{ "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", "dns": "hugging-face-inference-endpoints.grade.anchorterminal.com", "repo": "https://github.com/huggingface/hf-endpoints-documentation", "license": "Proprietary service under the Hugging Face Terms of Service. The `huggingface_hub` Python client and CLI are Apache-2.0", "transports": [ "http" ], "remoteUrl": "https://api.endpoints.huggingface.cloud", "packages": [ { "registry": "pypi", "name": "huggingface_hub" } ], "auth": "mixed", "authNotes": "Hugging Face access token sent as `Authorization: Bearer $HF_TOKEN` to the management API and to each endpoint. Tokens are created in the account settings in a browser and can be fine-grained, read or write. The MCP server uses OAuth through huggingface.co (authorisation code with PKCE, device code, dynamic client registration) with `read-endpoints` and `write-endpoints` scopes. `GET /v2/provider` and the catalogue list need no token. Access is self-serve, with quota requests for larger instances.", "pricing": "usage", "pricingNotes": "Usage priced by instance hour, billed per minute while a replica is initialising or running. GPUs run from $0.50 an hour (T4) to $10 (H100 on GCP), CPUs from $0.033. No free tier or trial was found. The docs require a payment method and credits before deploying, and the pricing page says an active subscription. Paused endpoints and endpoints at zero replicas aren't billed for compute (https://huggingface.co/docs/inference-endpoints/support/pricing).", "priceSummary": "$0.033 / vCPU-hr", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the Inference Endpoints docs, the two OpenAPI documents or the pricing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": 19, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": 60014944, "asOf": "2026-10-08" }, "docsUrl": "https://huggingface.co/docs/inference-endpoints/index", "llmsTxt": "https://huggingface.co/docs/inference-endpoints/llms.txt", "openapi": "https://api.endpoints.huggingface.cloud/openapi.json", "capabilities": [ "compute.gpu", "compute.endpoints", "compute.containers" ], "tags": [ "hosted", "usage-priced", "python", "cli", "mcp", "oauth", "openapi", "llms-txt", "status-page", "soc2", "enterprise" ], "lastRelease": "2026-10-29", "graded": true, "anchor": { "graded": true, "score": 66.7, "grade": "B", "agentReady": false, "rank": 358, "ranked": true, "rankOf": 1510, "categoryRank": 3, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 62, "maintenance": 80, "payments": 20, "reliability": 73, "schema": 73, "security": 83, "transparency": 70 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-02" }, "negative": 0, "verdict": "OAuth scopes separate reading endpoints from writing them, both OpenAPI documents are public, and the unauthenticated `/v2/provider` route lists every instance with its hourly price. An account needs a payment method and credits before the first deployment, no rate limits or SLA were found for the management API, and the docs price table disagrees with the live list in places.", "bestFor": "Teams whose models already live on the Hugging Face Hub and who want a dedicated endpoint on a named cloud and region with standard open-source engines.", "strengths": [ "Public OpenAPI 3.1 documents for the management API (46 operations) and the catalogue API (3), plus llms.txt and a Markdown twin of every docs page", "The MCP server at endpoints.huggingface.co/mcp uses OAuth with `read-endpoints` and `write-endpoints` scopes, PKCE and dynamic client registration", "`GET /v2/provider` needs no token and returns each instance type by cloud and region with status and price per hour", "The MCP `delete_endpoint` tool returns a preview and deletes only on a second call with `confirm: true`", "The Inference Endpoints UI and API components on status.huggingface.co show 100 per cent uptime over the 90 days to 2 November 2026" ], "weaknesses": [ "No free tier. The docs require a payment method and credits, and replicas are billed while initialising as well as running", "No rate limits, SLA or idempotency keys were found for the management API, and its OpenAPI document lists only 200 responses on 45 of 48 operations", "The docs price table and the live provider list disagree. Inferentia2 x1 is $0.75 in the docs and $1.95 in the API, and AWS H200 is listed in the docs and marked deprecated in the API", "A start from zero replicas takes minutes by the docs' own account, and the proxy answers 503 until a replica is ready", "No bug bounty was found. The security programme rests on a valid security.txt and a SOC2 Type 2 statement" ], "agentNotes": [ "Call `GET https://api.endpoints.huggingface.cloud/v2/provider` first and pick an instance whose `status` is `available`. The docs table lists types the API marks deprecated or not available", "Send `X-Scale-Up-Timeout: 600` on requests to an endpoint that scales to zero, or handle 503 while the first replica starts", "Set `scaleToZeroTimeout` yourself. The docs give a default of 1 hour and the OpenAPI document says 15 minutes", "Pause or delete an endpoint when the job is done. Billing covers every minute a replica is initialising or running", "Give the agent a fine-grained token or the `read-endpoints` scope unless it must deploy. Endpoints are private by default and take the same Hugging Face token as a bearer" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 66.7 } ], "editorialScores": { "ergonomics": 62, "maintenance": 80, "payments": 20, "reliability": 73, "schema": 73, "security": 83, "transparency": 68 }, "provenanceScore": 71, "checked": { "categories": { "ergonomics": "2026-11-02", "maintenance": "2026-11-02", "payments": "2026-11-02", "reliability": "2026-11-02", "schema": "2026-11-02", "security": "2026-11-02", "transparency": "2026-11-02" }, "last": "2026-11-02", "lastMoved": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install huggingface_hub", "http": "curl \"https://api.endpoints.huggingface.cloud/v2/endpoint/$NAMESPACE\" \\\n -H \"Authorization: Bearer $HF_TOKEN\"" }, "letme": { "capability": "https://letme.dev/compute.gpu", "tool": "https://letme.dev/hugging-face-inference-endpoints" }, "sameCompany": [ "hugging-face-inference-providers", "smolagents", "carbon-x", "smollm3-3b" ], "area": "models", "unitPrices": [ { "item": "NVIDIA T4 16 GB x1 (AWS, GCP)", "unit": "gpu-hour", "usd": 0.5, "note": "Billed per minute while initialising or running" }, { "item": "NVIDIA L4 24 GB x1 (AWS)", "unit": "gpu-hour", "usd": 0.8, "note": "$0.70 on GCP us-east4" }, { "item": "NVIDIA A10G 24 GB x1 (AWS)", "unit": "gpu-hour", "usd": 1, "note": "us-east-1 and eu-west-1" }, { "item": "NVIDIA L40S 48 GB x1 (AWS)", "unit": "gpu-hour", "usd": 1.8, "note": "us-east-1" }, { "item": "NVIDIA A100 80 GB x1 (AWS)", "unit": "gpu-hour", "usd": 2.5, "note": "$3.60 on GCP us-east4" }, { "item": "NVIDIA RTX PRO 6000 Blackwell 96 GB x1 (AWS)", "unit": "gpu-hour", "usd": 2.75, "note": "us-east-2, in the live provider list and absent from the docs table" }, { "item": "NVIDIA H200 141 GB x1 (GCP)", "unit": "gpu-hour", "usd": 5, "note": "us-south1. The AWS H200 in us-west-2 is marked deprecated in the API" }, { "item": "NVIDIA H100 80 GB x1 (GCP)", "unit": "gpu-hour", "usd": 10, "note": "us-east4. The AWS H100 at $4.50 is deprecated from December 