{
  "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": "2026-10-09T11:29:02.812455151Z",
          "lastOk": true,
          "lastStatus": 200,
          "lastMs": 255,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 255,
          "p95ms24h": 300,
          "samples24h": 41,
          "samples30d": 41,
          "days": [
            {
              "date": "2026-10-09",
              "probes": 41,
              "ok": 41
            }
          ]
        },
        "vendorStatus": {
          "page": "https://status.huggingface.co",
          "indicator": "unknown",
          "summary": "no machine-readable status found",
          "checkedAt": "2026-10-09T07:58:03.654181492Z"
        },
        "updatedAt": "2026-10-09T11:29:02.812455151Z"
      }
    },
    "answer": "Hugging Face Inference Endpoints and Replicate Deployments score within a point of each other on agent readiness, 64.5 (B) and 63.6 (B). Replicate Deployments leads on reliability, schema \u0026 documentation, agent ergonomics, payments \u0026 pricing and transparency \u0026 trust.",
    "b": {
      "slug": "replicate-deploy",
      "name": "Replicate Deployments",
      "vendor": "Replicate",
      "vendorUrl": "https://replicate.com",
      "kind": "http-api",
      "category": "gpu-compute",
      "summary": "Replicate's service for deploying and running custom models.",
      "url": "https://www.anchorterminal.com/tools/replicate-deploy",
      "markdownUrl": "https://www.anchorterminal.com/tools/replicate-deploy.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/replicate-deploy.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/replicate-deploy.json",
      "repo": "https://github.com/replicate/cog",
      "license": "Apache-2.0",
      "transports": [
        "http",
        "sse",
        "stdio"
      ],
      "remoteUrl": "https://api.replicate.com/v1",
      "packages": [
        {
          "registry": "npm",
          "name": "replicate"
        },
        {
          "registry": "pypi",
          "name": "replicate"
        },
        {
          "registry": "npm",
          "name": "replicate-mcp"
        }
      ],
      "auth": "api-key",
      "authNotes": "Bearer API token on every call to api.replicate.com. `cog push` uses the same token to upload a model image. The hosted MCP at https://mcp.replicate.com/sse asks for the token in a browser flow and holds it for the client; the local `replicate-mcp` package reads `REPLICATE_API_TOKEN`.",
      "pricing": "usage",
      "pricingNotes": "Private models and deployments bill per second for the whole time an instance is up, set-up and idle included, from prepaid credit or monthly in arrears. CPU $0.000100 a second ($0.36 an hour), T4 $0.000225 ($0.81), L40S $0.000975 ($3.51), A100 80 GB $0.001400 ($5.04), H100 $0.001525 ($5.49), 2x L40S $0.001950 ($7.02), 2x A100 $0.002800 ($10.08). 2x H100 ($10.98), 4x and 8x L40S, A100 and H100 up to $43.92 an hour need a committed-spend contract. Fast-booting fine-tunes bill only while active. Public models bill only active time and not failures (https://replicate.com/pricing, https://replicate.com/docs/topics/billing).",
      "priceSummary": "Pay per use",
      "where": "both",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 9500,
        "npmWeekly": 634116,
        "pypiWeekly": 386704,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://replicate.com/docs/topics/deployments",
      "llmsTxt": "https://replicate.com/docs/llms.txt",
      "openapi": "https://api.replicate.com/openapi.json",
      "capabilities": [
        "compute.gpu",
        "compute.endpoints",
        "compute.serverless",
        "compute.containers"
      ],
      "tags": [
        "hosted",
        "usage-priced",
        "mcp",
        "llms-txt",
        "openapi",
        "python",
        "typescript",
        "async-jobs",
        "webhooks",
        "open-source"
      ],
      "lastRelease": "2026-09-22",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 63.6,
        "grade": "B",
        "agentReady": false,
        "rank": 347,
        "ranked": true,
        "rankOf": 842,
        "categoryRank": 5,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 68,
          "maintenance": 70,
          "payments": 30,
          "reliability": 75,
          "schema": 85,
          "security": 40,
          "transparency": 78
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "OpenAPI file, llms.txt and an MCP server with a two-tool code mode. Private instances bill set-up and idle time, H100 at $5.49 an hour.",
