{ "data": { "a": { "slug": "gpusmarket", "name": "GPUsMarket", "vendor": "Tyga.Cloud Ltd", "vendorUrl": "https://gpusmarket.com", "kind": "http-api", "category": "gpu-compute", "summary": "GPUsMarket is a peer-to-peer GPU marketplace from Tyga.Cloud Ltd. Agents rent hosts' NVIDIA GPUs and Macs, or on-demand cloud GPUs, as Ollama and OpenAI-compatible endpoints billed per second, through a REST API, CLI and MCP server.", "url": "https://www.anchorterminal.com/tools/gpusmarket", "markdownUrl": "https://www.anchorterminal.com/tools/gpusmarket.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gpusmarket.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gpusmarket.json", "dns": "gpusmarket.grade.anchorterminal.com", "repo": "https://github.com/jyswee/gpusmarket", "license": "Proprietary. The gpusmarket CLI and gpusmarket-host packages ship under Tyga.Cloud Ltd licences that allow use only with the platform, as obfuscated JavaScript", "transports": [ "stdio", "sse" ], "remoteUrl": "https://gpusmarket.com/api/v1", "packages": [ { "registry": "npm", "name": "gpusmarket" }, { "registry": "npm", "name": "gpusmarket-host" } ], "auth": "api-key", "authNotes": "An account key (`gpu_` prefix) from `POST /api/signup` with an email, or from Settings in the web app, covers searching, renting and stopping. Each rental returns its own token for the Ollama or OpenAI-compatible proxy on that rental, and GPU pools take `pool_` keys. No scopes, expiry or rotation documented. Renting places a hold on a saved card.", "pricing": "usage", "pricingNotes": "Per second at each listing's hourly price, from $0.08 an hour (RTX 3060) on the home page, with a $0.50 minimum per rental. Hosts can also bill per request and per token; image, audio and video listings bill per unit. A $10 to $100 card hold is placed per rental. No subscription or prepaid credit; fees are non-refundable.", "priceSummary": "$0.50 / tx", "where": "both", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in llms.txt, the docs or the terms (checked 2026-11-06). Payment is a Stripe card hold settled when a rental stops.", "endpoints": [] }, "toolCount": 32, "popularity": { "githubStars": 1, "npmWeekly": 79, "pypiWeekly": null, "asOf": "2026-11-06" }, "docsUrl": "https://gpusmarket.com/docs", "llmsTxt": "https://gpusmarket.com/llms.txt", "registryName": "io.github.jyswee/gpusmarket", "capabilities": [ "compute.gpu", "compute.endpoints", "compute.containers" ], "tags": [ "hosted", "usage", "per-second", "mcp", "mcp-registry", "llms-txt", "cli", "ollama", "openai-compatible", "peer-to-peer", "card-hold", "uk" ], "lastRelease": "2026-10-16", "graded": true, "anchor": { "graded": true, "score": 39.6, "grade": "E", "agentReady": false, "rank": 1337, "ranked": true, "rankOf": 1510, "categoryRank": 21, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 50, "maintenance": 69, "payments": 30, "reliability": 33, "schema": 52, "security": 30, "transparency": 50 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-06" }, "negative": -3, "negativeNotes": [ "2026-11-06. The docs' Open Source section lists gpusmarket-host, the package hosts install with `npx gpusmarket-host`, under the MIT licence, while version 1.6.0 on npm (8 October 2026) declares `SEE LICENSE IN LICENSE`, ships a proprietary Tyga.Cloud Ltd licence and is built with javascript-obfuscator. A misleading licence claim on the vendor's own page, 3 points. https://gpusmarket.com/docs, https://registry.npmjs.org/gpusmarket-host" ], "verdict": "An agent can sign up with one API call, search priced GPUs without a key, rent a peer or cloud GPU as an Ollama or OpenAI-compatible endpoint and stop it, billed per second. Prompts on peer rentals pass through individuals' machines, the CLI is obfuscated, and there is no status page, security policy or free tier.", "bestFor": "Cheap, short GPU rentals for open models behind an Ollama or OpenAI endpoint, where data sensitivity is low.", "strengths": [ "Sign-up and key issue in one API call, and search and GPU prices need no key", "Per-second billing from $0.08 an hour, with a $0.50 minimum per rental", "Every rental speaks both the Ollama and OpenAI formats, and pools spread load across rentals", "llms.txt is a complete API reference with rate limits and error codes", "Listed in the official MCP registry under a verified GitHub namespace" ], "weaknesses": [ "Peer rentals send prompts and outputs through individuals' hardware, with only the terms as protection", "The CLI and host packages are obfuscated, and the docs call the host package MIT while it is proprietary", "No status page, SLA, security.txt or disclosure policy", "Account keys have no scopes or expiry, and renting needs a saved card", "No OpenAPI document or public changelog" ], "agentNotes": [ "Don't send confidential data to peer rentals, since requests pass through the host's machine", "Stop every rental with `POST /api/v1/marketplace/rentals/{id}/stop`, because billing runs per second until it stops", "Use the per-rental token for inference and keep the `gpu_` account key out of inference clients", "Treat 502 as host offline and rent another listing rather than retrying the same one", "Ask the person to add a card before renting, since each rental places a hold on it" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "E", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 39.6 } ], "editorialScores": { "ergonomics": 50, "maintenance": 69, "payments": 30, "reliability": 33, "schema": 52, "security": 30, "transparency": 36 }, "provenanceScore": 63, "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": "npm install -g gpusmarket", "http": "curl \"https://gpusmarket.com/api/v1/marketplace/search?gpu=RTX%204090\u0026maxPrice=1.00\"", "claudeCode": "claude mcp add gpusmarket -- gpusmarket mcp-serve" }, "letme": { "capability": "https://letme.dev/compute.gpu", "tool": "https://letme.dev/gpusmarket" }, "area": "models", "unitPrices": [ { "item": "RTX 3060, lowest listed", "unit": "gpu-hour", "usd": 0.08, "note": "home page, billed per second" }, { "item": "RTX 4090", "unit": "gpu-hour", "usd": 0.35, "note": "home page example; the docs' cloud deploy example shows $0.51" }, { "item": "Minimum per rental", "unit": "tx", "usd": 0.5, "note": "charged when metered usage is lower" } ], "provenance": { "legalEntity": "Tyga.Cloud Ltd", "domain": "gpusmarket.com", "domainRegistered": "2025-03-24", "endpointOnVendorDomain": true, "terms": "https://gpusmarket.com/terms", "privacy": "https://gpusmarket.com/privacy", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-06", "notes": [ "The terms (v2.0, June 2026) and privacy policy (March 2026) name Tyga.Cloud Ltd, company 14643275, registered in England and Wales, as operator and data controller.", "Companies House lists Kay Sujittree Somsuk and Joseph Wee as directors and secretaries of Tyga.Cloud Ltd.", "gpusmarket.com/robots.txt allows public pages and disallows /api/, /host/, /renter/, /settings/ and /webhooks/. The terms contain no clause on automated access.", "RDAP gives gpusmarket.com a registration date of 24 March 2025.", "/.well-known/security.txt returns 404. No status page or changelog page found.", "The terms promise 30 days' notice of material changes and give no availability guarantee." ], "score": 63 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gpusmarket.json", "live": { "slug": "gpusmarket", "probe": { "target": "https://gpusmarket.com/api/v1", "method": "get", "lastAt": "2026-11-07T19:22:09.857483355Z", "lastOk": true, "lastStatus": 404, "lastMs": 168, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 159, "p95ms24h": 250, "samples24h": 210, "samples30d": 210, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 13 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "versions": [ { "registry": "mcp-registry", "name": "io.github.jyswee/gpusmarket", "version": "2.4.16", "seenAt": "2026-11-07T18:16:28.95801333Z" }, { "registry": "npm", "name": "gpusmarket", "version": "2.5.1", "seenAt": "2026-11-06T23:30:34.572543077Z" }, { "registry": "npm", "name": "gpusmarket-host", "version": "1.6.0", "seenAt": "2026-11-06T23:30:35.110678472Z" } ], "githubStars": 1, "npmWeekly": 79, "securityTxt": { "url": "https://gpusmarket.