{ "data": { "a": { "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", "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": "2026-10-08", "graded": true, "anchor": { "graded": true, "score": 64.5, "grade": "B", "agentReady": false, "rank": 314, "ranked": true, "rankOf": 842, "categoryRank": 3, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 62, "maintenance": 80, "payments": 20, "reliability": 63, "schema": 73, "security": 83, "transparency": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "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 API component on status.huggingface.co shows 100 per cent uptime over the 90 days to 8 October 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 43 of 46 operations", "The docs price table and the live provider list disagree. Inferentia2 x1 is $0.75 in the docs and $1.95 in the API, and AWS H200 is listed in the docs and marked deprecated in the API", "A start from zero replicas takes minutes by the docs' own account, and the proxy answers 503 until a replica is ready", "The Hub outage of 16 July 2026 took the Inference Endpoints UI down for 1 hour 37 minutes" ], "agentNotes": [ "Call `GET https://api.endpoints.huggingface.cloud/v2/provider` first and pick an instance whose `status` is `available`. The docs table lists types the API marks deprecated or not available", "Send `X-Scale-Up-Timeout: 600` on requests to an endpoint that scales to zero, or handle 503 while the first replica starts", "Set `scaleToZeroTimeout` yourself. The docs give a default of 1 hour and the OpenAPI document says 15 minutes", "Pause or delete an endpoint when the job is done. Billing covers every minute a replica is initialising or running", "Give the agent a fine-grained token or the `read-endpoints` scope unless it must deploy. Endpoints are private by default and take the same Hugging Face token as a bearer" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.5 } ], "editorialScores": { "ergonomics": 62, "maintenance": 80, "payments": 20, "reliability": 63, "schema": 73, "security": 83, "transparency": 68 }, "provenanceScore": 67 }, "connect": { "install": "pip install huggingface_hub", "http": "curl \"https://api.endpoints.huggingface.cloud/v2/endpoint/$NAMESPACE\" \\\n -H \"Authorization: Bearer $HF_TOKEN\"" }, "letme": { "capability": "https://letme.dev/compute.gpu", "tool": "https://letme.dev/hugging-face-inference-endpoints" }, "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": 67 }, "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": "2026-10-09T11:46:30.653783864Z", "lastOk": true, "lastStatus": 200, "lastMs": 257, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 255, "p95ms24h": 300, "samples24h": 44, "samples30d": 44, "days": [ { "date": "2026-10-09", "probes": 44, "ok": 44 } ] }, "vendorStatus": { "page": "https://status.huggingface.co", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-09T07:58:03.654181492Z" }, "updatedAt": "2026-10-09T11:46:30.653783864Z" } }, "answer": "Hugging Face Inference Endpoints scores 64.5 (B) on agent readiness against Hyperbolic's 48 (D), and leads in 6 of 7 scored categories.", "b": { "slug": "hyperbolic", "name": "Hyperbolic", "vendor": "Hyperbolic Labs, Inc.", "vendorUrl": "https://www.hyperbolic.ai", "kind": "http-api", "category": "gpu-compute", "summary": "Hyperbolic is a GPU cloud from Hyperbolic Labs that rents H100, H200 and B200 virtual machines and bare-metal nodes by the hour, with reserved terms and private clusters. Agents use its REST API with an API key.", "url": "https://www.anchorterminal.com/tools/hyperbolic", "markdownUrl": "https://www.anchorterminal.com/tools/hyperbolic.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hyperbolic.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hyperbolic.json", "license": "Proprietary service under Hyperbolic's Terms of Service. The unmaintained hyperbolic-mcp repository on GitHub is MIT", "transports": [ "http" ], "remoteUrl": "https://api.hyperbolic.ai", "packages": [], "auth": "api-key", "authNotes": "Self-serve. A signed-in user creates an API key in the console's settings, and the secret is shown once. Requests send it as `Authorization: Bearer \u003capi-key\u003e`. An account can hold any number of keys and revoke any of them. Keys created inside an Organisation belong to it and are attributed to the member who made them. Members revoke their own keys and admins revoke any. No scopes, read-only keys or expiry were found in the reviewed documentation. `GET /v2/openapi.json`, `GET /v2/on-demand/rental-options` and `GET /v2/on-demand/reserve-options` need no key.", "pricing": "usage", "pricingNotes": "Prepaid credits, minimum purchase $5 through Stripe, 1 to 1 with dollars and with no expiry. A new account is on the free tier and cannot rent GPUs or storage until it has deposited $5 once. No trial was found, apart from credit codes handed out at events. On-demand rentals bill hourly from the moment an instance is ready, and the balance must cover one hour of all running instances. On 8 October 2026 the open price list showed one H100 virtual machine at $3.19 an hour. Reserved terms of 7, 14 and 30 days take 1, 3 and 5 per cent off and are paid up front. Private Cloud is by contract (https://www.hyperbolic.ai/docs/on-demand/pricing).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs corpus or the OpenAPI document for the GPU API. The archived HyperbolicLabs/hyperbolic-x402 repository covered chat completions on a separate host, not GPU rentals (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://www.hyperbolic.ai/docs/overview/overview", "llmsTxt": "https://www.hyperbolic.ai/docs/llms.txt", "openapi": "https://api.hyperbolic.ai/v2/openapi.json", "capabilities": [ "compute.gpu" ], "tags": [ "hosted", "usage-priced", "openapi", "llms-txt", "api-key", "bare-metal", "status-page", "prepaid" ], "lastRelease": "2026-10-05", "graded": true, "anchor": { "graded": true, "score": 48, "grade": "D", "agentReady": false, "rank": 730, "ranked": true, "rankOf": 842, "categoryRank": 15, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 55, "maintenance": 42, "payments": 15, "reliability": 52, "schema": 77, "security": 47, "transparency": 61 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": -3, "negativeNotes": [ "2026-10-05, removing the `roles` list from the `GET /v2/users/me` response and changing timestamps to RFC 3339 on the v2 API. Both are recorded in the changelog entry for the week they shipped, and no advance notice was found. Documented, so 3 points (https://www.hyperbolic.ai/docs/changelog)." ], "verdict": "The API serves its own OpenAPI 3.1 document without a key, lists GPU prices on an open endpoint and dates the removal of legacy paths at 1 March 2027. API keys carry no scopes and can delete the account, rental creation has no idempotency key, and renting needs a browser signup and a $5 deposit.", "bestFor": "Agents that rent whole H100, H200 or B200 machines or multi-node clusters for training and batch work and that have a funded account.", "strengths": [ "OpenAPI 3.1 document with 65 operations and 53 schemas, served by the API at `GET /v2/openapi.json` with no key", "`GET /v2/on-demand/rental-options` returns live GPU configurations and hourly prices in cents with no key", "Deprecated paths answer with a `Sunset` header and a stated end date of 1 March 2027, and each names its replacement", "Docs index at `/docs/llms.txt`, every page as Markdown and the whole corpus in `llms-full.txt` (188 KB)", "Billing starts only when an instance is ready, and instances that fail to provision within about 3 hours are not charged, per the pricing docs" ], "weaknesses": [ "API keys carry no scopes or expiry in the reviewed documentation, and the same key can call `DELETE /v2/users/me`", "`POST /v2/on-demand/rentals` has no idempotency key, so a retried call can start a second billed rental", "No 429 response, `Retry-After` header or backoff guidance was found in the spec or the docs", "A free-tier account cannot rent GPUs or storage. Renting needs a one-time deposit of at least $5 through Stripe", "The Terms of Service describe a marketplace in which uptime is between buyer and supplier, while the docs state a 99.5 per cent uptime SLA", "No official SDK or CLI for the v2 API was found. The vendor's MCP repository last changed in May 2025 and calls the legacy v1 marketplace paths" ], "agentNotes": [ "Read `GET /v2/on-demand/rental-options` first. It needs no key and lists what can be rented now, with `costPerHourCents` per GPU configuration.", "Send `rentalType` and `gpuCount` to `POST /v2/on-demand/rentals`. Region defaults to `us-central-1` and GPU type to `h100`, so set both from the options list.", "List active rentals before retrying a failed create call. There is no idempotency key and every ready rental bills hourly.", "Save an SSH public key with `POST /v2/ssh-keys` before renting. Without `sshPublicKeyIds` the newest saved key is attached.", "Do not create reserved rentals unless asked. They are paid in full up front and cannot be terminated early." ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 48 } ], "editorialScores": { "ergonomics": 55, "maintenance": 42, "payments": 15, "reliability": 52, "schema": 77, "security": 47, "transparency": 45 }, "provenanceScore": 76 }, "connect": { "http": "curl -X POST https://api.hyperbolic.ai/v2/ssh-keys -H \"Authorization: Bearer $HYPERBOLIC_API_KEY\" -H \"Content-Type: application/json\" -d \"{\\\"publicKey\\\": \\\"$(cat ~/.ssh/id_ed25519.pub)\\\", \\\"name\\\": \\\"Work laptop\\\"}\"" }, "letme": { "capability": "https://letme.dev/compute.gpu", "tool": "https://letme.dev/hyperbolic" }, "area": "models", "unitPrices": [ { "item": "H100 SXM5 80 GB virtual machine, on-demand", "unit": "gpu-hour", "usd": 3.19, "note": "The one configuration on the open price list on 8 October 2026 (us-east-1). Reserved terms take 1 to 5 per cent off" } ], "provenance": { "legalEntity": "Hyperbolic Labs, Inc.", "domain": "hyperbolic.ai", "domainRegistered": "2021-10-11", "endpointOnVendorDomain": true, "terms": "https://www.hyperbolic.ai/terms", "privacy": "https://www.hyperbolic.ai/privacy", "statusPage": "https://status.hyperbolic.ai", "changelog": "https://www.hyperbolic.ai/docs/changelog", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "The Terms of Service, last updated 24 March 2025, name Hyperbolic Labs, Inc. as owner and operator of the platform, apply California law and set AAA arbitration. They cover the website and all services, and no separate API or cloud agreement was found.", "The Privacy Policy is effective 3 June 2024 and names Hyperbolic Labs, Inc. One clause on business transfers names Babylon three times where the company's own name would be expected.", "The API answers at api.hyperbolic.ai, a subdomain of the vendor domain, and on the legacy host api.hyperbolic.xyz.", "https://www.hyperbolic.ai/.well-known/security.txt answered 404. The docs ask for vulnerability reports by email to support@hyperbolic.ai.", "RDAP for hyperbolic.ai gives a registration date of 2021-10-11." ], "score": 76 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hyperbolic.json", "live": { "slug": "hyperbolic", "probe": { "target": "https://api.hyperbolic.ai", "method": "get", "lastAt": "2026-10-09T11:46:30.929630035Z", "lastOk": true, "lastStatus": 200, "lastMs": 607, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 668, "p95ms24h": 763, "samples24h": 44, "samples30d": 44, "days": [ { "date": "2026-10-09", "probes": 44, "ok": 44 } ] }, "vendorStatus": { "page": "https://status.hyperbolic.ai", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-09T07:58:04.049851546Z" }, "updatedAt": "2026-10-09T11:46:30.929630035Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Hugging Face, Inc.", "b": "Hyperbolic Labs, Inc.", "name": "Vendor" }, { "a": "https://api.endpoints.huggingface.cloud", "b": "https://api.hyperbolic.ai", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary service under the Hugging Face Terms of Service. The `huggingface_hub` Python client and CLI are Apache-2.0", "b": "Proprietary service under Hyperbolic's Terms of Service. The unmaintained hyperbolic-mcp repository on GitHub is MIT", "name": "Licence" }, { "a": "19", "b": "none", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-08", "b": "2026-10-05", "name": "Last release" }, { "a": "2022-09-15", "b": "2025-03-24", "name": "Terms last updated" }, { "a": "2023-03-28", "b": "no date given", "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": "yes", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "60M PyPI/wk", "b": "none", "name": "Popularity" } ], "faq": [ { "answer": "Hugging Face Inference Endpoints scores 64.5 (B) on agent readiness against Hyperbolic's 48 (D), and leads in 6 of 7 scored categories.", "question": "Which is better for AI agents, Hugging Face Inference Endpoints or Hyperbolic?" }, { "answer": "Hugging Face Inference Endpoints takes an API key or an OAuth sign-in. Hyperbolic needs an API key.", "question": "Do Hugging Face Inference Endpoints and Hyperbolic need an API key?" }, { "answer": "Yes. Hugging Face Inference Endpoints has a hosted endpoint at https://api.endpoints.huggingface.cloud and Hyperbolic at https://api.hyperbolic.ai.", "question": "Can an agent call Hugging Face Inference Endpoints and Hyperbolic without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 63 against 52", "Agent ergonomics, 62 against 55", "Security \u0026 auth, 83 