism-inference" }, "tokens": { "markdown": 7450, "slim": 3430 }, "version": 1 }