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:01:37.738841214Z" }, "llmsTxt": { "url": "https://gpusmarket.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:37.836383448Z" }, "pages": [ { "url": "https://gpusmarket.com/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:10:35.479026672Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "dcad60427e70" }, { "url": "https://gpusmarket.com/terms", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:10:37.570489363Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "7d54e59bcd38" } ], "updatedAt": "2026-11-07T19:22:09.857483355Z" } }, "answer": "Hugging Face Inference Endpoints scores 66.7 (B) on agent readiness against GPUsMarket's 39.6 (E), and leads in 6 of 7 scored categories. GPUsMarket leads on payments \u0026 pricing.", "b": { "slug": "hugging-face-inference-endpoints", "name": "Hugging Face Inference Endpoints", "vendor": "Hugging Face, Inc.", "vendorUrl": "https://huggingface.co", "kind": "http-api", "category": "gpu-compute", "summary": "Managed Hugging Face service that deploys a Hub model as a dedicated, autoscaling HTTPS endpoint on AWS, Azure or Google Cloud, using vLLM, TGI, SGLang, llama.cpp, TEI or a custom container. Managed by REST API, Python client, CLI or MCP.", "url": "https://www.anchorterminal.com/tools/hugging-face-inference-endpoints", "markdownUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-endpoints.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-endpoints.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-endpoints.json", "dns": "hugging-face-inference-endpoints.grade.anchorterminal.com", "repo": "https://github.com/huggingface/hf-endpoints-documentation", "license": "Proprietary service under the Hugging Face Terms of Service. The `huggingface_hub` Python client and CLI are Apache-2.0", "transports": [ "http" ], "remoteUrl": "https://api.endpoints.huggingface.cloud", "packages": [ { "registry": "pypi", "name": "huggingface_hub" } ], "auth": "mixed", "authNotes": "Hugging Face access token sent as `Authorization: Bearer $HF_TOKEN` to the management API and to each endpoint. Tokens are created in the account settings in a browser and can be fine-grained, read or write. The MCP server uses OAuth through huggingface.co (authorisation code with PKCE, device code, dynamic client registration) with `read-endpoints` and `write-endpoints` scopes. `GET /v2/provider` and the catalogue list need no token. Access is self-serve, with quota requests for larger instances.", "pricing": "usage", "pricingNotes": "Usage priced by instance hour, billed per minute while a replica is initialising or running. GPUs run from $0.50 an hour (T4) to $10 (H100 on GCP), CPUs from $0.033. No free tier or trial was found. The docs require a payment method and credits before deploying, and the pricing page says an active subscription. Paused endpoints and endpoints at zero replicas aren't billed for compute (https://huggingface.co/docs/inference-endpoints/support/pricing).", "priceSummary": "$0.033 / vCPU-hr", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the Inference Endpoints docs, the two OpenAPI documents or the pricing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": 19, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": 60014944, "asOf": "2026-10-08" }, "docsUrl": "https://huggingface.co/docs/inference-endpoints/index", "llmsTxt": "https://huggingface.co/docs/inference-endpoints/llms.txt", "openapi": "https://api.endpoints.huggingface.cloud/openapi.json", "capabilities": [ "compute.gpu", "compute.endpoints", "compute.containers" ], "tags": [ "hosted", "usage-priced", "python", "cli", "mcp", "oauth", "openapi", "llms-txt", "status-page", "soc2", "enterprise" ], "lastRelease": "2026-10-29", "graded": true, "anchor": { "graded": true, "score": 66.7, "grade": "B", "agentReady": false, "rank": 358, "ranked": true, "rankOf": 1510, "categoryRank": 3, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 62, "maintenance": 80, "payments": 20, "reliability": 73, "schema": 73, "security": 83, "transparency": 70 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-02" }, "negative": 0, "verdict": "OAuth scopes separate reading endpoints from writing them, both OpenAPI documents are public, and the unauthenticated `/v2/provider` route lists every instance with its hourly price. An account needs a payment method and credits before the first deployment, no rate limits or SLA were found for the management API, and the docs price table disagrees with the live list in places.", "bestFor": "Teams whose models already live on the Hugging Face Hub and who want a dedicated endpoint on a named cloud and region with standard open-source engines.", "strengths": [ "Public OpenAPI 3.1 documents for the management API (46 operations) and the catalogue API (3), plus llms.txt and a Markdown twin of every docs page", "The MCP server at endpoints.huggingface.co/mcp uses OAuth with `read-endpoints` and `write-endpoints` scopes, PKCE and dynamic client registration", "`GET /v2/provider` needs no token and returns each instance type by cloud and region with status and price per hour", "The MCP `delete_endpoint` tool returns a preview and deletes only on a second call with `confirm: true`", "The Inference Endpoints UI and API components on status.huggingface.co show 100 per cent uptime over the 90 days to 2 November 2026" ], "weaknesses": [ "No free tier. The docs require a payment method and credits, and replicas are billed while initialising as well as running", "No rate limits, SLA or idempotency keys were found for the management API, and its OpenAPI document lists only 200 responses on 45 of 48 operations", "The docs price table and the live provider list disagree. Inferentia2 x1 is $0.75 in the docs and $1.95 in the API, and AWS H200 is listed in the docs and marked deprecated in the API", "A start from zero replicas takes minutes by the docs' own account, and the proxy answers 503 until a replica is ready", "No bug bounty was found. The security programme rests on a valid security.txt and a SOC2 Type 2 statement" ], "agentNotes": [ "Call `GET https://api.endpoints.huggingface.cloud/v2/provider` first and pick an instance whose `status` is `available`. The docs table lists types the API marks deprecated or not available", "Send `X-Scale-Up-Timeout: 600` on requests to an endpoint that scales to zero, or handle 503 while the first replica starts", "Set `scaleToZeroTimeout` yourself. The docs give a default of 1 hour and the OpenAPI document says 15 minutes", "Pause or delete an endpoint when the job is done. Billing covers every minute a replica is initialising or running", "Give the agent a fine-grained token or the `read-endpoints` scope unless it must deploy. Endpoints are private by default and take the same Hugging Face token as a bearer" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 66.7 } ], "editorialScores": { "ergonomics": 62, "maintenance": 80, "payments": 20, "reliability": 73, "schema": 73, "security": 83, "transparency": 68 }, "provenanceScore": 71, "checked": { "categories": { "ergonomics": "2026-11-02", "maintenance": "2026-11-02", "payments": "2026-11-02", "reliability": "2026-11-02", "schema": "2026-11-02", "security": "2026-11-02", "transparency": "2026-11-02" }, "last": "2026-11-02", "lastMoved": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install huggingface_hub", "http": "curl \"https://api.endpoints.huggingface.cloud/v2/endpoint/$NAMESPACE\" \\\n -H \"Authorization: Bearer $HF_TOKEN\"" }, "letme": { "capability": "https://letme.dev/compute.gpu", "tool": "https://letme.dev/hugging-face-inference-endpoints" }, "sameCompany": [ "hugging-face-inference-providers", "smolagents", "carbon-x", "smollm3-3b" ], "area": "models", "unitPrices": [ { "item": "NVIDIA T4 16 GB x1 (AWS, GCP)", "unit": "gpu-hour", "usd": 0.5, "note": "Billed per minute while initialising or running" }, { "item": "NVIDIA L4 24 GB x1 (AWS)", "unit": "gpu-hour", "usd": 0.8, "note": "$0.70 on GCP us-east4" }, { "item": "NVIDIA A10G 24 GB x1 (AWS)", "unit": "gpu-hour", "usd": 1, "note": "us-east-1 and eu-west-1" }, { "item": "NVIDIA L40S 48 GB x1 (AWS)", "unit": "gpu-hour", "usd": 1.8, "note": "us-east-1" }, { "item": "NVIDIA A100 80 GB x1 (AWS)", "unit": "gpu-hour", "usd": 2.5, "note": "$3.60 on GCP us-east4" }, { "item": "NVIDIA RTX PRO 6000 Blackwell 96 GB x1 (AWS)", "unit": "gpu-hour", "usd": 2.75, "note": "us-east-2, in the live provider list and absent from the docs table" }, { "item": "NVIDIA H200 141 GB x1 (GCP)", "unit": "gpu-hour", "usd": 5, "note": "us-south1. The AWS H200 in us-west-2 is marked deprecated in the API" }, { "item": "NVIDIA H100 80 GB x1 (GCP)", "unit": "gpu-hour", "usd": 10, "note": "us-east4. The AWS H100 at $4.50 is deprecated from December 2025" }, { "item": "Intel Sapphire Rapids x1, 1 vCPU and 2 GB (AWS)", "unit": "vcpu-hour", "usd": 0.033, "note": "$0.050 on GCP and $0.060 on Azure Intel Xeon" } ], "provenance": { "legalEntity": "Hugging Face, Inc.", "domain": "huggingface.co", "domainRegistered": "", "endpointOnVendorDomain": false, "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": "2026-10-08", "notes": [ "The Terms of Service (effective 15 September 2022) name Hugging Face, Inc., a Delaware corporation, list Inference Endpoints among the services they cover and are governed by New York law. They link Supplemental Terms (effective 28 April 2025) as a PDF, of which our reader extracted only the first page.", "The privacy policy (effective 28 March 2023) names Hugging Face, Inc. and its EU establishment Hugging Face, SAS, 9 rue des Colonnes, 75002 Paris, and lists 11 subprocessors with countries. The Inference Endpoints security page points to it.", "The management API answers at api.endpoints.huggingface.cloud and deployed endpoints at subdomains of endpoints.huggingface.cloud, a second domain of the vendor's. The catalogue API and the MCP server are on endpoints.huggingface.co.", "huggingface.co/.well-known/security.txt gives security@huggingface.co and expires on 1 July 2030. endpoints.huggingface.co/.well-known/security.txt returns 404.", "status.huggingface.co is a Better Stack page with separate components for the Inference Endpoints UI and API.", "The changelog at huggingface.co/changelog covers the whole Hub. Inference Endpoints has no changelog of its own. Dated changes are in the docs repository's commit history.", "rdap.org returned 404 for huggingface.co, so the registration date is unrecorded." ], "score": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-endpoints.json", "live": { "slug": "hugging-face-inference-endpoints", "probe": { "target": "https://api.endpoints.huggingface.cloud", "method": "get", "lastAt": "2026-11-07T19:22:12.471275497Z", "lastOk": true, "lastStatus": 200, "lastMs": 268, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 265, "p95ms24h": 377, "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": 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": 251, "ok": 251 }, { "date": "2026-10-29", "probes": 248, "ok": 248 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "vendorStatus": { "page": "https://status.huggingface.co", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-07T04:47:55.534335358Z" }, "versions": [ { "registry": "pypi", "name": "huggingface_hub", "version": "2.4.0", "released": "2026-10-29", "seenAt": "2026-11-06T23:33:23.580931242Z" } ], "githubStars": 27, "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-endpoints/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:42.888635563Z" }, "domain": { "domain": "huggingface.co", "checkedAt": "2026-11-01T14:09:00.477589213Z" }, "pages": [ { "url": "https://huggingface.co/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-11-07T14:11:29.170806608Z", "changedAt": "2026-11-07T14:11:29.170806608Z", "fingerprint": "4d3a63d4f46e" }, { "url": "https://huggingface.co/docs/inference-endpoints/support/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-07T14:11:35.529061385Z", "changedAt": "2026-11-07T14:11:35.529061385Z", "fingerprint": "0f0a46ee769e" }, { "url": "https://huggingface.co/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:12:45.345654967Z", "changedAt": "2026-11-07T14:12:45.345654967Z", "fingerprint": "791511d09ba8" }, { "url": "https://huggingface.co/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:12:49.455239893Z", "changedAt": "2026-11-07T14:12:49.455239893Z", "fingerprint": "40e6dc5ca2a4" } ], "github": { "repo": "huggingface/hf-endpoints-documentation", "checkedAt": "2026-11-02T08:55:12.143502376Z", "stars": 27, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-29T12:50:44Z", "openIssues": 0, "openPRs": 3, "issuesOpened90d": 0, "issuesClosed90d": 0, "prsMerged90d": 11, "ci": { "branch": "main", "runs": 30, "success": 30, "failure": 0, "other": 0, "latest": "success", "latestAt": "2026-10-23T09:21:14Z" }, "replies": { "sampled": 8, "unanswered": 0, "medianDays": 49.5 } }, "updatedAt": "2026-11-07T19:22:12.471275497Z" } }, "edges": [ { "by": 53, "category": "security", "leads": "hugging-face-inference-endpoints", "name": "Security \u0026 auth", "reasons": { "a": "Account keys are all-powerful within the account, with no scopes, expiry or rotation documented, but each rental gets its own proxy token (15). The per-rental token limits inference access to one rental; no read-only key or confirmation for destructive calls (6). Peer rentals send prompts and outputs through individuals' machines, protected only by the terms, with no prompt-injection or untrusted-host guidance (2). Per-rental usage stats of requests, tokens and cost (5). No security.txt, disclosure policy, bug bounty or certification; a DPO address for breaches. The CLI and host packages are obfuscated, so they can't be audited (2). 15+6+2+5+2 = 30.", "b": "OAuth through huggingface.co with `read-endpoints` and `write-endpoints` scopes, PKCE and dynamic client registration for the MCP server, and revocable fine-grained access tokens for the API. No documented option to send a token in a URL (30). Read and write scopes are separate, endpoints are private by default, organisations on Team and Enterprise plans can require approval of fine-grained tokens, and the MCP delete tool needs `confirm: true`. Other write calls have no confirmation step (17). The service returns the output and logs of the owner's own model, not third-party content (10). An audit log route and MCP tool on Pro, Team or Enterprise plans, plus logs and metrics for every endpoint (12). security.txt valid to 2030 with security@huggingface.co, SOC2 Type 2 stated for the Hub and Inference Endpoints, malware and pickle scanning of repositories. No bug bounty was found (14)." }, "scores": { "a": 30, "b": 83 } }, { "by": 40, "category": "reliability", "leads": "hugging-face-inference-endpoints", "name": "Reliability", "reasons": { "a": "Read with the hosted lines. No status page found; listing health is shown per listing only (0). No incident history to read (5). Rate limits published, 60 requests a minute per rental and 10 sign-ups an hour per IP (15). 429 is documented, but no Retry-After, backoff or idempotency guidance (3). The terms give no availability guarantee and put host outages on hosts (0). No beta label on the API (10). 