{ "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 and Modal score within a point of each other on agent readiness, 64.5 (B) and 63.6 (B). Modal leads on reliability, payments \u0026 pricing and maintenance \u0026 community.", "b": { "slug": "modal", "name": "Modal", "vendor": "Modal", "vendorUrl": "https://modal.com", "kind": "platform", "category": "gpu-compute", "summary": "Serverless functions, web endpoints, servers and GPU jobs from a Python decorator, with JavaScript and Go SDKs.", "url": "https://www.anchorterminal.com/tools/modal", "markdownUrl": "https://www.anchorterminal.com/tools/modal.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/modal.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/modal.json", "repo": "https://github.com/modal-labs/modal-client", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "pypi", "name": "modal" }, { "registry": "npm", "name": "modal" } ], "auth": "api-key", "authNotes": "No public REST API for deploying. The SDKs and CLI authenticate with a token ID and secret from `modal token new`, read from `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` or `~/.modal.toml`; tokens can carry a TTL. Deployed web endpoints are open by default and can be locked with proxy tokens sent as `Modal-Key` and `Modal-Secret` headers. Servers and Endpoints require a proxy token by default, sent as `Authorization: Bearer \u003cid\u003e.\u003csecret\u003e`.", "pricing": "freemium", "pricingNotes": "Starter is $0 a month with $30 of compute included every month, 3 seats, 100 containers and 10 concurrent GPUs. Team is $250 a month plus compute with $100 included, unlimited seats, 5,000 containers and 50 concurrent GPUs. Enterprise is custom. GPUs bill per second with nothing charged at zero containers. T4 $0.000164, L4 $0.000222, A10 $0.000306, L40S $0.000542, A100 40 GB $0.000583, A100 80 GB $0.000694, RTX PRO 6000 $0.000842, H100 $0.001097, H200 $0.001261, B200 $0.001736 and B300 $0.001972 a second. The pricing page lists CPU at $0.0000131 a core-second (0.125 core minimum per container) and memory at $0.00000222 a GiB-second, and volumes at $0.09 a GiB-month after 1 TiB free (https://modal.com/pricing).", "priceSummary": "$250 / mo", "where": "local", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 514, "npmWeekly": 940973, "pypiWeekly": 10146778, "asOf": "2026-09-30" }, "docsUrl": "https://modal.com/docs/guide", "llmsTxt": "https://modal.com/llms.txt", "capabilities": [ "compute.gpu", "compute.serverless", "compute.endpoints", "compute.batch", "compute.containers" ], "tags": [ "hosted", "freemium", "free-tier", "no-card", "python", "typescript", "go", "llms-txt", "enterprise" ], "lastRelease": "2026-09-28", "graded": true, "anchor": { "graded": true, "score": 63.6, "grade": "B", "agentReady": false, "rank": 346, "ranked": true, "rankOf": 842, "categoryRank": 4, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 57, "maintenance": 85, "payments": 30, "reliability": 70, "schema": 70, "security": 68, "transparency": 67 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Scale to zero by default, per-second billing and about one-second container boots. No REST API or OpenAPI spec for deploying or invoking Functions.", "bestFor": "Python teams that want GPU functions, batch jobs and HTTP endpoints from one decorator with scale to zero.", "strengths": [ "Scale to zero by default, per-second billing and about one-second container boots", "Retention stated per data type (inputs and outputs up to 7 days, logs 1 to 30 days)", "Python, JavaScript and Go SDKs, with llms.txt and dated release notes", "Four short incidents on the status page between July and September 2026", "SOC 2 Type 2, a private HackerOne programme and published disclosure response times" ], "weaknesses": [ "No REST API or OpenAPI spec for deploying or invoking Functions", "Web endpoints are open by default until proxy tokens are added", "RBAC, audit logs and HIPAA only on Enterprise", "No published SLA, and Starter caps concurrent GPUs at 10", "Region pinning costs 1.15 to 1.75 times the base price" ], "agentNotes": [ "Create a proxy token and require it on every web endpoint before sharing the URL; endpoints are public by default", "Pass a list to `gpu=` (for example `[\"H100\", \"A100-80GB\"]`) so a job still runs when the