2025" }, { "item": "Intel Sapphire Rapids x1, 1 vCPU and 2 GB (AWS)", "unit": "vcpu-hour", "usd": 0.033, "note": "$0.050 on GCP and $0.060 on Azure Intel Xeon" } ], "provenance": { "legalEntity": "Hugging Face, Inc.", "domain": "huggingface.co", "domainRegistered": "", "endpointOnVendorDomain": false, "terms": "https://huggingface.co/terms-of-service", "privacy": "https://huggingface.co/privacy", "statusPage": "https://status.huggingface.co", "changelog": "https://huggingface.co/changelog", "securityTxt": "valid", "checked": "2026-10-08", "notes": [ "The Terms of Service (effective 15 September 2022) name Hugging Face, Inc., a Delaware corporation, list Inference Endpoints among the services they cover and are governed by New York law. They link Supplemental Terms (effective 28 April 2025) as a PDF, of which our reader extracted only the first page.", "The privacy policy (effective 28 March 2023) names Hugging Face, Inc. and its EU establishment Hugging Face, SAS, 9 rue des Colonnes, 75002 Paris, and lists 11 subprocessors with countries. The Inference Endpoints security page points to it.", "The management API answers at api.endpoints.huggingface.cloud and deployed endpoints at subdomains of endpoints.huggingface.cloud, a second domain of the vendor's. The catalogue API and the MCP server are on endpoints.huggingface.co.", "huggingface.co/.well-known/security.txt gives security@huggingface.co and expires on 1 July 2030. endpoints.huggingface.co/.well-known/security.txt returns 404.", "status.huggingface.co is a Better Stack page with separate components for the Inference Endpoints UI and API.", "The changelog at huggingface.co/changelog covers the whole Hub. Inference Endpoints has no changelog of its own. Dated changes are in the docs repository's commit history.", "rdap.org returned 404 for huggingface.co, so the registration date is unrecorded." ], "score": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-endpoints.json", "live": { "slug": "hugging-face-inference-endpoints", "probe": { "target": "https://api.endpoints.huggingface.cloud", "method": "get", "lastAt": "2026-11-07T19:22:12.471275497Z", "lastOk": true, "lastStatus": 200, "lastMs": 268, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 265, "p95ms24h": 377, "samples24h": 238, "samples30d": 7299, "days": [ { "date": "2026-10-09", "probes": 169, "ok": 169 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 252 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 253 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 251, "ok": 251 }, { "date": "2026-10-29", "probes": 248, "ok": 248 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "vendorStatus": { "page": "https://status.huggingface.co", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-07T04:47:55.534335358Z" }, "versions": [ { "registry": "pypi", "name": "huggingface_hub", "version": "2.4.0", "released": "2026-10-29", "seenAt": "2026-11-06T23:33:23.580931242Z" } ], "githubStars": 27, "pypiWeekly": 71518708, "securityTxt": { "url": "https://huggingface.co/.well-known/security.txt", "state": "valid", "expires": "2030-07-01T08:42:00.000Z", "checkedAt": "2026-11-07T16:00:00.493446773Z" }, "llmsTxt": { "url": "https://huggingface.co/docs/inference-endpoints/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:42.888635563Z" }, "domain": { "domain": "huggingface.co", "checkedAt": "2026-11-01T14:09:00.477589213Z" }, "pages": [ { "url": "https://huggingface.co/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-11-07T14:11:29.170806608Z", "changedAt": "2026-11-07T14:11:29.170806608Z", "fingerprint": "4d3a63d4f46e" }, { "url": "https://huggingface.co/docs/inference-endpoints/support/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-07T14:11:35.529061385Z", "changedAt": "2026-11-07T14:11:35.529061385Z", "fingerprint": "0f0a46ee769e" }, { "url": "https://huggingface.co/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:12:45.345654967Z", "changedAt": "2026-11-07T14:12:45.345654967Z", "fingerprint": "791511d09ba8" }, { "url": "https://huggingface.co/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:12:49.455239893Z", "changedAt": "2026-11-07T14:12:49.455239893Z", "fingerprint": "40e6dc5ca2a4" } ], "github": { "repo": "huggingface/hf-endpoints-documentation", "checkedAt": "2026-11-02T08:55:12.143502376Z", "stars": 27, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-29T12:50:44Z", "openIssues": 0, "openPRs": 3, "issuesOpened90d": 0, "issuesClosed90d": 0, "prsMerged90d": 11, "ci": { "branch": "main", "runs": 30, "success": 30, "failure": 0, "other": 0, "latest": "success", "latestAt": "2026-10-23T09:21:14Z" }, "replies": { "sampled": 8, "unanswered": 0, "medianDays": 49.5 } }, "updatedAt": "2026-11-07T19:22:12.471275497Z" } }, "answer": "Hugging Face Inference Endpoints scores 66.7 (B) on agent readiness against Together GPU Clusters's 58.1 (C), and leads in 5 of 7 scored categories. Together GPU Clusters leads on schema \u0026 documentation.", "b": { "slug": "together-gpu-clusters", "name": "Together GPU Clusters", "vendor": "Together AI", "vendorUrl": "https://www.together.ai", "kind": "http-api", "category": "gpu-compute", "summary": "Together GPU Clusters rents multi-node NVIDIA H100, H200, B200 and B300 clusters with InfiniBand and shared storage, run as Kubernetes or Slurm, by the GPU-hour. Clusters are created and scaled from the console, the tg CLI, SDKs or REST API.", "url": "https://www.anchorterminal.com/tools/together-gpu-clusters", "markdownUrl": "https://www.anchorterminal.com/tools/together-gpu-clusters.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/together-gpu-clusters.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/together-gpu-clusters.json", "dns": "together-gpu-clusters.grade.anchorterminal.com", "repo": "https://github.com/togethercomputer/together-py", "license": "Proprietary service under Together's terms of service. The Python SDK and CLI are Apache-2.0 and the OpenAPI document is MIT", "transports": [], "remoteUrl": "https://api.together.ai/v1/compute/clusters", "packages": [ { "registry": "pypi", "name": "together" }, { "registry": "npm", "name": "together-ai" } ], "auth": "api-key", "authNotes": "Bearer `TOGETHER_API_KEY`, scoped to a project, revocable and optionally expiring, the same keys as Together's other APIs. Only a project admin's key returns the cluster's admin kubeconfig. Team members can reach the Kubernetes API through OIDC with their own identity provider, and SSH uses OIDC or uploaded keys.", "pricing": "usage", "pricingNotes": "On-demand from $3.99 per GPU-hour (H100) to $9.99 (B300); reserved 3 to 180 days from $3.19 to $7.99; preemptible (preview) from $1.99. Storage per GiB-month. No ingress or egress fees. Reservations are prepaid and non-refundable, and creation needs enough credit to cover them.", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the GPU clusters docs, billing pages or OpenAPI document (checked 2026-11-06). An exhausted balance returns 402 to scale requests with no payment requirements.