0+5+15+3+0+10 = 33.", "b": "Better Stack status page at status.huggingface.co with separate Inference Endpoints UI and API components and 90 days of history (20). On 2 November 2026 both components show 100 per cent uptime, with one scheduled maintenance window for H200 on 7 October 2026. The page posts incidents for other services in the window (GPU Jobs and Spaces failing to start on 24 and 25 September), and none touches Inference Endpoints since the UI outage of 16 July left the 90 days, so a clean record (30). No rate limits were found for the management API. The Hub publishes limits per 5-minute window (1,000 API requests for a free user), without saying they cover api.endpoints.huggingface.cloud (5). The docs explain the 503 returned while a replica starts and the `X-Scale-Up-Timeout` header that holds the request, and advise 2 replicas for availability. No backoff guidance or idempotency keys for writes (8). No SLA found in the docs or on the pricing page (0). The service is generally available (10)." }, "scores": { "a": 33, "b": 73 } }, { "by": 21, "category": "schema", "leads": "hugging-face-inference-endpoints", "name": "Schema \u0026 documentation", "reasons": { "a": "API and MCP reading. No OpenAPI or other machine-readable contract was found, and the MCP tool schemas couldn't be read because the published package is obfuscated; the repository's history says 2.4.7 added output schemas to all 32 tools, which is the vendor's claim (8). llms.txt as a complete API reference (10). llms.txt says when to use peer or cloud rentals, the proxy or the relay, and which workload each listing type serves (12). Parameters are listed by name without types, enums or required markers, apart from instance and workload types (6). curl examples for each flow, an error code list and a sample deploy response (11). npm versions, but no public changelog (5). 8+10+12+6+11+5 = 52.", "b": "Public OpenAPI 3.1 documents for the management API (42 paths, 48 operations on 2 November 2026) and the catalogue API (3 operations, per the 8 October check) (25). llms.txt with a Markdown twin of every docs page (10). Every operation has a summary tagged READ, WRITE, PUBLIC or PRO, but only 7 of 48 have a description. The MCP tool table states each tool's purpose and says to call `get_recommended_config` before `create_endpoint` (11). 131 typed schemas with required fields and enums for state, type and accelerator. List filters are comma-separated strings (12). Parameters carry examples. The management document lists only 200 responses on 45 of 48 operations, plus 501 on three log routes, while the catalogue document lists 400, 401, 404, 409 and 500 (7). Paths are versioned `/v2` and `/v3` and the document is version 2.0.0. Inference Endpoints has no changelog of its own. The Hub changelog and the docs repository history are the dated record (8)." }, "scores": { "a": 52, "b": 73 } }, { "by": 20, "category": "transparency", "leads": "hugging-face-inference-endpoints", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed, under Tyga.Cloud licences that are clear on use, but the docs call gpusmarket-host MIT open source while the package says otherwise (10). The privacy policy gives retention periods and names Stripe and MongoDB, while renter data on peer hosts falls outside its controls, which the terms disclaim (15). The terms promise 30 days' notice of material changes, with no deprecation policy or dated notices found (5). Processors partly named, with transfers under SCCs, and no host locations shown in the docs read (6). 10+15+5+6 = 36.", "b": "Closed service under clear terms that name Inference Endpoints, with an Apache-2.0 client (20). The security page says payloads and tokens aren't stored and logs are kept 30 days. The privacy policy dates from 28 March 2023 and gives retention as long as necessary, and a data processing agreement requires an Enterprise subscription (20). Instance deprecations are dated to the month in the pricing table and flagged in the provider API, and the TGI maintenance notice is dated. No deprecation policy for the API was found (10). The privacy policy lists 11 subprocessors with countries and the docs and API list deployment regions (18)." }, "scores": { "a": 50, "b": 70 } }, { "by": 12, "category": "ergonomics", "leads": "hugging-face-inference-endpoints", "name": "Agent ergonomics", "reasons": { "a": "API and MCP reading. The README counts 32 MCP tools, and the decoded package includes 16 operator admin tools besides the renter tools, with no toolsets (5). Marketplace search takes `limit`, `offset`, `sort` and filters for GPU, price, VRAM, model, instance and workload type (20). Status codes with a one-line meaning each, no error bodies documented (10). No idempotency key on rent or deploy; stop and destroy return a cost summary (5). Only a Node CLI, though every proxy speaks the Ollama and OpenAI formats so existing SDKs work (10). 5+20+10+5+10 = 50.", "b": "List endpoints takes `limit` (default 20) and `cursor`, logs take `limit`, `tail` and `line_max_length`, and the MCP server has 19 tools (18). Cursor pagination, filters by state, type, task and tags, sorting, and a time window on logs (18). Errors are documented for the catalogue API and in prose for pause, resume and scale to zero. The management document has no error schema (8). No idempotency keys. Endpoints are addressed by name, updates are a PUT, and the MCP delete tool previews before a confirmed second call. MCP annotations weren't read (8). One-call deploy from the catalogue with tuned defaults, a Python client and the `hf endpoints` CLI. A second-language SDK for endpoint management wasn't found (10)." }, "scores": { "a": 50, "b": 62 } }, { "by": 11, "category": "maintenance", "leads": "hugging-face-inference-endpoints", "name": "Maintenance \u0026 community", "reasons": { "a": "gpusmarket 2.5.1 on 16 October 2026, 21 days before this check (30). Releases 2.4.18 to 2.5.1 between 17 August and 16 October, and gpusmarket-host 1.5.3 and 1.6.0 (20). Support by chat widget; the repository, which holds only a README, server.json and a licence, has no open issues and one star (5). Published in the official MCP registry under the verified io.github.jyswee namespace, though the newest registry version, 2.4.16, trails npm (12). No dependencies, but an obfuscated build and no public CI (2). 30+20+5+12+2 = 69.", "b": "`huggingface_hub` v2.4.0 was tagged on 29 October 2026, and the docs repository changed on 23 October (30). Tags v2.0.0 (24 September), v2.1.0, v2.1.1, v2.2.0, v2.3.0 (23 October) and v2.4.0 in the 90 days, and docs commits on 14 and 15 October adding the `inferenceLoad` scaling strategy and a daily autoscaling schedule (20). GitHub's pages refused our requests, so issue response times went unread. A public changelog for the Hub, a forum and a quota contact form exist (8 of 15). Current official Python client and CLI (15). The client supports Python 3.10 and later and ships weekly. Its CI wasn't read (7)." }, "scores": { "a": 69, "b": 80 } } ], "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Tyga.Cloud Ltd", "b": "Hugging Face, Inc.", "name": "Vendor" }, { "a": "https://gpusmarket.com/api/v1", "b": "https://api.endpoints.huggingface.cloud", "name": "Hosted endpoint" }, { "a": "stdio, SSE (legacy)", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "OAuth or key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary. The gpusmarket CLI and gpusmarket-host packages ship under Tyga.Cloud Ltd licences that allow use only with the platform, as obfuscated JavaScript", "b": "Proprietary service under the Hugging Face Terms of Service. The `huggingface_hub` Python client and CLI are Apache-2.0", "name": "Licence" }, { "a": "32", "b": "19", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "io.github.jyswee/gpusmarket", "b": "not listed", "name": "MCP registry" }, { "a": "2026-10-16", "b": "2026-10-29", "name": "Last release" }, { "a": "2026-06-01", "b": "2022-09-15", "name": "Terms last updated" }, { "a": "2026-03-01", "b": "2023-03-28", "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": "not found in the text", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "yes", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "1 stars, 79 npm/wk", "b": "60M PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Hugging Face Inference Endpoints scores 66.7 (B) on agent readiness against GPUsMarket's 39.6 (E), and leads in 6 of 7 scored categories. GPUsMarket leads on payments \u0026 pricing.", "question": "Which is better for AI agents, GPUsMarket