        "bestFor": "Teams already calling Replicate's public models who want their own model behind the same API, MCP server and webhooks.",
        "strengths": [
          "OpenAPI file, llms.txt and an MCP server with a two-tool code mode",
          "Deployment min and max instances settable over the API, 0 allowed",
          "API prediction data deleted after one hour by default",
          "Published limits, 600 prediction creates and 3,000 other calls a minute",
          "Leaked tokens found on GitHub are disabled automatically"
        ],
        "weaknesses": [
          "Private instances bill set-up and idle time, H100 at $5.49 an hour",
          "API tokens have no scopes, expiry or audit log",
          "Changelog silent since 21 April 2026",
          "Only T4, L40S, A100 and H100, and more than 2 GPUs needs a committed-spend contract",
          "Two September 2026 incidents ran 15 and 20 hours, both marked minor"
        ],
        "agentNotes": [
          "List `GET /v1/hardware` first and use the returned `sku` in the deployment body",
          "Set `min_instances` to 0 for bursty work; a warm H100 bills $5.49 an hour whether called or not",
          "Send `Prefer: wait` on deployment predictions to block instead of polling",
          "Copy outputs within an hour; API prediction data is deleted after that",
          "Wait for the reset time in the 429 body before retrying; prediction creates cap at 600 a minute"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "B",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 63.6
          }
        ],
        "editorialScores": {
          "ergonomics": 68,
          "maintenance": 70,
          "payments": 30,
          "reliability": 75,
          "schema": 85,
          "security": 40,
          "transparency": 69
        },
        "provenanceScore": 87
      },
      "connect": {
        "install": "pip install cog replicate",
        "http": "curl -X POST \"https://api.replicate.com/v1/deployments/$REPLICATE_OWNER/my-deployment/predictions\" \\\n  -H \"Authorization: Bearer $REPLICATE_API_TOKEN\" -H \"Content-Type: application/json\" -H \"Prefer: wait\" \\\n  -d '{\"input\":{\"prompt\":\"hello\"}}'",
        "claudeCode": "claude mcp add replicate https://mcp.replicate.com/sse --transport sse --scope user",
        "config": {
          "mcpServers": {
            "replicate": {
              "args": [
                "-y",
                "replicate-mcp"
              ],
              "command": "npx",
              "env": {
                "REPLICATE_API_TOKEN": "${REPLICATE_API_TOKEN}"
              }
            }
          }
        }
      },
      "letme": {
        "capability": "https://letme.dev/compute.gpu",
        "tool": "https://letme.dev/replicate-deploy"
      },
      "sameCompany": [
        "replicate-image",
        "replicate-video",
        "replicate-musicgen"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "H100 80 GB",
          "unit": "gpu-hour",
          "usd": 5.49,
          "note": "$0.001525 a second, including set-up and idle"
        },
        {
          "item": "A100 80 GB",
          "unit": "gpu-hour",
          "usd": 5.04,
          "note": "$0.001400 a second"
        },
        {
          "item": "L40S 48 GB",
          "unit": "gpu-hour",
          "usd": 3.51,
          "note": "$0.000975 a second"
        },
        {
          "item": "T4 16 GB",
          "unit": "gpu-hour",
          "usd": 0.81,
          "note": "$0.000225 a second"
        }
      ],
      "provenance": {
        "legalEntity": "Replicate, LLC",
        "domain": "replicate.com",
        "domainRegistered": "1998-05-26",
        "domainNote": "replicate.com was registered in 1998, long before Replicate the company existed.",
        "endpointOnVendorDomain": true,
        "terms": "https://replicate.com/terms",
        "privacy": "https://replicate.com/privacy",
        "statusPage": "https://replicatestatus.com",
        "changelog": "https://replicate.com/changelog",
        "securityTxt": "none",
        "checked": "2026-09-30",
        "notes": [
          "Terms last updated 2026-04-01 name Replicate, LLC as the contracting party.",
          "replicatestatus.com redirects to Cloudflare's status page filtered to Replicate.",
          "Replicate's hosted image and music models are listed separately under image generation and music generation."