against 47", "Payments \u0026 pricing, 20 against 15", "Maintenance \u0026 community, 80 against 42", "Transparency \u0026 trust, 68 against 61" ], "also": [ "No incidents deducted, where Hyperbolic 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" }, { "aheadOn": null, "also": null, "goodFor": "Agents that rent whole H100, H200 or B200 machines or multi-node clusters for training and batch work and that have a funded account.", "slug": "hyperbolic", "watchFor": "API keys carry no scopes or expiry in the reviewed documentation, and the same key can call `DELETE /v2/users/me`" } ], "job": { "capability": "compute.gpu", "name": "Compute gpu" }, "others": [ { "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/baseten-vs-hyperbolic.json", "title": "Baseten vs Hyperbolic", "url": "https://www.anchorterminal.com/compare/baseten-vs-hyperbolic" }, { "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/beam-vs-hyperbolic.json", "title": "Beam vs Hyperbolic", "url": "https://www.anchorterminal.com/compare/beam-vs-hyperbolic" }, { "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/cerebrium-vs-hyperbolic.json", "title": "Cerebrium vs Hyperbolic", "url": "https://www.anchorterminal.com/compare/cerebrium-vs-hyperbolic" }, { "json": "https://www.anchorterminal.com/compare/coreweave-vs-hugging-face-inference-endpoints.json", "title": "CoreWeave vs Hugging Face Inference Endpoints", "url": "https://www.anchorterminal.com/compare/coreweave-vs-hugging-face-inference-endpoints" }, { "json": "https://www.anchorterminal.com/compare/coreweave-vs-hyperbolic.json", "title": "CoreWeave vs Hyperbolic", "url": "https://www.anchorterminal.com/compare/coreweave-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-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-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" }, { "json": "https://www.anchorterminal.com/compare/hyperbolic-vs-koyeb.json", "title": "Hyperbolic vs Koyeb", "url": "https://www.anchorterminal.com/compare/hyperbolic-vs-koyeb" }, { "json": "https://www.anchorterminal.com/compare/hyperbolic-vs-lambda.json", "title": "Hyperbolic vs Lambda Cloud", "url": "https://www.anchorterminal.com/compare/hyperbolic-vs-lambda" }, { "json": "https://www.anchorterminal.com/compare/hyperbolic-vs-modal.json", "title": "Hyperbolic vs Modal", "url": "https://www.anchorterminal.com/compare/hyperbolic-vs-modal" }, { "json": "https://www.anchorterminal.com/compare/hyperbolic-vs-nebius-ai-cloud.json", "title": "Hyperbolic vs Nebius AI Cloud", "url": "https://www.anchorterminal.com/compare/hyperbolic-vs-nebius-ai-cloud" }, { "json": "https://www.anchorterminal.com/compare/hyperbolic-vs-northflank.json", "title": "Hyperbolic vs Northflank", "url": "https://www.anchorterminal.com/compare/hyperbolic-vs-northflank" }, { "json": "https://www.anchorterminal.com/compare/hyperbolic-vs-replicate-deploy.json", "title": "Hyperbolic vs Replicate Deployments", "url": "https://www.anchorterminal.com/compare/hyperbolic-vs-replicate-deploy" }, { "json": "https://www.anchorterminal.com/compare/hyperbolic-vs-runpod.json", "title": "Hyperbolic vs Runpod", "url": "https://www.anchorterminal.com/compare/hyperbolic-vs-runpod" }, { "json": "https://www.anchorterminal.com/compare/hyperbolic-vs-thunder-compute.json", "title": "Hyperbolic vs Thunder Compute", "url": "https://www.anchorterminal.com/compare/hyperbolic-vs-thunder-compute" }, { "json": "https://www.anchorterminal.com/compare/hyperbolic-vs-vast-ai.json", "title": "Hyperbolic vs Vast.ai", "url": "https://www.anchorterminal.com/compare/hyperbolic-vs-vast-ai" }, { "json": "https://www.anchorterminal.com/compare/hyperbolic-vs-verda.json", "title": "Hyperbolic vs Verda", "url": "https://www.anchorterminal.com/compare/hyperbolic-vs-verda" } ], "scores": [ { "by": 11, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 63, "hyperbolic": 52, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 4, "edge": "hyperbolic", "hugging-face-inference-endpoints": 73, "hyperbolic": 77, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 7, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 62, "hyperbolic": 55, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 36, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 83, "hyperbolic": 47, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 5, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 20, "hyperbolic": 15, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 38, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 