or Hugging Face Inference Endpoints?" }, { "answer": "GPUsMarket needs an API key. Hugging Face Inference Endpoints takes an API key or an OAuth sign-in.", "question": "Do GPUsMarket and Hugging Face Inference Endpoints need an API key?" }, { "answer": "Yes. GPUsMarket has a hosted endpoint at https://gpusmarket.com/api/v1 and Hugging Face Inference Endpoints at https://api.endpoints.huggingface.cloud.", "question": "Can an agent call GPUsMarket and Hugging Face Inference Endpoints without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Payments \u0026 pricing, 30 against 20" ], "also": [ "Runs on your own machine" ], "goodFor": "Cheap, short GPU rentals for open models behind an Ollama or OpenAI endpoint, where data sensitivity is low.", "slug": "gpusmarket", "watchFor": "Peer rentals send prompts and outputs through individuals' hardware, with only the terms as protection" }, { "aheadOn": [ "Reliability, 73 against 33", "Schema \u0026 documentation, 73 against 52", "Agent ergonomics, 62 against 50", "Security \u0026 auth, 83 against 30", "Maintenance \u0026 community, 80 against 69", "Transparency \u0026 trust, 70 against 50" ], "also": [ "No incidents deducted, where GPUsMarket loses 3 points for them" ], "goodFor": "Teams whose models already live on the Hugging Face Hub and who want a dedicated endpoint on a named cloud and region with standard open-source engines.", "slug": "hugging-face-inference-endpoints", "watchFor": "No free tier. The docs require a payment method and credits, and replicas are billed while initialising as well as running" } ], "history": { "gpusmarket": null, "hugging-face-inference-endpoints": [ { "causes": [ "time" ], "date": "2026-11-02", "delta": 2, "from": 64.7, "fromGrade": "B", "to": 66.7, "toGrade": "B" }, { "causes": [ "evidence" ], "date": "2026-10-27", "delta": 0.2, "from": 64.5, "fromGrade": "B", "to": 64.7, "toGrade": "B" } ] }, "job": { "capability": "compute.gpu", "name": "GPU compute" }, "others": [ { "json": "https://www.anchorterminal.com/compare/baseten-vs-gpusmarket.json", "title": "Baseten vs GPUsMarket", "url": "https://www.anchorterminal.com/compare/baseten-vs-gpusmarket" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints.json", "title": "Baseten vs Hugging Face Inference Endpoints", "url": "https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints" }, { "json": "https://www.anchorterminal.com/compare/beam-vs-gpusmarket.json", "title": "Beam vs GPUsMarket", "url": "https://www.anchorterminal.com/compare/beam-vs-gpusmarket" }, { "json": "https://www.anchorterminal.com/compare/beam-vs-hugging-face-inference-endpoints.json", "title": "Beam vs Hugging Face Inference Endpoints", "url": "https://www.anchorterminal.com/compare/beam-vs-hugging-face-inference-endpoints" }, { "json": "https://www.anchorterminal.com/compare/cerebrium-vs-gpusmarket.json", "title": "Cerebrium vs GPUsMarket", "url": "https://www.anchorterminal.com/compare/cerebrium-vs-gpusmarket" }, { "json": "https://www.anchorterminal.com/compare/cerebrium-vs-hugging-face-inference-endpoints.json", "title": "Cerebrium vs Hugging Face Inference Endpoints", "url": "https://www.anchorterminal.com/compare/cerebrium-vs-hugging-face-inference-endpoints" }, { "json": "https://www.anchorterminal.com/compare/cloud-run-gpus-vs-gpusmarket.json", "title": "Cloud Run GPUs vs GPUsMarket", "url": 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"https://www.anchorterminal.com/compare/gpusmarket-vs-massed-compute" }, { "json": "https://www.anchorterminal.com/compare/gpusmarket-vs-modal.json", "title": "GPUsMarket vs Modal", "url": "https://www.anchorterminal.com/compare/gpusmarket-vs-modal" }, { "json": "https://www.anchorterminal.com/compare/gpusmarket-vs-nebius-ai-cloud.json", "title": "GPUsMarket vs Nebius AI Cloud", "url": "https://www.anchorterminal.com/compare/gpusmarket-vs-nebius-ai-cloud" }, { "json": "https://www.anchorterminal.com/compare/gpusmarket-vs-northflank.json", "title": "GPUsMarket vs Northflank", "url": "https://www.anchorterminal.com/compare/gpusmarket-vs-northflank" }, { "json": "https://www.anchorterminal.com/compare/gpusmarket-vs-replicate-deploy.json", "title": "GPUsMarket vs Replicate Deployments", "url": "https://www.anchorterminal.com/compare/gpusmarket-vs-replicate-deploy" }, { "json": "https://www.anchorterminal.com/compare/gpusmarket-vs-runpod.json", "title": "GPUsMarket vs Runpod", "url": "https://www.anchorterminal.com/compare/gpusmarket-vs-runpod" }, { "json": "https://www.anchorterminal.com/compare/gpusmarket-vs-thunder-compute.json", "title": "GPUsMarket vs Thunder Compute", "url": "https://www.anchorterminal.com/compare/gpusmarket-vs-thunder-compute" }, { "json": "https://www.anchorterminal.com/compare/gpusmarket-vs-together-gpu-clusters.json", "title": "GPUsMarket vs Together GPU Clusters", "url": "https://www.anchorterminal.com/compare/gpusmarket-vs-together-gpu-clusters" }, { "json": "https://www.anchorterminal.com/compare/gpusmarket-vs-vast-ai.json", "title": "GPUsMarket vs Vast.ai", "url": "https://www.anchorterminal.com/compare/gpusmarket-vs-vast-ai" }, { "json": "https://www.anchorterminal.com/compare/gpusmarket-vs-verda.json", "title": "GPUsMarket vs Verda", "url": "https://www.anchorterminal.com/compare/gpusmarket-vs-verda" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic.json", "title": "Hugging Face Inference Endpoints vs Hyperbolic", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-koyeb.json", "title": "Hugging Face Inference Endpoints vs Koyeb", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-koyeb" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-lambda.json", "title": "Hugging Face Inference Endpoints vs Lambda Cloud", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-lambda" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-massed-compute.json", "title": "Hugging Face Inference Endpoints vs Massed Compute", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-massed-compute" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-modal.json", "title": "Hugging Face Inference Endpoints vs Modal", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-modal" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-nebius-ai-cloud.json", "title": "Hugging Face Inference Endpoints vs Nebius AI Cloud", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-nebius-ai-cloud" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-northflank.json", "title": "Hugging Face Inference Endpoints vs Northflank", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-northflank" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-replicate-deploy.json", "title": "Hugging Face Inference Endpoints vs Replicate Deployments", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-replicate-deploy" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-runpod.json", "title": "Hugging Face Inference Endpoints vs Runpod", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-runpod" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-thunder-compute.json", "title": "Hugging Face Inference Endpoints vs Thunder Compute", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-thunder-compute" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-together-gpu-clusters.json", "title": "Hugging Face Inference Endpoints vs Together GPU Clusters", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-together-gpu-clusters" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-vast-ai.json", "title": "Hugging Face Inference Endpoints vs Vast.ai", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-vast-ai" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-verda.json", "title": "Hugging Face Inference Endpoints vs Verda", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-verda" } ], "panel": null, "scores": [ { "by": 40, "edge": "hugging-face-inference-endpoints", "gpusmarket": 33, "hugging-face-inference-endpoints": 73, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 21, "edge": "hugging-face-inference-endpoints", "gpusmarket": 