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-06" }, "docsUrl": "https://docs.together.ai/docs/gpu-clusters-overview", "llmsTxt": "https://docs.together.ai/llms.txt", "openapi": "https://github.com/togethercomputer/openapi", "capabilities": [ "compute.gpu", "compute.containers", "compute.batch" ], "tags": [ "hosted", "official", "usage", "prepaid", "openapi", "llms-txt", "kubernetes", "slurm", "python", "typescript", "cli", "h100", "b200" ], "lastRelease": "2026-10-22", "graded": true, "anchor": { "graded": true, "score": 58.1, "grade": "C", "agentReady": false, "rank": 768, "ranked": true, "rankOf": 1510, "categoryRank": 10, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 55, "maintenance": 75, "payments": 20, "reliability": 38, "schema": 92, "security": 68, "transparency": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": 0, "verdict": "Multi-node H100 to B300 clusters on Kubernetes or Slurm, created and deleted headlessly through an OpenAPI-described API, a CLI with JSON output and two SDKs, at published GPU-hour prices. Clusters take 10 minutes to an hour to provision, can't be paused, and have no status component, SLA or idempotent create.", "bestFor": "Training, fine-tuning and batch jobs that need several 8-GPU nodes with InfiniBand, run by a pipeline that creates and deletes clusters.", "strengths": [ "Public OpenAPI 3.1, llms.txt and Markdown docs, with an automation guide for CI pipelines", "Published per GPU-hour prices for on-demand, reserved and preemptible capacity, with no egress fees", "Kubernetes or Slurm on the same hardware, with InfiniBand and a persistent shared volume", "Automatic health checks and node repair, approved by hand or run automatically", "Project-scoped keys, admin-only kubeconfig and OIDC access for team members" ], "weaknesses": [ "Provisioning takes 10 minutes to an hour, and a cluster can't be paused or scaled to zero", "No status page component, SLA or published rate limits for GPU clusters", "No idempotency key on creation, so a retried create can bill a second cluster", "No free trial; reservations are prepaid and non-refundable", "Volumes bill until deleted and outlive the clusters they were attached to" ], "agentNotes": [ "Use `tg clusters`, not `tg beta clusters`, which stopped working with CLI 3.0 on 22 October 2026", "Pass `--json` and `--non-interactive`, poll `tg clusters retrieve` until status is Ready, and delete the cluster in a cleanup step", "Check for an existing cluster by name before retrying a create, since creation has no idempotency key", "Read live availability from the regions API and prices from the pricing page; the regions response has no prices", "Use a project admin's key when the task needs the kubeconfig, since other roles get none" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58.1 } ], "editorialScores": { "ergonomics": 55, "maintenance": 75, "payments": 20, "reliability": 38, "schema": 92, "security": 68, "transparency": 46 }, "provenanceScore": 83, "checked": { "categories": { "ergonomics": "2026-11-06", "maintenance": "2026-11-06", "payments": "2026-11-06", "reliability": "2026-11-06", "schema": "2026-11-06", "security": "2026-11-06", "transparency": "2026-11-06" }, "last": "2026-11-06", "oldest": "2026-11-06", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "uv tool install \"together[cli]\"", "http": "curl -H \"Authorization: Bearer $TOGETHER_API_KEY\" https://api.together.ai/v1/compute/clusters" }, "letme": { "capability": "https://letme.dev/compute.gpu", "tool": "https://letme.dev/together-gpu-clusters" }, "sameCompany": [ "together-inference", "together-fine-tuning", "together-code-sandbox" ], "area": "models", "unitPrices": [ { "item": "H100 on-demand", "unit": "gpu-hour", "usd": 3.99, "note": "reserved $3.69 to $3.19, preemptible $1.99" }, { "item": "H200 on-demand", "unit": "gpu-hour", "usd": 5.99, "note": "reserved $4.99 to $3.99, preemptible $2.99" }, { "item": "B200 on-demand", "unit": "gpu-hour", "usd": 8.19, "note": "reserved $7.99 to $6.79, preemptible $4.09" }, { "item": "B300 on-demand", "unit": "gpu-hour", "usd": 9.99, "note": "preemptible $4.99, reserved by sales" } ], "provenance": { "legalEntity": "Together Computer, Inc.", "domain": "together.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://www.together.ai/terms-of-service", "privacy": "https://www.together.ai/privacy", "statusPage": "https://status.together.ai", "changelog": "https://docs.together.ai/docs/changelog", "securityTxt": "valid", "checked": "2026-11-06", "notes": [ "Legal entity, terms, privacy and security.txt as on the Together AI Inference listing, checked 1 November 2026, and not re-read for this listing.", "docs.together.ai/robots.txt disallows only /cdn-cgi/ and /_next/, with Content-Signal ai-train=yes, search=yes, ai-input=yes. www.together.ai/robots.txt allows all.", "status.together.ai has no component for GPU clusters on 6 November 2026." ], "score": 83 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/together-gpu-clusters.json", "live": { "slug": "together-gpu-clusters", "probe": { "target": "https://api.together.ai/v1/compute/clusters", "method": "get", "lastAt": "2026-11-07T19:22:40.660879578Z", "lastOk": true, "lastStatus": 401, "lastMs": 263, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 493, "p95ms24h": 857, "samples24h": 210, "samples30d": 210, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 13 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "versions": [ { "registry": "github", "name": "togethercomputer/together-py", "version": "v3.1.1", "released": "2026-11-04", "seenAt": "2026-11-07T00:12:15.100983119Z" }, { "registry": "npm", "name": "together-ai", "version": "1.0.0", "seenAt": "2026-11-07T00:12:15.067840103Z" }, { "registry": "pypi", "name": "together", "version": "3.1.1", "released": "2026-11-04", "seenAt": "2026-11-07T00:12:14.921893617Z" } ], "githubStars": 12, "npmWeekly": 99619, "pypiWeekly": 424792, "securityTxt": { "url": "https://together.ai/.well-known/security.txt", "state": "valid", "expires": "2028-09-30T00:00:00.000Z", "checkedAt": "2026-11-07T16:00:47.073630606Z" }, "llmsTxt": { "url": "https://docs.together.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:34:12.138384896Z" }, "updatedAt": "2026-11-07T19:22:40.660879578Z" } }, "edges": [ { "by": 35, "category": "reliability", "leads": "hugging-face-inference-endpoints", "name": "Reliability", "reasons": { "a": "Better Stack status page at status.huggingface.co with separate Inference Endpoints UI and API components and 90 days of history (20). On 2 November 2026 both components show 100 per cent uptime, with one scheduled maintenance window for H200 on 7 October 2026. The page posts incidents for other services in the window (GPU Jobs and Spaces failing to start on 24 and 25 September), and none touches Inference Endpoints since the UI outage of 16 July left the 90 days, so a clean record (30). No rate limits were found for the management API. The Hub publishes limits per 5-minute window (1,000 API requests for a free user), without saying they cover api.endpoints.huggingface.cloud (5). The docs explain the 503 returned while a replica starts and the `X-Scale-Up-Timeout` header that holds the request, and advise 2 replicas for availability. No backoff guidance or idempotency keys for writes (8). No SLA found in the docs or on the pricing page (0). The service is generally available (10).", "b": "Read with the hosted lines. status.together.ai lists the website, the playground and serverless models with no component for GPU clusters, and a vendor page with no component for the product takes 15 in this run, as for Together's Code Sandbox and fine-tuning listings (15). No incident history for clusters can be read (5). No rate limits for the clusters API found (0). Together's docs give 429 and 503 backoff guidance for its APIs, per the inference listing's 1 November check, and the CLI has `--non-interactive` and `--json` for scripted retries, but creation has no idempotency key (8). No SLA for clusters found; the published 99% commitment covers provisioned inference throughput only (0). Generally available since 9 September 2025 and out of the CLI's beta paths since 22 October 2026, with preemptible nodes in preview (10). 