80, "hyperbolic": 42, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 7, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 68, "hyperbolic": 61, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Hugging Face Inference Endpoints scores 64.5 (B) on agent readiness against Hyperbolic's 48 (D), and leads in 6 of 7 scored categories. Both do compute gpu.", "verdicts": { "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.", "hyperbolic": "The API serves its own OpenAPI 3.1 document without a key, lists GPU prices on an open endpoint and dates the removal of legacy paths at 1 March 2027. API keys carry no scopes and can delete the account, rental creation has no idempotency key, and renting needs a browser signup and a $5 deposit." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic", "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic.md", "slim": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic.min.md" }, "markdown": "Hugging Face Inference Endpoints scores 64.5 (B) on agent readiness against Hyperbolic's 48 (D), and leads in 6 of 7 scored categories. Both do compute gpu.\n\n- Hugging Face Inference Endpoints: grade B, 64.5/100, rank #314 of 842. 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- Hyperbolic: grade D, 48/100, rank #730 of 842. Markdown https://www.anchorterminal.com/tools/hyperbolic.md · JSON https://www.anchorterminal.com/api/v1/tools/hyperbolic.json\n\n## Which one, for what\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, 63 against 52\n- Agent ergonomics, 62 against 55\n- Security \u0026 auth, 83 against 47\n- Payments \u0026 pricing, 20 against 15\n- Maintenance \u0026 community, 80 against 42\n- Transparency \u0026 trust, 68 against 61\n\nAlso in its favour:\n- No incidents deducted, where Hyperbolic 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### Hyperbolic (D)\n\nGood for: Agents that rent whole H100, H200 or B200 machines or multi-node clusters for training and batch work and that have a funded account.\n\nWatch for: API keys carry no scopes or expiry in the reviewed documentation, and the same key can call `DELETE /v2/users/me`\n\n\n## Score by category\n\n| Category | Weight | Hugging Face Inference Endpoints | Hyperbolic | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 63 | 52 | Hugging Face Inference Endpoints +11 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 73 | 77 | Hyperbolic +4 |\n| Agent ergonomics | 13% (16.2 this run) | 62 | 55 | Hugging Face Inference Endpoints +7 |\n| Security \u0026 auth | 14% (17.5 this run) | 83 | 47 | Hugging Face Inference Endpoints +36 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 15 | Hugging Face Inference Endpoints +5 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 80 | 42 | Hugging Face Inference Endpoints +38 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 68 | 61 | Hugging Face Inference Endpoints +7 |\n| Negative events | ≤15 | 0 | -3 | |\n| **Total** | | **64.5 · B** | **48 · D** | |\n\n## Facts side by side\n\n| Fact | Hugging Face Inference Endpoints | Hyperbolic |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Hugging Face, Inc. | Hyperbolic Labs, Inc. |\n| Hosted endpoint | `https://api.endpoints.huggingface.cloud` | `https://api.hyperbolic.ai` |\n| Transports | HTTP | HTTP |\n| Auth | OAuth or key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Proprietary service under the Hugging Face Terms of Service. The `huggingface_hub` Python client and CLI are Apache-2.0 | Proprietary service under Hyperbolic's Terms of Service. The unmaintained hyperbolic-mcp repository on GitHub is MIT |\n| Tools exposed | 19 | none |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-08 | 2026-10-05 |\n| Terms last updated | 2022-09-15 | 2025-03-24 |\n| Privacy policy last updated | 2023-03-28 | no date given |\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 | yes |\n| Arbitration or class-action waiver | not found in the text | yes |\n| Popularity | 60M PyPI/wk | none |\n\n## Verdicts\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**Hyperbolic.** The API serves its own OpenAPI 3.1 document without a key, lists GPU prices on an open endpoint and dates the removal of legacy paths at 1 March 2027. API keys carry no scopes and can delete the account, rental creation has no idempotency key, and renting needs a browser signup and a $5 deposit.\n\n## Before you call either\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### Hyperbolic\n\n1. Read `GET /v2/on-demand/rental-options` first. It needs no key and lists what can be rented now, with `costPerHourCents` per GPU configuration.