first choice is unavailable", "Set `scaledown_window` and `min_containers` explicitly; the defaults are 60 seconds and 0", "Use `.spawn()` and poll the call ID for long work instead of holding a web request open", "Keep web endpoint traffic under 200 requests a second or ask Modal to raise the limit" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 4, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.6 } ], "editorialScores": { "ergonomics": 57, "maintenance": 85, "payments": 30, "reliability": 70, "schema": 70, "security": 68, "transparency": 63 }, "provenanceScore": 71 }, "connect": { "install": "pip install modal \u0026\u0026 modal setup", "http": "curl -X POST \"https://$MODAL_WORKSPACE--my-app-predict.modal.run\" \\\n -H \"Modal-Key: $MODAL_PROXY_KEY\" -H \"Modal-Secret: $MODAL_PROXY_SECRET\" \\\n -H \"Content-Type: application/json\" -d '{\"prompt\":\"hello\"}'" }, "letme": { "capability": "https://letme.dev/compute.gpu", "tool": "https://letme.dev/modal" }, "sameCompany": [ "modal-sandboxes" ], "area": "models", "unitPrices": [ { "item": "H100 80 GB", "unit": "gpu-hour", "usd": 3.95, "note": "$0.001097 a second, may be upgraded to H200 at the same price" }, { "item": "H200 141 GB", "unit": "gpu-hour", "usd": 4.54, "note": "$0.001261 a second" }, { "item": "B200 180 GB", "unit": "gpu-hour", "usd": 6.25, "note": "$0.001736 a second" }, { "item": "A100 80 GB", "unit": "gpu-hour", "usd": 2.5, "note": "$0.000694 a second" }, { "item": "L40S 48 GB", "unit": "gpu-hour", "usd": 1.95, "note": "$0.000542 a second" }, { "item": "L4 24 GB", "unit": "gpu-hour", "usd": 0.8, "note": "$0.000222 a second" }, { "item": "T4 16 GB", "unit": "gpu-hour", "usd": 0.59, "note": "$0.000164 a second" }, { "item": "Team plan", "unit": "month", "usd": 250, "note": "Plus compute, $100 included, 50 concurrent GPUs" } ], "provenance": { "legalEntity": "Modal Labs, Inc.", "domain": "modal.com", "domainRegistered": "1999-03-18", "domainNote": "modal.com was registered in 1999, long before Modal Labs, so the domain was bought later.", "endpointOnVendorDomain": false, "terms": "https://modal.com/legal/terms", "privacy": "https://modal.com/legal/privacy-policy", "statusPage": "https://status.modal.com", "changelog": "https://modal.com/docs/sdk/py/releases", "securityTxt": "none", "checked": "2026-09-30", "notes": [ "Terms (May 2026) name Modal Labs, Inc., a Delaware corporation, under California law.", "Deployed web endpoints and Servers are served from *.modal.run, a separate domain from modal.com. Deployment itself goes through the SDK, so there's no public API base URL to check.", "modal.com/.well-known/security.txt returns 404. The security guide gives security@modal.com and a private HackerOne programme.", "Modal Sandboxes are listed separately under code sandboxes." ], "score": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/modal.json", "live": { "slug": "modal", "vendorStatus": { "page": "https://status.modal.com", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-09T07:58:11.313506769Z" }, "versions": [ { "registry": "npm", "name": "modal", "version": "0.11.0", "seenAt": "2026-10-08T16:21:43.713175523Z" }, { "registry": "pypi", "name": "modal", "version": "1.6.1", "released": "2026-10-03", "seenAt": "2026-10-08T16:21:43.571967985Z" } ], "githubStars": 522, "npmWeekly": 976721, "pypiWeekly": 10794735, "securityTxt": { "url": "https://modal.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:38:35.387911158Z" }, "llmsTxt": { "url": "https://modal.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:41.354164105Z" }, "domain": { "domain": "modal.com", "registered": "1999-03-18", "source": "https://rdap.verisign.com/com/v1/domain/modal.com", "checkedAt": "2026-10-04T13:03:51.14581991Z" }, "updatedAt": "2026-10-09T07:58:11.313506769Z" } }, "facts": [ { "a": "HTTP API", "b": "Model platform", "name": "Kind" }, { "a": "Hugging Face, Inc.", "b": "Modal", "name": "Vendor" }, { "a": "https://api.endpoints.huggingface.cloud", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "", "name": "Transports" }, { "a": "OAuth or key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Freemium", "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": "Apache-2.0", "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": "2026-09-28", "name": "Last