15+5+0+8+0+10 = 38." }, "scores": { "a": 73, "b": 38 } }, { "by": 19, "category": "schema", "leads": "together-gpu-clusters", "name": "Schema \u0026 documentation", "reasons": { "a": "Public OpenAPI 3.1 documents for the management API (42 paths, 48 operations on 2 November 2026) and the catalogue API (3 operations, per the 8 October check) (25). llms.txt with a Markdown twin of every docs page (10). Every operation has a summary tagged READ, WRITE, PUBLIC or PRO, but only 7 of 48 have a description. The MCP tool table states each tool's purpose and says to call `get_recommended_config` before `create_endpoint` (11). 131 typed schemas with required fields and enums for state, type and accelerator. List filters are comma-separated strings (12). Parameters carry examples. The management document lists only 200 responses on 45 of 48 operations, plus 501 on three log routes, while the catalogue document lists 400, 401, 404, 409 and 500 (7). Paths are versioned `/v2` and `/v3` and the document is version 2.0.0. Inference Endpoints has no changelog of its own. The Hub changelog and the docs repository history are the dated record (8).", "b": "Public OpenAPI 3.1 for /v1/compute/clusters and shared volumes, MIT, embedded in every reference page (25). llms.txt and a Markdown twin of every page (10). The docs say when to choose Kubernetes or Slurm and each capacity type, and the management page carries instructions for agents on reporting live availability and prices (18). Enums for cluster type, billing type, GPU type and status, required fields marked (13). CLI, Python, TypeScript and console steps on each task, with 402 and 403 cases documented, though no error catalogue for the clusters API (11). Versioned SDKs and a dated changelog with cluster entries (15). 25+10+18+13+11+15 = 92." }, "scores": { "a": 73, "b": 92 } }, { "by": 15, "category": "security", "leads": "hugging-face-inference-endpoints", "name": "Security \u0026 auth", "reasons": { "a": "OAuth through huggingface.co with `read-endpoints` and `write-endpoints` scopes, PKCE and dynamic client registration for the MCP server, and revocable fine-grained access tokens for the API. No documented option to send a token in a URL (30). Read and write scopes are separate, endpoints are private by default, organisations on Team and Enterprise plans can require approval of fine-grained tokens, and the MCP delete tool needs `confirm: true`. Other write calls have no confirmation step (17). The service returns the output and logs of the owner's own model, not third-party content (10). An audit log route and MCP tool on Pro, Team or Enterprise plans, plus logs and metrics for every endpoint (12). security.txt valid to 2030 with security@huggingface.co, SOC2 Type 2 stated for the Hub and Inference Endpoints, malware and pickle scanning of repositories. No bug bounty was found (14).", "b": "Project-scoped API keys, revocable with optional expiry, read as Together's other listings read the same key model (30). Admin and editor roles with no read-only role. Only admins get the admin kubeconfig, which has cluster-admin rights, and OIDC lets editors use kubectl under RBAC. Deletes run without confirmation when `--non-interactive` is passed (8). The API returns cluster state, not third-party content (10). Invoices itemise reserved, on-demand and storage charges, and no audit log for cluster operations was found (5). security.txt with a HackerOne route, per the 1 November check of the inference listing (15). 30+8+10+5+15 = 68." }, "scores": { "a": 83, "b": 68 } }, { "by": 7, "category": "ergonomics", "leads": "hugging-face-inference-endpoints", "name": "Agent ergonomics", "reasons": { "a": "List endpoints takes `limit` (default 20) and `cursor`, logs take `limit`, `tail` and `line_max_length`, and the MCP server has 19 tools (18). Cursor pagination, filters by state, type, task and tags, sorting, and a time window on logs (18). Errors are documented for the catalogue API and in prose for pause, resume and scale to zero. The management document has no error schema (8). No idempotency keys. Endpoints are addressed by name, updates are a PUT, and the MCP delete tool previews before a confirmed second call. MCP annotations weren't read (8). One-call deploy from the catalogue with tuned defaults, a Python client and the `hf endpoints` CLI. A second-language SDK for endpoint management wasn't found (10).", "b": "Cluster objects are compact, and `--json` gives machine-readable output, but list calls take no field selection (15). No pagination or filters on the clusters list (8). 402 and 403 with readable messages such as \"Insufficient credits for cluster creation\", and troubleshooting for common failures (12). Status polling until Ready is documented and deletes run non-interactively, but there is no idempotency key on creation and a duplicate create bills a second cluster (8). Official Python and TypeScript SDKs and a CLI, though a create takes GPU type, region, driver and CUDA versions, cluster type and billing type (12). 15+8+12+8+12 = 55." }, "scores": { "a": 62, "b": 55 } }, { "by": 5, "category": "maintenance", "leads": "hugging-face-inference-endpoints", "name": "Maintenance \u0026 community", "reasons": { "a": "`huggingface_hub` v2.4.0 was tagged on 29 October 2026, and the docs repository changed on 23 October (30). Tags v2.0.0 (24 September), v2.1.0, v2.1.1, v2.2.0, v2.3.0 (23 October) and v2.4.0 in the 90 days, and docs commits on 14 and 15 October adding the `inferenceLoad` scaling strategy and a daily autoscaling schedule (20). GitHub's pages refused our requests, so issue response times went unread. A public changelog for the Hub, a forum and a quota contact form exist (8 of 15). Current official Python client and CLI (15). The client supports Python 3.10 and later and ships weekly. Its CI wasn't read (7).", "b": "Closed service. The newest cluster change is CLI and SDK 3.0 on 22 October 2026, 15 days before this check (30). Dated cluster entries in the 90 days from 8 August include 12, 18, 19, 24, 27 and 28 August, 10 September and 22 October (20). Dated changelog, a support portal and Discord, with responsiveness not tested (8). Python SDK 3.1 and TypeScript SDK 1.0 are current and cover clusters (15). SDK CI and dependencies weren't audited (2). 