\n2. Send `rentalType` and `gpuCount` to `POST /v2/on-demand/rentals`. Region defaults to `us-central-1` and GPU type to `h100`, so set both from the options list.\n3. List active rentals before retrying a failed create call. There is no idempotency key and every ready rental bills hourly.\n4. Save an SSH public key with `POST /v2/ssh-keys` before renting. Without `sshPublicKeyIds` the newest saved key is attached.\n5. Do not create reserved rentals unless asked. They are paid in full up front and cannot be terminated early.\n\n## Questions\n\n### Which is better for AI agents, Hugging Face Inference Endpoints or Hyperbolic?\n\nHugging Face Inference Endpoints scores 64.5 (B) on agent readiness against Hyperbolic's 48 (D), and leads in 6 of 7 scored categories.\n\n### Do Hugging Face Inference Endpoints and Hyperbolic need an API key?\n\nHugging Face Inference Endpoints takes an API key or an OAuth sign-in. Hyperbolic needs an API key.\n\n### Can an agent call Hugging Face Inference Endpoints and Hyperbolic without installing anything?\n\nYes. Hugging Face Inference Endpoints has a hosted endpoint at https://api.endpoints.huggingface.cloud and Hyperbolic at https://api.hyperbolic.ai.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hugging-face-inference-endpoints\", \"b\": \"hyperbolic\"}`. From a terminal: `anchor compare hugging-face-inference-endpoints hyperbolic`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-endpoints.json and https://www.anchorterminal.com/api/v1/tools/hyperbolic.json\n\n## Other comparisons with Hugging Face Inference Endpoints or Hyperbolic\n\n- [Baseten vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints.md)\n- [Baseten vs Hyperbolic](https://www.anchorterminal.com/compare/baseten-vs-hyperbolic.md)\n- [Beam vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/beam-vs-hugging-face-inference-endpoints.md)\n- [Beam vs Hyperbolic](https://www.anchorterminal.com/compare/beam-vs-hyperbolic.md)\n- [Cerebrium vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/cerebrium-vs-hugging-face-inference-endpoints.md)\n- [Cerebrium vs Hyperbolic](https://www.anchorterminal.com/compare/cerebrium-vs-hyperbolic.md)\n- [CoreWeave vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/coreweave-vs-hugging-face-inference-endpoints.md)\n- [CoreWeave vs Hyperbolic](https://www.anchorterminal.com/compare/coreweave-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 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 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- [Hyperbolic vs Koyeb](https://www.anchorterminal.com/compare/hyperbolic-vs-koyeb.md)\n- [Hyperbolic vs Lambda Cloud](https://www.anchorterminal.com/compare/hyperbolic-vs-lambda.md)\n- [Hyperbolic vs Modal](https://www.anchorterminal.com/compare/hyperbolic-vs-modal.md)\n- [Hyperbolic vs Nebius AI Cloud](https://www.anchorterminal.com/compare/hyperbolic-vs-nebius-ai-cloud.md)\n- [Hyperbolic vs Northflank](https://www.anchorterminal.com/compare/hyperbolic-vs-northflank.md)\n- [Hyperbolic vs Replicate Deployments](https://www.anchorterminal.com/compare/hyperbolic-vs-replicate-deploy.md)\n- [Hyperbolic vs Runpod](https://www.anchorterminal.com/compare/hyperbolic-vs-runpod.md)\n- [Hyperbolic vs Thunder Compute](https://www.anchorterminal.com/compare/hyperbolic-vs-thunder-compute.md)\n- [Hyperbolic vs Vast.ai](https://www.anchorterminal.com/compare/hyperbolic-vs-vast-ai.md)\n- [Hyperbolic vs Verda](https://www.anchorterminal.com/compare/hyperbolic-vs-verda.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Hugging Face Inference Endpoints vs Hyperbolic", "url": "" } ], "description": "Hugging Face Inference Endpoints scores 64.5 (B) on agent readiness against Hyperbolic's 48 (D), and leads in 6 of 7 scored categories. Both do compute gpu. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Hugging Face Inference Endpoints B 64.5", "Hyperbolic D 48", "scores" ], "h1": "Hugging Face Inference Endpoints vs Hyperbolic", "image": "https://www.anchorterminal.com/assets/og/compare-hugging-face-inference-endpoints-vs-hyperbolic.png", "path": "/compare/hugging-face-inference-endpoints-vs-hyperbolic", "published": "2026-10-01", "section": "tools", "title": "Hugging Face Inference Endpoints vs Hyperbolic for AI agents", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic" }, "tokens": { "markdown": 2850, "slim": 680 }, "version": 1 }