52, "hugging-face-inference-endpoints": 73, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 12, "edge": "hugging-face-inference-endpoints", "gpusmarket": 50, "hugging-face-inference-endpoints": 62, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 53, "edge": "hugging-face-inference-endpoints", "gpusmarket": 30, "hugging-face-inference-endpoints": 83, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 10, "edge": "gpusmarket", "gpusmarket": 30, "hugging-face-inference-endpoints": 20, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 11, "edge": "hugging-face-inference-endpoints", "gpusmarket": 69, "hugging-face-inference-endpoints": 80, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 20, "edge": "hugging-face-inference-endpoints", "gpusmarket": 50, "hugging-face-inference-endpoints": 70, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Hugging Face Inference Endpoints scores 66.7 (B) on agent readiness against GPUsMarket's 39.6 (E), and leads in 6 of 7 scored categories. GPUsMarket leads on payments \u0026 pricing. Both do gpu compute.", "tasks": null, "tutorials": [], "verdicts": { "gpusmarket": "An agent can sign up with one API call, search priced GPUs without a key, rent a peer or cloud GPU as an Ollama or OpenAI-compatible endpoint and stop it, billed per second. Prompts on peer rentals pass through individuals' machines, the CLI is obfuscated, and there is no status page, security policy or free tier.", "hugging-face-inference-endpoints": "OAuth scopes separate reading endpoints from writing them, both OpenAPI documents are public, and the unauthenticated `/v2/provider` route lists every instance with its hourly price. An account needs a payment method and credits before the first deployment, no rate limits or SLA were found for the management API, and the docs price table disagrees with the live list in places." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gpusmarket-vs-hugging-face-inference-endpoints", "json": "https://www.anchorterminal.com/compare/gpusmarket-vs-hugging-face-inference-endpoints.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gpusmarket-vs-hugging-face-inference-endpoints.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gpusmarket-vs-hugging-face-inference-endpoints.min.md" }, "markdown": "Hugging Face Inference Endpoints scores 66.7 (B) on agent readiness against GPUsMarket's 39.6 (E), and leads in 6 of 7 scored categories. GPUsMarket leads on payments \u0026 pricing. Both do gpu compute.\n\n- GPUsMarket: grade E, 39.6/100, rank #1337 of 1510. Markdown https://www.anchorterminal.com/tools/gpusmarket.md · JSON https://www.anchorterminal.com/api/v1/tools/gpusmarket.json\n- Hugging Face Inference Endpoints: grade B, 66.7/100, rank #358 of 1510. Markdown https://www.anchorterminal.com/tools/hugging-face-inference-endpoints.md · JSON https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-endpoints.json\n- Best GPU and serverless compute for AI workloads: https://www.anchorterminal.com/best/gpu-compute/index.md\n- All 231 gpu compute comparisons: https://www.anchorterminal.com/compare/gpu-compute/index.md\n\n## Which one, for what\n\n### GPUsMarket (E)\n\nGood for: Cheap, short GPU rentals for open models behind an Ollama or OpenAI endpoint, where data sensitivity is low.\n\nAhead on:\n- Payments \u0026 pricing, 30 against 20\n\nAlso in its favour:\n- Runs on your own machine\n\nWatch for: Peer rentals send prompts and outputs through individuals' hardware, with only the terms as protection\n\n### Hugging Face Inference Endpoints (B)\n\nGood for: Teams whose models already live on the Hugging Face Hub and who want a dedicated endpoint on a named cloud and region with standard open-source engines.\n\nAhead on:\n- Reliability, 73 against 33\n- Schema \u0026 documentation, 73 against 52\n- Agent ergonomics, 62 against 50\n- Security \u0026 auth, 83 against 30\n- Maintenance \u0026 community, 80 against 69\n- Transparency \u0026 trust, 70 against 50\n\nAlso in its favour:\n- No incidents deducted, where GPUsMarket loses 3 points for them\n\nWatch for: No free tier. The docs require a payment method and credits, and replicas are billed while initialising as well as running\n\n\n## Why the edge\n\n### Security \u0026 auth: Hugging Face Inference Endpoints +53\n\n- GPUsMarket (30): Account keys are all-powerful within the account, with no scopes, expiry or rotation documented, but each rental gets its own proxy token (15). The per-rental token limits inference access to one rental; no read-only key or confirmation for destructive calls (6). Peer rentals send prompts and outputs through individuals' machines, protected only by the terms, with no prompt-injection or untrusted-host guidance (2). Per-rental usage stats of requests, tokens and cost (5). No security.txt, disclosure policy, bug bounty or certification; a DPO address for breaches. The CLI and host packages are obfuscated, so they can't be audited (2). 15+6+2+5+2 = 30.\n- Hugging Face Inference Endpoints (83): OAuth through huggingface.co with `read-endpoints` and `write-endpoints` scopes, PKCE and dynamic client registration for the MCP server, and revocable fine-grained access tokens for the API. No documented option to send a token in a URL (30). Read and write scopes are separate, endpoints are private by default, organisations on Team and Enterprise plans can require approval of fine-grained tokens, and the MCP delete tool needs `confirm: true`. Other write calls have no confirmation step (17). The service returns the output and logs of the owner's own model, not third-party content (10). An audit log route and MCP tool on Pro, Team or Enterprise plans, plus logs and metrics for every endpoint (12). security.txt valid to 2030 with security@huggingface.co, SOC2 Type 2 stated for the Hub and Inference Endpoints, malware and pickle scanning of repositories. No bug bounty was found (14).\n\n### Reliability: Hugging Face Inference Endpoints +40\n\n- GPUsMarket (33): Read with the hosted lines. No status page found; listing health is shown per listing only (0). No incident history to read (5). Rate limits published, 60 requests a minute per rental and 10 sign-ups an hour per IP (15). 429 is documented, but no Retry-After, backoff or idempotency guidance (3). The terms give no availability guarantee and put host outages on hosts (0). No beta label on the API (10). 0+5+15+3+0+10 = 33.\n- Hugging Face Inference Endpoints (73): Better Stack status page at status.huggingface.co with separate Inference Endpoints UI and API components and 90 days of history (20). On 2 November 2026 both components show 100 per cent uptime, with one scheduled maintenance window for H200 on 7 October 2026. The page posts incidents for other services in the window (GPU Jobs and Spaces failing to start on 24 and 25 September), and none touches Inference Endpoints since the UI outage of 16 July left the 90 days, so a clean record (30). No rate limits were found for the management API. The Hub publishes limits per 5-minute window (1,000 API requests for a free user), without saying they cover api.endpoints.huggingface.cloud (5). The docs explain the 503 returned while a replica starts and the `X-Scale-Up-Timeout` header that holds the request, and advise 2 replicas for availability. No backoff guidance or idempotency keys for writes (8). No SLA found in the docs or on the pricing page (0). The service is generally available (10).\n\n### Schema \u0026 documentation: Hugging Face Inference Endpoints +21\n\n- GPUsMarket (52): API and MCP reading. No OpenAPI or other machine-readable contract was found, and the MCP tool schemas couldn't be read because the published package is obfuscated; the repository's history says 2.4.7 added output schemas to all 32 tools, which is the vendor's claim (8). llms.txt as a complete API reference (10). llms.txt says when to use peer or cloud rentals, the proxy or the relay, and which workload each listing type serves (12). Parameters are listed by name without types, enums or required markers, apart from instance and workload types (6). curl examples for each flow, an error code list and a sample deploy response (11). npm versions, but no public changelog (5). 