30+20+8+15+2 = 75." }, "scores": { "a": 80, "b": 75 } }, { "by": 5, "category": "transparency", "leads": "hugging-face-inference-endpoints", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed service under clear terms that name Inference Endpoints, with an Apache-2.0 client (20). The security page says payloads and tokens aren't stored and logs are kept 30 days. The privacy policy dates from 28 March 2023 and gives retention as long as necessary, and a data processing agreement requires an Enterprise subscription (20). Instance deprecations are dated to the month in the pricing table and flagged in the provider API, and the TGI maintenance notice is dated. No deprecation policy for the API was found (10). The privacy policy lists 11 subprocessors with countries and the docs and API list deployment regions (18).", "b": "Closed service under Together's terms, with Apache-2.0 SDKs, which don't raise the line for a closed API (15). The privacy policy's data table covers GPU clusters, per the 4 November Code Sandbox check, and the billing page states when volumes are deleted after credit runs out, with no retention period for data on deleted clusters (15). The 3.0 breaking change came with a migration guide on the day, and reservations carry dated end-of-term emails, with no notice period for API changes found (8). Regions are listed through the API and US regions appear in examples; subprocessors and data locations for clusters were not established (8). 15+15+8+8 = 46." }, "scores": { "a": 70, "b": 65 } } ], "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Hugging Face, Inc.", "b": "Together AI", "name": "Vendor" }, { "a": "https://api.endpoints.huggingface.cloud", "b": "https://api.together.ai/v1/compute/clusters", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "", "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 Together's terms of service. The Python SDK and CLI are Apache-2.0 and the OpenAPI document 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": "2026-10-29", "b": "2026-10-22", "name": "Last release" }, { "a": "2022-09-15", "b": "no date given", "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": "not found in the text", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "yes", "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": "none", "name": "Popularity" }, { "a": "3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Hugging Face Inference Endpoints scores 66.7 (B) on agent readiness against Together GPU Clusters's 58.1 (C), and leads in 5 of 7 scored categories. Together GPU Clusters leads on schema \u0026 documentation.", "question": "Which is better for AI agents, Hugging Face Inference Endpoints or Together GPU Clusters?" }, { "answer": "Hugging Face Inference Endpoints takes an API key or an OAuth sign-in. Together GPU Clusters needs an API key.", "question": "Do Hugging Face Inference Endpoints and Together GPU Clusters need an API key?" }, { "answer": "Yes. Hugging Face Inference Endpoints has a hosted endpoint at https://api.endpoints.huggingface.cloud and Together GPU Clusters at https://api.together.ai/v1/compute/clusters.", "question": "Can an agent call Hugging Face Inference Endpoints and Together GPU Clusters without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 73 against 38", "Agent ergonomics, 62 against 55", "Security \u0026 auth, 83 against 68", "Maintenance \u0026 community, 80 against 75", "Transparency \u0026 trust, 70 against 65" ], "also": null, "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": [ "Schema \u0026 documentation, 92 against 73" ], "also": null, "goodFor": "Training, fine-tuning and batch jobs that need several 8-GPU nodes with InfiniBand, run by a pipeline that creates and deletes clusters.", "slug": "together-gpu-clusters", "watchFor": "Provisioning takes 10 minutes to an hour, and a cluster can't be paused or scaled to zero" } ], "history": { "hugging-face-inference-endpoints": [ { "causes": [ "time" ], "date": "2026-11-02", "delta": 2, "from": 64.7, "fromGrade": "B", "to": 66.7, "toGrade": "B" }, { "causes": [ "evidence" ], "date": "2026-10-27", "delta": 0.2, "from": 64.5, "fromGrade": "B", "to": 64.7, "toGrade": "B" } ], "together-gpu-clusters": null }, "job": { "capability": "compute.gpu", "name": "GPU compute" }, "others": [ { "json": "https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints.json", "title": 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"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-together-gpu-clusters.json", "title": "Hyperbolic vs Together GPU Clusters", "url": "https://www.anchorterminal.com/compare/hyperbolic-vs-together-gpu-clusters" }, { "json": "https://www.anchorterminal.com/compare/koyeb-vs-together-gpu-clusters.json", "title": "Koyeb vs Together GPU Clusters", "url": "https://www.anchorterminal.com/compare/koyeb-vs-together-gpu-clusters" }, { "json": "https://www.anchorterminal.com/compare/lambda-vs-together-gpu-clusters.json", "title": "Lambda Cloud vs Together GPU Clusters", "url": "https://www.anchorterminal.com/compare/lambda-vs-together-gpu-clusters" }, { "json": "https://www.anchorterminal.com/compare/massed-compute-vs-together-gpu-clusters.json", "title": "Massed Compute vs Together GPU Clusters", "url": "https://www.anchorterminal.com/compare/massed-compute-vs-together-gpu-clusters" }, { "json": "https://www.anchorterminal.com/compare/modal-vs-together-gpu-clusters.json", "title": "Modal vs Together GPU Clusters", "url": "https://www.anchorterminal.com/compare/modal-vs-together-gpu-clusters" }, { "json": "https://www.anchorterminal.com/compare/nebius-ai-cloud-vs-together-gpu-clusters.json", "title": "Nebius AI Cloud vs Together GPU Clusters", "url": "https://www.anchorterminal.com/compare/nebius-ai-cloud-vs-together-gpu-clusters" }, { "json": "https://www.anchorterminal.com/compare/northflank-vs-together-gpu-clusters.json", "title": "Northflank vs Together GPU Clusters", "url": "https://www.anchorterminal.com/compare/northflank-vs-together-gpu-clusters" }, { "json": "https://www.anchorterminal.com/compare/replicate-deploy-vs-together-gpu-clusters.json", "title": "Replicate Deployments vs Together GPU Clusters", "url": "https://www.anchorterminal.com/compare/replicate-deploy-vs-together-gpu-clusters" }, { "json": "https://www.anchorterminal.com/compare/runpod-vs-together-gpu-clusters.json", "title": "Runpod vs Together GPU Clusters", "url": "https://www.anchorterminal.com/compare/runpod-vs-together-gpu-clusters" }, { "json": "https://www.anchorterminal.com/compare/thunder-compute-vs-together-gpu-clusters.json", "title": "Thunder Compute vs Together GPU Clusters", "url": "https://www.anchorterminal.com/compare/thunder-compute-vs-together-gpu-clusters" }, { "json": "https://www.anchorterminal.com/compare/together-gpu-clusters-vs-vast-ai.json", "title": "Together GPU Clusters vs Vast.ai", "url": "https://www.anchorterminal.com/compare/together-gpu-clusters-vs-vast-ai" }, { "json": "https://www.anchorterminal.com/compare/together-gpu-clusters-vs-verda.json", "title": "Together GPU Clusters vs Verda", "url": "https://www.anchorterminal.com/compare/together-gpu-clusters-vs-verda" } ], "panel": null, "scores": [ { "by": 35, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 73, "key": "reliability", "name": "Reliability", "together-gpu-clusters": 38, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 19, "edge": "together-gpu-clusters", "hugging-face-inference-endpoints": 73, "key": "schema", "name": "Schema \u0026 documentation", "together-gpu-clusters": 92, "weight": 13 }, { "by": 7, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 62, "key": "ergonomics", "name": "Agent ergonomics", "together-gpu-clusters": 55, "weight": 13 }, { "by": 15, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 83, "key": "security", "name": "Security \u0026 auth", "together-gpu-clusters": 68, "weight": 14 }, { "by": 0, "edge": "", "hugging-face-inference-endpoints": 20, "key": "payments", "name": "Payments \u0026 pricing", "together-gpu-clusters": 20, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 5, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 80, "key": "maintenance", "name": "Maintenance \u0026 community", "together-gpu-clusters": 75, "weight": 7 }, { "by": 5, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 70, "key": "transparency", "name": "Transparency \u0026 trust", "together-gpu-clusters": 65, "weight": 7 } ], "summary": "Hugging Face Inference Endpoints scores 66.7 (B) on agent readiness against Together GPU Clusters's 58.1 (C), and leads in 5 of 7 scored categories. Together GPU Clusters leads on schema \u0026 documentation. Both do gpu compute.", "tasks": null, "tutorials": [], "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.", "together-gpu-clusters": "Multi-node H100 to B300 clusters on Kubernetes or Slurm, created and deleted headlessly through an OpenAPI-described API, a CLI with JSON output and two SDKs, at published GPU-hour prices. Clusters take 10 minutes to an hour to provision, can't be paused, and have no status component, SLA or idempotent create." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-together-gpu-clusters", "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-together-gpu-clusters.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-together-gpu-clusters.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-together-gpu-clusters.min.md" }, "markdown": "Hugging Face Inference Endpoints scores 66.7 (B) on agent readiness against Together GPU Clusters's 58.1 (C), and leads in 5 of 7 scored categories. Together GPU Clusters leads on schema \u0026 documentation. Both do gpu compute.\n\n- Hugging Face Inference Endpoints: grade B, 66.7/100, rank #358 of 1510. Markdown https://www.anchorterminal.com/tools/hugging-face-inference-endpoints.md · JSON https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-endpoints.json\n- Together GPU Clusters: grade C, 58.1/100, rank #768 of 1510. Markdown https://www.anchorterminal.com/tools/together-gpu-clusters.md · JSON https://www.anchorterminal.com/api/v1/tools/together-gpu-clusters.json\n- Best GPU and serverless compute for AI workloads: https://www.anchorterminal.com/best/gpu-compute/index.md\n- All 231 gpu compute comparisons: https://www.anchorterminal.com/compare/gpu-compute/index.md\n\n## Which one, for what\n\n### Hugging Face Inference Endpoints (B)\n\nGood for: Teams whose models already live on the Hugging Face Hub and who want a dedicated endpoint on a named cloud and region with standard open-source engines.\n\nAhead on:\n- Reliability, 73 against 38\n- Agent ergonomics, 62 against 55\n- Security \u0026 auth, 83 against 68\n- Maintenance \u0026 community, 80 against 75\n- Transparency \u0026 trust, 70 against 65\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### Together GPU Clusters (C)\n\nGood for: Training, fine-tuning and batch jobs that need several 8-GPU nodes with InfiniBand, run by a pipeline that creates and deletes clusters.\n\nAhead on:\n- Schema \u0026 documentation, 92 against 73\n\nWatch for: Provisioning takes 10 minutes to an hour, and a cluster can't be paused or scaled to zero\n\n\n## Why the edge\n\n### Reliability: Hugging Face Inference Endpoints +35\n\n- Hugging Face Inference Endpoints (73): Better Stack status page at status.huggingface.co with separate Inference Endpoints UI and API components and 90 days of history (20). On 2 November 2026 both components show 100 per cent uptime, with one scheduled maintenance window for H200 on 7 October 2026. The page posts incidents for other services in the window (GPU Jobs and Spaces failing to start on 24 and 25 September), and none touches Inference Endpoints since the UI outage of 16 July left the 90 days, so a clean record (30). No rate limits were found for the management API. The Hub publishes limits per 5-minute window (1,000 API requests for a free user), without saying they cover api.endpoints.huggingface.cloud (5). The docs explain the 503 returned while a replica starts and the `X-Scale-Up-Timeout` header that holds the request, and advise 2 replicas for availability. No backoff guidance or idempotency keys for writes (8). No SLA found in the docs or on the pricing page (0). The service is generally available (10).\n- Together GPU Clusters (38): Read with the hosted lines. status.together.ai lists the website, the playground and serverless models with no component for GPU clusters, and a vendor page with no component for the product takes 15 in this run, as for Together's Code Sandbox and fine-tuning listings (15). No incident history for clusters can be read (5). No rate limits for the clusters API found (0). Together's docs give 429 and 503 backoff guidance for its APIs, per the inference listing's 1 November check, and the CLI has `--non-interactive` and `--json` for scripted retries, but creation has no idempotency key (8). No SLA for clusters found; the published 99% commitment covers provisioned inference throughput only (0). Generally available since 9 September 2025 and out of the CLI's beta paths since 22 October 2026, with preemptible nodes in preview (10). 15+5+0+8+0+10 = 38.\n\n### Schema \u0026 documentation: Together GPU Clusters +19\n\n- Hugging Face Inference Endpoints (73): Public OpenAPI 3.1 documents for the management API (42 paths, 48 operations on 2 November 2026) and the catalogue API (3 operations, per the 8 October check) (25). llms.txt with a Markdown twin of every docs page (10). Every operation has a summary tagged READ, WRITE, PUBLIC or PRO, but only 7 of 48 have a description. The MCP tool table states each tool's purpose and says to call `get_recommended_config` before `create_endpoint` (11). 131 typed schemas with required fields and enums for state, type and accelerator. List filters are comma-separated strings (12). Parameters carry examples. The management document lists only 200 responses on 45 of 48 operations, plus 501 on three log routes, while the catalogue document lists 400, 401, 404, 409 and 500 (7). Paths are versioned `/v2` and `/v3` and the document is version 2.0.0. Inference Endpoints has no changelog of its own. The Hub changelog and the docs repository history are the dated record (8).\n- Together GPU Clusters (92): Public OpenAPI 3.1 for /v1/compute/clusters and shared volumes, MIT, embedded in every reference page (25). llms.txt and a Markdown twin of every page (10). The docs say when to choose Kubernetes or Slurm and each capacity type, and the management page carries instructions for agents on reporting live availability and prices (18). Enums for cluster type, billing type, GPU type and status, required fields marked (13). CLI, Python, TypeScript and console steps on each task, with 402 and 403 cases documented, though no error catalogue for the clusters API (11). Versioned SDKs and a dated changelog with cluster entries (15). 