release" }, { "a": "2022-09-15", "b": "2026-05-01", "name": "Terms last updated" }, { "a": "2023-03-28", "b": "2023-05-17", "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": "yes", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "60M PyPI/wk", "b": "514 stars, 941k npm/wk, 10.1M PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "4/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Hugging Face Inference Endpoints and Modal score within a point of each other on agent readiness, 64.5 (B) and 63.6 (B). Modal leads on reliability, payments \u0026 pricing and maintenance \u0026 community.", "question": "Which is better for AI agents, Hugging Face Inference Endpoints or Modal?" }, { "answer": "Hugging Face Inference Endpoints has a hosted endpoint at https://api.endpoints.huggingface.cloud. No hosted endpoint is listed for Modal.", "question": "Can an agent call Hugging Face Inference Endpoints and Modal without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Agent ergonomics, 62 against 57", "Security \u0026 auth, 83 against 68" ], "also": [ "A hosted endpoint, with nothing to install" ], "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": [ "Reliability, 70 against 63", "Payments \u0026 pricing, 30 against 20", "Maintenance \u0026 community, 85 against 80" ], "also": [ "Free to start without a card" ], "goodFor": "Python teams that want GPU functions, batch jobs and HTTP endpoints from one decorator with scale to zero.", "slug": "modal", "watchFor": "No REST API or OpenAPI spec for deploying or invoking Functions" } ], "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-modal.json", "title": "Baseten vs Modal", "url": "https://www.anchorterminal.com/compare/baseten-vs-modal" }, { "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-modal.json", "title": "Beam vs Modal", "url": "https://www.anchorterminal.com/compare/beam-vs-modal" }, { "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-modal.json", "title": "Cerebrium vs Modal", "url": "https://www.anchorterminal.com/compare/cerebrium-vs-modal" }, { "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-modal.json", "title": "CoreWeave vs Modal", "url": "https://www.anchorterminal.com/compare/coreweave-vs-modal" }, { "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-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-modal.json", "title": "Hyperbolic vs Modal", "url": "https://www.anchorterminal.com/compare/hyperbolic-vs-modal" }, { "json": "https://www.anchorterminal.com/compare/koyeb-vs-modal.json", "title": "Koyeb vs Modal", "url": "https://www.anchorterminal.com/compare/koyeb-vs-modal" }, { "json": "https://www.anchorterminal.com/compare/lambda-vs-modal.json", "title": "Lambda Cloud vs Modal", "url": "https://www.anchorterminal.com/compare/lambda-vs-modal" }, { "json": "https://www.anchorterminal.com/compare/modal-vs-nebius-ai-cloud.json", "title": "Modal vs Nebius AI Cloud", "url": "https://www.anchorterminal.com/compare/modal-vs-nebius-ai-cloud" }, { "json": "https://www.anchorterminal.com/compare/modal-vs-northflank.json", "title": "Modal vs Northflank", "url": "https://www.anchorterminal.com/compare/modal-vs-northflank" }, { "json": "https://www.anchorterminal.com/compare/modal-vs-replicate-deploy.json", "title": "Modal vs Replicate Deployments", "url": "https://www.anchorterminal.com/compare/modal-vs-replicate-deploy" }, { "json": "https://www.anchorterminal.com/compare/modal-vs-runpod.json", "title": "Modal vs Runpod", "url": "https://www.anchorterminal.com/compare/modal-vs-runpod" }, { "json": "https://www.anchorterminal.com/compare/modal-vs-thunder-compute.json", "title": "Modal vs Thunder Compute", "url": "https://www.anchorterminal.com/compare/modal-vs-thunder-compute" }, { "json": "https://www.anchorterminal.com/compare/modal-vs-vast-ai.json", "title": "Modal vs Vast.ai", "url": "https://www.anchorterminal.com/compare/modal-vs-vast-ai" }, { "json": "https://www.anchorterminal.com/compare/modal-vs-verda.json", "title": "Modal vs Verda", "url": "https://www.anchorterminal.com/compare/modal-vs-verda" } ], "scores": [ { "by": 7, "edge": "modal", "hugging-face-inference-endpoints": 63, "key": "reliability", "modal": 70, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 3, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 73, "key": "schema", "modal": 70, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 5, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 62, "key": "ergonomics", "modal": 57, "name": "Agent ergonomics", "weight": 13 }, { "by": 15, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 83, "key": "security", "modal": 68, "name": "Security \u0026 auth", "weight": 14 }, { "by": 10, "edge": "modal", "hugging-face-inference-endpoints": 20, "key": "payments", "modal": 30, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 5, "edge": "modal", "hugging-face-inference-endpoints": 80, "key": "maintenance", "modal": 85, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 1, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 68, "key": "transparency", "modal": 67, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Hugging Face Inference Endpoints and Modal score within a point of each other on agent readiness, 64.5 (B) and 63.6 (B). Modal leads on reliability, payments \u0026 pricing and maintenance \u0026 community. 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.", "modal": "Scale to zero by default, per-second billing and about one-second container boots. No REST API or OpenAPI spec for deploying or invoking Functions." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-modal", "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-modal.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-modal.md", "slim": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-modal.min.md" }, "markdown": "Hugging Face Inference Endpoints and Modal score within a point of each other on agent readiness, 64.5 (B) and 63.6 (B). Modal leads on reliability, payments \u0026 pricing and maintenance \u0026 community. 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- Modal: grade B, 63.6/100, rank #346 of 842. Markdown https://www.anchorterminal.com/tools/modal.md · JSON https://www.anchorterminal.com/api/v1/tools/modal.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- Agent ergonomics, 62 against 57\n- Security \u0026 auth, 83 against 68\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\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### Modal (B)\n\nGood for: Python teams that want GPU functions, batch jobs and HTTP endpoints from one decorator with scale to zero.\n\nAhead on:\n- Reliability, 70 against 63\n- Payments \u0026 pricing, 30 against 20\n- Maintenance \u0026 community, 85 against 80\n\nAlso in its favour:\n- Free to start without a card\n\nWatch for: No REST API or OpenAPI spec for deploying or invoking Functions\n\n\n## Score by category\n\n| Category | Weight | Hugging Face Inference Endpoints | Modal | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 63 | 70 | Modal +7 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 73 | 70 | Hugging Face Inference Endpoints +3 |\n| Agent ergonomics | 13% (16.2 this run) | 62 | 57 | Hugging Face Inference Endpoints +5 |\n| Security \u0026 auth | 14% (17.5 this run) | 83 | 68 | Hugging Face Inference Endpoints +15 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 30 | Modal +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 80 | 85 | Modal +5 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 68 | 67 | Hugging Face Inference Endpoints +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64.5 · B** | **63.6 · B** | |\n\n## Facts side by side\n\n| Fact | Hugging Face Inference Endpoints | Modal |\n| --- | --- | --- |\n| Kind | HTTP API | Model platform |\n| Vendor | Hugging Face, Inc. | Modal |\n| Hosted endpoint | `https://api.endpoints.huggingface.cloud` | no (local only) |\n| Transports | HTTP | |\n| Auth | OAuth or key | API key |\n| Pricing | Pay per use | Freemium |\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 | Apache-2.0 |\n| Tools exposed | 19 | none |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-08 | 2026-09-28 |\n| Terms last updated | 2022-09-15 | 2026-05-01 |\n| Privacy policy last updated | 2023-03-28 | 2023-05-17 |\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 | yes | not found in the text |\n| Arbitration or class-action waiver | not found in the text | not found in the text |\n| Popularity | 60M PyPI/wk | 514 stars, 941k npm/wk, 10.1M PyPI/wk |\n| Agent reviews | none | 4/5 (2) |\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**Modal.