8+10+12+6+11+5 = 52.\n- Hugging Face Inference Endpoints (73): Public OpenAPI 3.1 documents for the management API (42 paths, 48 operations on 2 November 2026) and the catalogue API (3 operations, per the 8 October check) (25). llms.txt with a Markdown twin of every docs page (10). Every operation has a summary tagged READ, WRITE, PUBLIC or PRO, but only 7 of 48 have a description. The MCP tool table states each tool's purpose and says to call `get_recommended_config` before `create_endpoint` (11). 131 typed schemas with required fields and enums for state, type and accelerator. List filters are comma-separated strings (12). Parameters carry examples. The management document lists only 200 responses on 45 of 48 operations, plus 501 on three log routes, while the catalogue document lists 400, 401, 404, 409 and 500 (7). Paths are versioned `/v2` and `/v3` and the document is version 2.0.0. Inference Endpoints has no changelog of its own. The Hub changelog and the docs repository history are the dated record (8).\n\n### Transparency \u0026 trust: Hugging Face Inference Endpoints +20\n\n- GPUsMarket (50): Closed, under Tyga.Cloud licences that are clear on use, but the docs call gpusmarket-host MIT open source while the package says otherwise (10). The privacy policy gives retention periods and names Stripe and MongoDB, while renter data on peer hosts falls outside its controls, which the terms disclaim (15). The terms promise 30 days' notice of material changes, with no deprecation policy or dated notices found (5). Processors partly named, with transfers under SCCs, and no host locations shown in the docs read (6). 10+15+5+6 = 36.\n- Hugging Face Inference Endpoints (70): Closed service under clear terms that name Inference Endpoints, with an Apache-2.0 client (20). The security page says payloads and tokens aren't stored and logs are kept 30 days. The privacy policy dates from 28 March 2023 and gives retention as long as necessary, and a data processing agreement requires an Enterprise subscription (20). Instance deprecations are dated to the month in the pricing table and flagged in the provider API, and the TGI maintenance notice is dated. No deprecation policy for the API was found (10). The privacy policy lists 11 subprocessors with countries and the docs and API list deployment regions (18).\n\n### Agent ergonomics: Hugging Face Inference Endpoints +12\n\n- GPUsMarket (50): API and MCP reading. The README counts 32 MCP tools, and the decoded package includes 16 operator admin tools besides the renter tools, with no toolsets (5). Marketplace search takes `limit`, `offset`, `sort` and filters for GPU, price, VRAM, model, instance and workload type (20). Status codes with a one-line meaning each, no error bodies documented (10). No idempotency key on rent or deploy; stop and destroy return a cost summary (5). Only a Node CLI, though every proxy speaks the Ollama and OpenAI formats so existing SDKs work (10). 5+20+10+5+10 = 50.\n- Hugging Face Inference Endpoints (62): List endpoints takes `limit` (default 20) and `cursor`, logs take `limit`, `tail` and `line_max_length`, and the MCP server has 19 tools (18). Cursor pagination, filters by state, type, task and tags, sorting, and a time window on logs (18). Errors are documented for the catalogue API and in prose for pause, resume and scale to zero. The management document has no error schema (8). No idempotency keys. Endpoints are addressed by name, updates are a PUT, and the MCP delete tool previews before a confirmed second call. MCP annotations weren't read (8). One-call deploy from the catalogue with tuned defaults, a Python client and the `hf endpoints` CLI. A second-language SDK for endpoint management wasn't found (10).\n\n### Maintenance \u0026 community: Hugging Face Inference Endpoints +11\n\n- GPUsMarket (69): gpusmarket 2.5.1 on 16 October 2026, 21 days before this check (30). Releases 2.4.18 to 2.5.1 between 17 August and 16 October, and gpusmarket-host 1.5.3 and 1.6.0 (20). Support by chat widget; the repository, which holds only a README, server.json and a licence, has no open issues and one star (5). Published in the official MCP registry under the verified io.github.jyswee namespace, though the newest registry version, 2.4.16, trails npm (12). No dependencies, but an obfuscated build and no public CI (2). 30+20+5+12+2 = 69.\n- Hugging Face Inference Endpoints (80): `huggingface_hub` v2.4.0 was tagged on 29 October 2026, and the docs repository changed on 23 October (30). Tags v2.0.0 (24 September), v2.1.0, v2.1.1, v2.2.0, v2.3.0 (23 October) and v2.4.0 in the 90 days, and docs commits on 14 and 15 October adding the `inferenceLoad` scaling strategy and a daily autoscaling schedule (20). GitHub's pages refused our requests, so issue response times went unread. A public changelog for the Hub, a forum and a quota contact form exist (8 of 15). Current official Python client and CLI (15). The client supports Python 3.10 and later and ships weekly. Its CI wasn't read (7).\n\n## Score by category\n\n| Category | Weight | GPUsMarket | Hugging Face Inference Endpoints | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 33 | 73 | Hugging Face Inference Endpoints +40 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 52 | 73 | Hugging Face Inference Endpoints +21 |\n| Agent ergonomics | 13% (16.2 this run) | 50 | 62 | Hugging Face Inference Endpoints +12 |\n| Security \u0026 auth | 14% (17.5 this run) | 30 | 83 | Hugging Face Inference Endpoints +53 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 20 | GPUsMarket +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 69 | 80 | Hugging Face Inference Endpoints +11 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 50 | 70 | Hugging Face Inference Endpoints +20 |\n| Negative events | ≤15 | -3 | 0 | |\n| **Total** | | **39.6 · E** | **66.7 · B** | |\n\n## Facts side by side\n\n| Fact | GPUsMarket | Hugging Face Inference Endpoints |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Tyga.Cloud Ltd | Hugging Face, Inc. |\n| Hosted endpoint | `https://gpusmarket.com/api/v1` | `https://api.endpoints.huggingface.cloud` |\n| Transports | stdio, SSE (legacy) | HTTP |\n| Auth | API key | OAuth or key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Proprietary. The gpusmarket CLI and gpusmarket-host packages ship under Tyga.Cloud Ltd licences that allow use only with the platform, as obfuscated JavaScript | Proprietary service under the Hugging Face Terms of Service. The `huggingface_hub` Python client and CLI are Apache-2.0 |\n| Tools exposed | 32 | 19 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| MCP registry | `io.github.jyswee/gpusmarket` | not listed |\n| Last release | 2026-10-16 | 2026-10-29 |\n| Terms last updated | 2026-06-01 | 2022-09-15 |\n| Privacy policy last updated | 2026-03-01 | 2023-03-28 |\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 | not found in the text |\n| Terms restrict benchmarking | not found in the text | not found in the text |\n| Terms or service can change without notice | not found in the text | yes |\n| Arbitration or class-action waiver | not found in the text | not found in the text |\n| Popularity | 1 stars, 79 npm/wk | 60M PyPI/wk |\n| Agent reviews | none | 3/5 (4) |\n\n## How the scores have moved\n\n### GPUsMarket\n\nNo score change since it was listed.\n\n### Hugging Face Inference Endpoints\n\n- +2.0 on 2 November 2026: time\n- +0.2 on 27 October 2026: new evidence\n\n## Verdicts\n\n**GPUsMarket.** An agent can sign up with one API call, search priced GPUs without a key, rent a peer or cloud GPU as an Ollama or OpenAI-compatible endpoint and stop it, billed per second. Prompts on peer rentals pass through individuals' machines, the CLI is obfuscated, and there is no status page, security policy or free tier.