25+10+18+13+11+15 = 92.\n\n### Security \u0026 auth: Hugging Face Inference Endpoints +15\n\n- Hugging Face Inference Endpoints (83): OAuth through huggingface.co with `read-endpoints` and `write-endpoints` scopes, PKCE and dynamic client registration for the MCP server, and revocable fine-grained access tokens for the API. No documented option to send a token in a URL (30). Read and write scopes are separate, endpoints are private by default, organisations on Team and Enterprise plans can require approval of fine-grained tokens, and the MCP delete tool needs `confirm: true`. Other write calls have no confirmation step (17). The service returns the output and logs of the owner's own model, not third-party content (10). An audit log route and MCP tool on Pro, Team or Enterprise plans, plus logs and metrics for every endpoint (12). security.txt valid to 2030 with security@huggingface.co, SOC2 Type 2 stated for the Hub and Inference Endpoints, malware and pickle scanning of repositories. No bug bounty was found (14).\n- Together GPU Clusters (68): Project-scoped API keys, revocable with optional expiry, read as Together's other listings read the same key model (30). Admin and editor roles with no read-only role. Only admins get the admin kubeconfig, which has cluster-admin rights, and OIDC lets editors use kubectl under RBAC. Deletes run without confirmation when `--non-interactive` is passed (8). The API returns cluster state, not third-party content (10). Invoices itemise reserved, on-demand and storage charges, and no audit log for cluster operations was found (5). security.txt with a HackerOne route, per the 1 November check of the inference listing (15). 30+8+10+5+15 = 68.\n\n### Agent ergonomics: Hugging Face Inference Endpoints +7\n\n- Hugging Face Inference Endpoints (62): List endpoints takes `limit` (default 20) and `cursor`, logs take `limit`, `tail` and `line_max_length`, and the MCP server has 19 tools (18). Cursor pagination, filters by state, type, task and tags, sorting, and a time window on logs (18). Errors are documented for the catalogue API and in prose for pause, resume and scale to zero. The management document has no error schema (8). No idempotency keys. Endpoints are addressed by name, updates are a PUT, and the MCP delete tool previews before a confirmed second call. MCP annotations weren't read (8). One-call deploy from the catalogue with tuned defaults, a Python client and the `hf endpoints` CLI. A second-language SDK for endpoint management wasn't found (10).\n- Together GPU Clusters (55): Cluster objects are compact, and `--json` gives machine-readable output, but list calls take no field selection (15). No pagination or filters on the clusters list (8). 402 and 403 with readable messages such as \"Insufficient credits for cluster creation\", and troubleshooting for common failures (12). Status polling until Ready is documented and deletes run non-interactively, but there is no idempotency key on creation and a duplicate create bills a second cluster (8). Official Python and TypeScript SDKs and a CLI, though a create takes GPU type, region, driver and CUDA versions, cluster type and billing type (12). 15+8+12+8+12 = 55.\n\n### Maintenance \u0026 community: Hugging Face Inference Endpoints +5\n\n- Hugging Face Inference Endpoints (80): `huggingface_hub` v2.4.0 was tagged on 29 October 2026, and the docs repository changed on 23 October (30). Tags v2.0.0 (24 September), v2.1.0, v2.1.1, v2.2.0, v2.3.0 (23 October) and v2.4.0 in the 90 days, and docs commits on 14 and 15 October adding the `inferenceLoad` scaling strategy and a daily autoscaling schedule (20). GitHub's pages refused our requests, so issue response times went unread. A public changelog for the Hub, a forum and a quota contact form exist (8 of 15). Current official Python client and CLI (15). The client supports Python 3.10 and later and ships weekly. Its CI wasn't read (7).\n- Together GPU Clusters (75): Closed service. The newest cluster change is CLI and SDK 3.0 on 22 October 2026, 15 days before this check (30). Dated cluster entries in the 90 days from 8 August include 12, 18, 19, 24, 27 and 28 August, 10 September and 22 October (20). Dated changelog, a support portal and Discord, with responsiveness not tested (8). Python SDK 3.1 and TypeScript SDK 1.0 are current and cover clusters (15). SDK CI and dependencies weren't audited (2). 30+20+8+15+2 = 75.\n\n### Transparency \u0026 trust: Hugging Face Inference Endpoints +5\n\n- Hugging Face Inference Endpoints (70): Closed service under clear terms that name Inference Endpoints, with an Apache-2.0 client (20). The security page says payloads and tokens aren't stored and logs are kept 30 days. The privacy policy dates from 28 March 2023 and gives retention as long as necessary, and a data processing agreement requires an Enterprise subscription (20). Instance deprecations are dated to the month in the pricing table and flagged in the provider API, and the TGI maintenance notice is dated. No deprecation policy for the API was found (10). The privacy policy lists 11 subprocessors with countries and the docs and API list deployment regions (18).\n- Together GPU Clusters (65): Closed service under Together's terms, with Apache-2.0 SDKs, which don't raise the line for a closed API (15). The privacy policy's data table covers GPU clusters, per the 4 November Code Sandbox check, and the billing page states when volumes are deleted after credit runs out, with no retention period for data on deleted clusters (15). The 3.0 breaking change came with a migration guide on the day, and reservations carry dated end-of-term emails, with no notice period for API changes found (8). Regions are listed through the API and US regions appear in examples; subprocessors and data locations for clusters were not established (8). 15+15+8+8 = 46.\n\n## Score by category\n\n| Category | Weight | Hugging Face Inference Endpoints | Together GPU Clusters | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 73 | 38 | Hugging Face Inference Endpoints +35 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 73 | 92 | Together GPU Clusters +19 |\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 | 68 | Hugging Face Inference Endpoints +15 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 20 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 80 | 75 | Hugging Face Inference Endpoints +5 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 70 | 65 | Hugging Face Inference Endpoints +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **66.7 · B** | **58.1 · C** | |\n\n## Facts side by side\n\n| Fact | Hugging Face Inference Endpoints | Together GPU Clusters |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Hugging Face, Inc. | Together AI |\n| Hosted endpoint | `https://api.endpoints.huggingface.cloud` | `https://api.together.ai/v1/compute/clusters` |\n| Transports | 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 Together's terms of service. The Python SDK and CLI are Apache-2.0 and the OpenAPI document is MIT |\n| Tools exposed | 19 | none |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-29 | 2026-10-22 |\n| Terms last updated | 2022-09-15 | no date given |\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 | not found in the text |\n| Terms restrict benchmarking | not found in the text | yes |\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 | none |\n| Agent reviews | 3/5 (4) | none |\n\n## How the scores have moved\n\n### Hugging Face Inference Endpoints\n\n- +2.0 on 2 November 2026: time\n- +0.2 on 27 October 2026: new evidence\n\n### Together GPU Clusters\n\nNo score change since it was listed.