** Scale to zero by default, per-second billing and about one-second container boots. No REST API or OpenAPI spec for deploying or invoking Functions.\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### Modal\n\n1. Create a proxy token and require it on every web endpoint before sharing the URL; endpoints are public by default\n2. Pass a list to `gpu=` (for example `[\"H100\", \"A100-80GB\"]`) so a job still runs when the first choice is unavailable\n3. Set `scaledown_window` and `min_containers` explicitly; the defaults are 60 seconds and 0\n4. Use `.spawn()` and poll the call ID for long work instead of holding a web request open\n5. Keep web endpoint traffic under 200 requests a second or ask Modal to raise the limit\n\n## Questions\n\n### Which is better for AI agents, Hugging Face Inference Endpoints or Modal?\n\nHugging Face Inference Endpoints and Modal score within a point of each other on agent readiness, 64.5 (B) and 63.6 (B). Modal leads on reliability, payments \u0026 pricing and maintenance \u0026 community.\n\n### Can an agent call Hugging Face Inference Endpoints and Modal without installing anything?\n\nHugging Face Inference Endpoints has a hosted endpoint at https://api.endpoints.huggingface.cloud. No hosted endpoint is listed for Modal.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-modal.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-modal.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hugging-face-inference-endpoints\", \"b\": \"modal\"}`. From a terminal: `anchor compare hugging-face-inference-endpoints modal`\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/modal.json\n\n## Other comparisons with Hugging Face Inference Endpoints or Modal\n\n- [Baseten vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints.md)\n- [Baseten vs Modal](https://www.anchorterminal.com/compare/baseten-vs-modal.md)\n- [Beam vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/beam-vs-hugging-face-inference-endpoints.md)\n- [Beam vs Modal](https://www.anchorterminal.com/compare/beam-vs-modal.md)\n- [Cerebrium vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/cerebrium-vs-hugging-face-inference-endpoints.md)\n- [Cerebrium vs Modal](https://www.anchorterminal.com/compare/cerebrium-vs-modal.md)\n- [CoreWeave vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/coreweave-vs-hugging-face-inference-endpoints.md)\n- [CoreWeave vs Modal](https://www.anchorterminal.com/compare/coreweave-vs-modal.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 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 Modal](https://www.anchorterminal.com/compare/hyperbolic-vs-modal.md)\n- [Koyeb vs Modal](https://www.anchorterminal.com/compare/koyeb-vs-modal.md)\n- [Lambda Cloud vs Modal](https://www.anchorterminal.com/compare/lambda-vs-modal.md)\n- [Modal vs Nebius AI Cloud](https://www.anchorterminal.com/compare/modal-vs-nebius-ai-cloud.md)\n- [Modal vs Northflank](https://www.anchorterminal.com/compare/modal-vs-northflank.md)\n- [Modal vs Replicate Deployments](https://www.anchorterminal.com/compare/modal-vs-replicate-deploy.md)\n- [Modal vs Runpod](https://www.anchorterminal.com/compare/modal-vs-runpod.md)\n- [Modal vs Thunder Compute](https://www.anchorterminal.com/compare/modal-vs-thunder-compute.md)\n- [Modal vs Vast.ai](https://www.anchorterminal.com/compare/modal-vs-vast-ai.md)\n- [Modal vs Verda](https://www.anchorterminal.com/compare/modal-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 Modal", "url": "" } ], "description": "Hugging Face Inference Endpoints and Modal score within a point of each other on agent readiness, 64.5 (B) and 63.6 (B). Modal leads on reliability, payments \u0026 pricing and maintenance \u0026 community. Both do compute gpu. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Hugging Face Inference Endpoints B 64.5", "Modal B 63.6", "scores" ], "h1": "Hugging Face Inference Endpoints vs Modal", "image": "https://www.anchorterminal.com/assets/og/compare-hugging-face-inference-endpoints-vs-modal.png", "path": "/compare/hugging-face-inference-endpoints-vs-modal", "published": "2026-10-01", "section": "tools", "title": "Hugging Face Inference Endpoints vs Modal for AI agents", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-modal" }, "tokens": { "markdown": 2700, "slim": 680 }, "version": 1 }