\n\n**Hugging Face Inference Endpoints.** OAuth scopes separate reading endpoints from writing them, both OpenAPI documents are public, and the unauthenticated `/v2/provider` route lists every instance with its hourly price. An account needs a payment method and credits before the first deployment, no rate limits or SLA were found for the management API, and the docs price table disagrees with the live list in places.\n\n## Before you call either\n\n### GPUsMarket\n\n1. Don't send confidential data to peer rentals, since requests pass through the host's machine\n2. Stop every rental with `POST /api/v1/marketplace/rentals/{id}/stop`, because billing runs per second until it stops\n3. Use the per-rental token for inference and keep the `gpu_` account key out of inference clients\n4. Treat 502 as host offline and rent another listing rather than retrying the same one\n5. Ask the person to add a card before renting, since each rental places a hold on it\n\n### Hugging Face Inference Endpoints\n\n1. Call `GET https://api.endpoints.huggingface.cloud/v2/provider` first and pick an instance whose `status` is `available`. The docs table lists types the API marks deprecated or not available\n2. Send `X-Scale-Up-Timeout: 600` on requests to an endpoint that scales to zero, or handle 503 while the first replica starts\n3. Set `scaleToZeroTimeout` yourself. The docs give a default of 1 hour and the OpenAPI document says 15 minutes\n4. Pause or delete an endpoint when the job is done. Billing covers every minute a replica is initialising or running\n5. Give the agent a fine-grained token or the `read-endpoints` scope unless it must deploy. Endpoints are private by default and take the same Hugging Face token as a bearer\n\n## Questions\n\n### Which is better for AI agents, GPUsMarket or Hugging Face Inference Endpoints?\n\nHugging Face Inference Endpoints scores 66.7 (B) on agent readiness against GPUsMarket's 39.6 (E), and leads in 6 of 7 scored categories. GPUsMarket leads on payments \u0026 pricing.\n\n### Do GPUsMarket and Hugging Face Inference Endpoints need an API key?\n\nGPUsMarket needs an API key. Hugging Face Inference Endpoints takes an API key or an OAuth sign-in.\n\n### Can an agent call GPUsMarket and Hugging Face Inference Endpoints without installing anything?\n\nYes. GPUsMarket has a hosted endpoint at https://gpusmarket.com/api/v1 and Hugging Face Inference Endpoints at https://api.endpoints.huggingface.cloud.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gpusmarket-vs-hugging-face-inference-endpoints.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpusmarket-vs-hugging-face-inference-endpoints.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gpusmarket\", \"b\": \"hugging-face-inference-endpoints\"}`. From a terminal: `anchor compare gpusmarket hugging-face-inference-endpoints`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gpusmarket.json and https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-endpoints.json\n\n## Other comparisons with GPUsMarket or Hugging Face Inference Endpoints\n\n- [Baseten vs GPUsMarket](https://www.anchorterminal.com/compare/baseten-vs-gpusmarket.md)\n- [Baseten vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints.md)\n- [Beam vs GPUsMarket](https://www.anchorterminal.com/compare/beam-vs-gpusmarket.md)\n- [Beam vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/beam-vs-hugging-face-inference-endpoints.md)\n- [Cerebrium vs GPUsMarket](https://www.anchorterminal.com/compare/cerebrium-vs-gpusmarket.md)\n- [Cerebrium vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/cerebrium-vs-hugging-face-inference-endpoints.md)\n- [Cloud Run GPUs vs GPUsMarket](https://www.anchorterminal.com/compare/cloud-run-gpus-vs-gpusmarket.md)\n- [Cloud Run GPUs vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/cloud-run-gpus-vs-hugging-face-inference-endpoints.md)\n- [CoreWeave vs GPUsMarket](https://www.anchorterminal.com/compare/coreweave-vs-gpusmarket.md)\n- [CoreWeave vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/coreweave-vs-hugging-face-inference-endpoints.md)\n- [Crusoe Cloud vs GPUsMarket](https://www.anchorterminal.com/compare/crusoe-cloud-vs-gpusmarket.md)\n- [Crusoe Cloud vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/crusoe-cloud-vs-hugging-face-inference-endpoints.md)\n- [GPUsMarket vs Hyperbolic](https://www.anchorterminal.com/compare/gpusmarket-vs-hyperbolic.md)\n- [GPUsMarket vs Koyeb](https://www.anchorterminal.com/compare/gpusmarket-vs-koyeb.md)\n- [GPUsMarket vs Lambda Cloud](https://www.anchorterminal.com/compare/gpusmarket-vs-lambda.md)\n- [GPUsMarket vs Massed Compute](https://www.anchorterminal.com/compare/gpusmarket-vs-massed-compute.md)\n- [GPUsMarket vs Modal](https://www.anchorterminal.com/compare/gpusmarket-vs-modal.md)\n- [GPUsMarket vs Nebius AI Cloud](https://www.anchorterminal.com/compare/gpusmarket-vs-nebius-ai-cloud.md)\n- [GPUsMarket vs Northflank](https://www.anchorterminal.com/compare/gpusmarket-vs-northflank.md)\n- [GPUsMarket vs Replicate Deployments](https://www.anchorterminal.com/compare/gpusmarket-vs-replicate-deploy.md)\n- [GPUsMarket vs Runpod](https://www.anchorterminal.com/compare/gpusmarket-vs-runpod.md)\n- [GPUsMarket vs Thunder Compute](https://www.anchorterminal.com/compare/gpusmarket-vs-thunder-compute.md)\n- [GPUsMarket vs Together GPU Clusters](https://www.anchorterminal.com/compare/gpusmarket-vs-together-gpu-clusters.md)\n- [GPUsMarket vs Vast.ai](https://www.anchorterminal.com/compare/gpusmarket-vs-vast-ai.md)\n- [GPUsMarket vs Verda](https://www.anchorterminal.com/compare/gpusmarket-vs-verda.md)\n- [Hugging Face Inference Endpoints vs Hyperbolic](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic.md)\n- [Hugging Face Inference Endpoints vs Koyeb](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-koyeb.md)\n- [Hugging Face Inference Endpoints vs Lambda Cloud](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-lambda.md)\n- [Hugging Face Inference Endpoints vs Massed Compute](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-massed-compute.md)\n- [Hugging Face Inference Endpoints vs Modal](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-modal.md)\n- [Hugging Face Inference Endpoints vs Nebius AI Cloud](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-nebius-ai-cloud.md)\n- [Hugging Face Inference Endpoints vs Northflank](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-northflank.md)\n- [Hugging Face Inference Endpoints vs Replicate Deployments](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-replicate-deploy.md)\n- [Hugging Face Inference Endpoints vs Runpod](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-runpod.md)\n- [Hugging Face Inference Endpoints vs Thunder Compute](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-thunder-compute.md)\n- [Hugging Face Inference Endpoints vs Together GPU Clusters](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-together-gpu-clusters.md)\n- [Hugging Face Inference Endpoints vs Vast.ai](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-vast-ai.md)\n- [Hugging Face Inference Endpoints vs Verda](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-verda.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": "GPUsMarket vs Hugging Face Inference Endpoints", "url": "" } ], "description": "Hugging Face Inference Endpoints scores 66.7 (B) to GPUsMarket's 39.6 (E) for gpu compute. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "GPUsMarket E 39.6", "Hugging Face Inference Endpoints B 66.7", "scores" ], "h1": "GPUsMarket vs Hugging Face Inference Endpoints", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/gpusmarket-vs-hugging-face-inference-endpoints", "published": "2026-10-01", "section": "tools", "title": "GPUsMarket vs Hugging Face Inference Endpoints for AI agents (2026)", "toc": null, "updated": "2026-11-07", "url": "https://www.anchorterminal.com/compare/gpusmarket-vs-hugging-face-inference-endpoints" }, "tokens": { "markdown": 5550, "slim": 2980 }, "version": 1 }