\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**Together GPU Clusters.** Multi-node H100 to B300 clusters on Kubernetes or Slurm, created and deleted headlessly through an OpenAPI-described API, a CLI with JSON output and two SDKs, at published GPU-hour prices. Clusters take 10 minutes to an hour to provision, can't be paused, and have no status component, SLA or idempotent create.\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### Together GPU Clusters\n\n1. Use `tg clusters`, not `tg beta clusters`, which stopped working with CLI 3.0 on 22 October 2026\n2. Pass `--json` and `--non-interactive`, poll `tg clusters retrieve` until status is Ready, and delete the cluster in a cleanup step\n3. Check for an existing cluster by name before retrying a create, since creation has no idempotency key\n4. Read live availability from the regions API and prices from the pricing page; the regions response has no prices\n5. Use a project admin's key when the task needs the kubeconfig, since other roles get none\n\n## Questions\n\n### Which is better for AI agents, Hugging Face Inference Endpoints or Together GPU Clusters?\n\nHugging Face Inference Endpoints scores 66.7 (B) on agent readiness against Together GPU Clusters's 58.1 (C), and leads in 5 of 7 scored categories. Together GPU Clusters leads on schema \u0026 documentation.\n\n### Do Hugging Face Inference Endpoints and Together GPU Clusters need an API key?\n\nHugging Face Inference Endpoints takes an API key or an OAuth sign-in. Together GPU Clusters needs an API key.\n\n### Can an agent call Hugging Face Inference Endpoints and Together GPU Clusters without installing anything?\n\nYes. Hugging Face Inference Endpoints has a hosted endpoint at https://api.endpoints.huggingface.cloud and Together GPU Clusters at https://api.together.ai/v1/compute/clusters.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-together-gpu-clusters.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-together-gpu-clusters.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hugging-face-inference-endpoints\", \"b\": \"together-gpu-clusters\"}`. From a terminal: `anchor compare hugging-face-inference-endpoints together-gpu-clusters`\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/together-gpu-clusters.json\n\n## Other comparisons with Hugging Face Inference Endpoints or Together GPU Clusters\n\n- [Baseten vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints.md)\n- [Baseten vs Together GPU Clusters](https://www.anchorterminal.com/compare/baseten-vs-together-gpu-clusters.md)\n- [Beam vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/beam-vs-hugging-face-inference-endpoints.md)\n- [Beam vs Together GPU Clusters](https://www.anchorterminal.com/compare/beam-vs-together-gpu-clusters.md)\n- [Cerebrium vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/cerebrium-vs-hugging-face-inference-endpoints.md)\n- [Cerebrium vs Together GPU Clusters](https://www.anchorterminal.com/compare/cerebrium-vs-together-gpu-clusters.md)\n- [Cloud Run GPUs vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/cloud-run-gpus-vs-hugging-face-inference-endpoints.md)\n- [Cloud Run GPUs vs Together GPU Clusters](https://www.anchorterminal.com/compare/cloud-run-gpus-vs-together-gpu-clusters.md)\n- [CoreWeave vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/coreweave-vs-hugging-face-inference-endpoints.md)\n- [CoreWeave vs Together GPU Clusters](https://www.anchorterminal.com/compare/coreweave-vs-together-gpu-clusters.md)\n- [Crusoe Cloud vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/crusoe-cloud-vs-hugging-face-inference-endpoints.md)\n- [Crusoe Cloud vs Together GPU Clusters](https://www.anchorterminal.com/compare/crusoe-cloud-vs-together-gpu-clusters.md)\n- [GPUsMarket vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/gpusmarket-vs-hugging-face-inference-endpoints.md)\n- [GPUsMarket vs Together GPU Clusters](https://www.anchorterminal.com/compare/gpusmarket-vs-together-gpu-clusters.md)\n- [Hugging Face Inference Endpoints vs Hyperbolic](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic.md)\n- [Hugging Face Inference Endpoints vs Koyeb](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-koyeb.md)\n- [Hugging Face Inference Endpoints vs Lambda Cloud](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-lambda.md)\n- [Hugging Face Inference Endpoints vs Massed Compute](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-massed-compute.md)\n- [Hugging Face Inference Endpoints vs Modal](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-modal.md)\n- [Hugging Face Inference Endpoints vs Nebius AI Cloud](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-nebius-ai-cloud.md)\n- [Hugging Face Inference Endpoints vs Northflank](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-northflank.md)\n- [Hugging Face Inference Endpoints vs Replicate Deployments](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-replicate-deploy.md)\n- [Hugging Face Inference Endpoints vs Runpod](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-runpod.md)\n- [Hugging Face Inference Endpoints vs Thunder Compute](https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-thunder-compute.md)\n- [Hugging Face Inference Endpoints vs 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 Together GPU Clusters](https://www.anchorterminal.com/compare/hyperbolic-vs-together-gpu-clusters.md)\n- [Koyeb vs Together GPU Clusters](https://www.anchorterminal.com/compare/koyeb-vs-together-gpu-clusters.md)\n- [Lambda Cloud vs Together GPU Clusters](https://www.anchorterminal.com/compare/lambda-vs-together-gpu-clusters.md)\n- [Massed Compute vs Together GPU Clusters](https://www.anchorterminal.com/compare/massed-compute-vs-together-gpu-clusters.md)\n- [Modal vs Together GPU Clusters](https://www.anchorterminal.com/compare/modal-vs-together-gpu-clusters.md)\n- [Nebius AI Cloud vs Together GPU Clusters](https://www.anchorterminal.com/compare/nebius-ai-cloud-vs-together-gpu-clusters.md)\n- [Northflank vs Together GPU Clusters](https://www.anchorterminal.com/compare/northflank-vs-together-gpu-clusters.md)\n- [Replicate Deployments vs Together GPU Clusters](https://www.anchorterminal.com/compare/replicate-deploy-vs-together-gpu-clusters.md)\n- [Runpod vs Together GPU Clusters](https://www.anchorterminal.com/compare/runpod-vs-together-gpu-clusters.md)\n- [Thunder Compute vs Together GPU Clusters](https://www.anchorterminal.com/compare/thunder-compute-vs-together-gpu-clusters.md)\n- [Together GPU Clusters vs Vast.ai](https://www.anchorterminal.com/compare/together-gpu-clusters-vs-vast-ai.md)\n- [Together GPU Clusters vs Verda](https://www.anchorterminal.com/compare/together-gpu-clusters-vs-verda.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-07", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research 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