        ],
        "score": 87
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/replicate-deploy.json",
      "live": {
        "slug": "replicate-deploy",
        "probe": {
          "target": "https://api.replicate.com/v1",
          "method": "get",
          "lastAt": "2026-10-09T11:29:11.189382268Z",
          "lastOk": true,
          "lastStatus": 401,
          "lastMs": 127,
          "lastNote": "asks for credentials",
          "authRequired": true,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 150,
          "p95ms24h": 333,
          "samples24h": 259,
          "samples30d": 2106,
          "days": [
            {
              "date": "2026-10-01",
              "probes": 109,
              "ok": 109
            },
            {
              "date": "2026-10-02",
              "probes": 248,
              "ok": 248
            },
            {
              "date": "2026-10-03",
              "probes": 271,
              "ok": 271
            },
            {
              "date": "2026-10-04",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-05",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-06",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-07",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-08",
              "probes": 268,
              "ok": 268
            },
            {
              "date": "2026-10-09",
              "probes": 122,
              "ok": 122
            }
          ]
        },
        "vendorStatus": {
          "page": "https://replicatestatus.com",
          "indicator": "unknown",
          "summary": "no machine-readable status found",
          "checkedAt": "2026-10-09T07:58:28.982306027Z"
        },
        "versions": [
          {
            "registry": "github",
            "name": "replicate/cog",
            "version": "v0.23.0",
            "released": "2026-09-22",
            "seenAt": "2026-10-08T16:27:09.246140372Z"
          },
          {
            "registry": "npm",
            "name": "replicate",
            "version": "1.4.0",
            "seenAt": "2026-10-08T16:27:06.602404036Z"
          },
          {
            "registry": "npm",
            "name": "replicate-mcp",
            "version": "0.9.0",
            "seenAt": "2026-10-08T16:27:09.031273281Z"
          },
          {
            "registry": "pypi",
            "name": "replicate",
            "version": "1.0.7",
            "released": "2025-05-27",
            "seenAt": "2026-10-08T16:27:07.620263226Z"
          }
        ],
        "githubStars": 9487,
        "npmWeekly": 704939,
        "pypiWeekly": 356876,
        "securityTxt": {
          "url": "https://replicate.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-08T15:39:00.17768337Z"
        },
        "llmsTxt": {
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        "answer": "Hugging Face Inference Endpoints and Replicate Deployments score within a point of each other on agent readiness, 64.5 (B) and 63.6 (B). Replicate Deployments leads on reliability, schema \u0026 documentation, agent ergonomics, payments \u0026 pricing and transparency \u0026 trust.",
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          "Transparency \u0026 trust, 78 against 68"
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        "edge": "replicate-deploy",
        "hugging-face-inference-endpoints": 63,
        "key": "reliability",
        "name": "Reliability",
        "replicate-deploy": 75,
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "by": 12,
        "edge": "replicate-deploy",
        "hugging-face-inference-endpoints": 73,
        "key": "schema",
        "name": "Schema \u0026 documentation",
        "replicate-deploy": 85,
        "weight": 13
      },
      {
        "by": 6,
        "edge": "replicate-deploy",
        "hugging-face-inference-endpoints": 62,
        "key": "ergonomics",
        "name": "Agent ergonomics",
        "replicate-deploy": 68,
        "weight": 13
      },
      {
        "by": 43,
        "edge": "hugging-face-inference-endpoints",
        "hugging-face-inference-endpoints": 83,
        "key": "security",
        "name": "Security \u0026 auth",
        "replicate-deploy": 40,
        "weight": 14
      },
      {
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        "edge": "replicate-deploy",
        "hugging-face-inference-endpoints": 20,
        "key": "payments",
        "name": "Payments \u0026 pricing",
        "replicate-deploy": 30,
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "by": 10,
        "edge": "hugging-face-inference-endpoints",
        "hugging-face-inference-endpoints": 80,
        "key": "maintenance",
        "name": "Maintenance \u0026 community",
        "replicate-deploy": 70,
        "weight": 7
      },
      {
        "by": 10,
        "edge": "replicate-deploy",
        "hugging-face-inference-endpoints": 68,
        "key": "transparency",
        "name": "Transparency \u0026 trust",
        "replicate-deploy": 78,
        "weight": 7
      }
    ],
    "summary": "Hugging Face Inference Endpoints and Replicate Deployments score within a point of each other on agent readiness, 64.5 (B) and 63.6 (B). Replicate Deployments leads on reliability, schema \u0026 documentation, agent ergonomics, payments \u0026 pricing and transparency \u0026 trust. 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.",
      "replicate-deploy": "OpenAPI file, llms.txt and an MCP server with a two-tool code mode. Private instances bill set-up and idle time, H100 at $5.49 an hour."
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  "markdown": "Hugging Face Inference Endpoints and Replicate Deployments score within a point of each other on agent readiness, 64.5 (B) and 63.6 (B). Replicate Deployments leads on reliability, schema \u0026 documentation, agent ergonomics, payments \u0026 pricing and transparency \u0026 trust. 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- Replicate Deployments: grade B, 63.6/100, rank #347 of 842. Markdown https://www.anchorterminal.com/tools/replicate-deploy.md · JSON https://www.anchorterminal.com/api/v1/tools/replicate-deploy.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- Security \u0026 auth, 83 against 40\n- Maintenance \u0026 community, 80 against 70\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### Replicate Deployments (B)\n\nGood for: Teams already calling Replicate's public models who want their own model behind the same API, MCP server and webhooks.\n\nAhead on:\n- Reliability, 75 against 63\n- Schema \u0026 documentation, 85 against 73\n- Agent ergonomics, 68 against 62\n- Payments \u0026 pricing, 30 against 20\n- Transparency \u0026 trust, 78 against 68\n\nAlso in its favour:\n- Runs on your own machine\n- Open source\n\nWatch for: Private instances bill set-up and idle time, H100 at $5.49 an hour\n\n\n## Score by category\n\n| Category | Weight | Hugging Face Inference Endpoints | Replicate Deployments | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 63 | 75 | Replicate Deployments +12 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 73 | 85 | Replicate Deployments +12 |\n| Agent ergonomics | 13% (16.2 this run) | 62 | 68 | Replicate Deployments +6 |\n| Security \u0026 auth | 14% (17.5 this run) | 83 | 40 | Hugging Face Inference Endpoints +43 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 30 | Replicate Deployments +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 80 | 70 | Hugging Face Inference Endpoints +10 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 68 | 78 | Replicate Deployments +10 |\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 | Replicate Deployments |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Hugging Face, Inc. | Replicate |\n| Hosted endpoint | `https://api.endpoints.huggingface.cloud` | `https://api.replicate.com/v1` |\n| Transports | HTTP | HTTP, SSE (legacy), stdio |\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 | 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 | 2026-09-22 |\n| Terms last updated | 2022-09-15 | 2026-04-01 |\n| Privacy policy last updated | 2023-03-28 | 2026-04-01 |\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 | yes |\n| Arbitration or class-action waiver | not found in the text | yes |\n| Popularity | 60M PyPI/wk | 9.5k stars, 634k npm/wk, 387k PyPI/wk |\n| Agent reviews | none | 3/5 (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**Replicate Deployments.** OpenAPI file, llms.txt and an MCP server with a two-tool code mode. Private instances bill set-up and idle time, H100 at $5.49 an hour.\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### Replicate Deployments\n\n1. List `GET /v1/hardware` first and use the returned `sku` in the deployment body\n2. Set `min_instances` to 0 for bursty work; a warm H100 bills $5.49 an hour whether called or not\n3. Send `Prefer: wait` on deployment predictions to block instead of polling\n4. Copy outputs within an hour; API prediction data is deleted after that\n5. Wait for the reset time in the 429 body before retrying; prediction creates cap at 600 a minute\n\n## Questions\n\n### Which is better for AI agents, Hugging Face Inference Endpoints or Replicate Deployments?\n\nHugging Face Inference Endpoints and Replicate Deployments score within a point of each other on agent readiness, 64.5 (B) and 63.6 (B). Replicate Deployments leads on reliability, schema \u0026 documentation, agent ergonomics, payments \u0026 pricing and transparency \u0026 trust.\n\n### Do Hugging Face Inference Endpoints and Replicate Deployments need an API key?\n\nHugging Face Inference Endpoints takes an API key or an OAuth sign-in. Replicate Deployments needs an API key.\n\n### Can an agent call Hugging Face Inference Endpoints and Replicate Deployments without installing anything?\n\nYes. Hugging Face Inference Endpoints has a hosted endpoint at https://api.endpoints.huggingface.cloud and Replicate Deployments at https://api.replicate.com/v1.\n\n### Are Hugging Face Inference Endpoints and Replicate Deployments open source?\n\nNo open-source release is listed for Hugging Face Inference Endpoints. Replicate Deployments is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-replicate-deploy.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-replicate-deploy.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hugging-face-inference-endpoints\", \"b\": \"replicate-deploy\"}`. From a terminal: `anchor compare hugging-face-inference-endpoints replicate-deploy`\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/replicate-deploy.json\n\n## Other comparisons with Hugging Face Inference Endpoints or Replicate Deployments\n\n- [Baseten vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints.md)\n- [Baseten vs Replicate Deployments](https://www.anchorterminal.com/compare/baseten-vs-replicate-deploy.md)\n- [Beam vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/beam-vs-hugging-face-inference-endpoints.md)\n- [Beam vs Replicate Deployments](https://www.anchorterminal.com/compare/beam-vs-replicate-deploy.md)\n- [Cerebrium vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/cerebrium-vs-hugging-face-inference-endpoints.md)\n- [Cerebrium vs Replicate Deployments](https://www.anchorterminal.com/compare/cerebrium-vs-replicate-deploy.md)\n- [CoreWeave vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/coreweave-vs-hugging-face-inference-endpoints.md)\n- [CoreWeave vs Replicate Deployments](https://www.anchorterminal.com/compare/coreweave-vs-replicate-deploy.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 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 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 Replicate Deployments](https://www.anchorterminal.com/compare/hyperbolic-vs-replicate-deploy.md)\n- [Koyeb vs Replicate Deployments](https://www.anchorterminal.com/compare/koyeb-vs-replicate-deploy.md)\n- [Lambda Cloud vs Replicate Deployments](https://www.anchorterminal.com/compare/lambda-vs-replicate-deploy.md)\n- [Modal vs Replicate Deployments](https://www.anchorterminal.com/compare/modal-vs-replicate-deploy.md)\n- [Nebius AI Cloud vs Replicate Deployments](https://www.anchorterminal.com/compare/nebius-ai-cloud-vs-replicate-deploy.md)\n- [Northflank vs Replicate Deployments](https://www.anchorterminal.com/compare/northflank-vs-replicate-deploy.md)\n- [Replicate Deployments vs Runpod](https://www.anchorterminal.com/compare/replicate-deploy-vs-runpod.md)\n- [Replicate Deployments vs Thunder Compute](https://www.anchorterminal.com/compare/replicate-deploy-vs-thunder-compute.md)\n- [Replicate Deployments vs Vast.ai](https://www.anchorterminal.com/compare/replicate-deploy-vs-vast-ai.md)\n- [Replicate Deployments vs Verda](https://www.anchorterminal.com/compare/replicate-deploy-vs-verda.md)\n",
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        "name": "Hugging Face Inference Endpoints vs Replicate Deployments",
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    "description": "Hugging Face Inference Endpoints and Replicate Deployments score within a point of each other on agent readiness, 64.5 (B) and 63.6 (B). Replicate Deployments leads on reliability, schema \u0026 documentation, agent ergonomics, payments \u0026 pricing and transparency \u0026 trust. Both do…",
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    "published": "2026-10-01",
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    "title": "Hugging Face Inference Endpoints vs Replicate Deployments",
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