{ "data": { "a": { "slug": "baseten", "name": "Baseten", "vendor": "Baseten", "vendorUrl": "https://www.baseten.co", "kind": "http-api", "category": "gpu-compute", "summary": "Dedicated model deployments packaged with the open-source Truss framework and served behind a per-model HTTPS endpoint, with autoscaling from zero replicas, async inference, a management API and per-minute GPU billing from T4 to B200.", "url": "https://www.anchorterminal.com/tools/baseten", "markdownUrl": "https://www.anchorterminal.com/tools/baseten.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/baseten.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/baseten.json", "repo": "https://github.com/basetenlabs/truss", "license": "MIT", "transports": [ "http" ], "remoteUrl": "https://api.baseten.co", "packages": [ { "registry": "pypi", "name": "truss" } ], "auth": "api-key", "authNotes": "API key from the workspace settings, sent as `Authorization: Bearer $BASETEN_API_KEY` (preferred) or the legacy `Authorization: Api-Key` scheme. Keys created from 1 October 2026 carry a `b10_` prefix. Inference goes to model-\u003cid\u003e.api.baseten.co and management calls to api.baseten.co.", "pricing": "usage", "pricingNotes": "Basic is $0 a month, pay as you go; Pro and Enterprise add volume discounts. Dedicated deployments bill per minute of replica time, including start-up and idle, and nothing at zero replicas. T4 16 GiB $0.01052 a minute (about $0.63 an hour), L4 24 GiB $0.01414 ($0.85), A10G 24 GiB $0.02012 ($1.21), H100 MIG 40 GiB $0.0625 ($3.75), A100 80 GiB $0.06667 ($4.00), H100 80 GiB $0.10833 ($6.50), B200 180 GiB $0.16633 ($9.98). New accounts get a small credit to try the UI. Model APIs bill per token instead (https://www.baseten.co/pricing/).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 1200, "npmWeekly": null, "pypiWeekly": 74496, "asOf": "2026-09-30" }, "docsUrl": "https://docs.baseten.co", "llmsTxt": "https://docs.baseten.co/llms.txt", "openapi": "https://api.baseten.co/v1/spec", "capabilities": [ "compute.gpu", "compute.endpoints", "compute.serverless", "compute.containers" ], "tags": [ "hosted", "usage-priced", "python", "llms-txt", "open-source", "async-jobs", "webhooks", "enterprise" ], "lastRelease": "2026-09-28", "graded": true, "anchor": { "graded": true, "score": 66.5, "grade": "B", "agentReady": false, "rank": 264, "ranked": true, "rankOf": 842, "categoryRank": 2, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 55, "maintenance": 90, "payments": 40, "reliability": 80, "schema": 84, "security": 82, "transparency": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": -5, "negativeNotes": [ "-5: a GitHub personal access token for `basetenbot`, exposed in a public Harbor image since March 2023, gave admin and push access to Baseten's main product repository, the GitOps repository that drives its clusters, its Homebrew tap and per-customer private repositories. Reported on 2026-07-13, revoked on 2026-07-14, no misuse found, published with Baseten's approval in September 2026. Deducted less because the fix was quick and documented (https://www.strix.ai/blog/baseten-harbor-github-pat-takeover)" ], "verdict": "Team API keys scoped to inference-only, metrics-only or a single environment or model, plus a Viewer role since 1 September 2026. H100 at $6.50 and A100 at $4.00 an hour, and start-up and idle replica time are billed.", "bestFor": "Teams that want one model behind a production endpoint with real autoscaling knobs, environments and scoped keys.", "strengths": [ "Team API keys scoped to inference-only, metrics-only or a single environment or model, plus a Viewer role since 1 September 2026", "Public OpenAPI spec for the management API at api.baseten.co/v1/spec, and llms.txt with Markdown twins", "Rate limits published per endpoint with a `retry_after` field on 429", "Free starting credits with no payment method needed until they run out", "Truss (MIT) keeps the model package portable, with three releases in September 2026" ], "weaknesses": [ "H100 at $6.50 and A100 at $4.00 an hour, and start-up and idle replica time are billed", "21 status-page incidents between 31 July and 29 September 2026, mostly single-cluster 5xx", "A bot token with admin access to the product and GitOps repositories sat exposed from March 2023 until July 2026", "No pagination on management list endpoints and no idempotency keys", "No SLA below Enterprise, no bug bounty and no security.txt" ], "agentNotes": [ "Create a team key with inference-only permission for calling models and keep full-access keys out of the agent", "Sleep for `retry_after` seconds on a 429 from api.baseten.co; the activate and deactivate endpoints allow 20 calls a minute", "Retry 429, 503 and 529 with backoff, but treat 500 as a bug in your model code", "Set `scale_down_delay` below the 900-second default or every burst bills 15 idle minutes", "Send payloads over 256 KiB to `/predict`, not `/async_predict`, unless support has raised the async limit" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 66.5 } ], "editorialScores": { "ergonomics": 55, "maintenance": 90, "payments": 40, "reliability": 80, "schema": 84, "security": 82, "transparency": 58 }, "provenanceScore": 71 }, "connect": { "install": "pip install truss", "http": "curl -X POST \"https://model-$BASETEN_MODEL_ID.api.baseten.co/environments/production/predict\" \\\n -H \"Authorization: Bearer $BASETEN_API_KEY\" -H \"Content-Type: application/json\" \\\n -d '{\"prompt\":\"Hello, world!\"}'" }, "letme": { "capability": "https://letme.dev/compute.gpu", "tool": "https://letme.dev/baseten" }, "area": "models", "unitPrices": [ { "item": "H100 80 GiB", "unit": "gpu-hour", "usd": 6.5, "note": "$0.10833 a minute" }, { "item": "B200 180 GiB", "unit": "gpu-hour", "usd": 9.98, "note": "$0.16633 a minute" }, { "item": "A100 80 GiB", "unit": "gpu-hour", "usd": 4, "note": "$0.06667 a minute" }, { "item": "H100 MIG 40 GiB", "unit": "gpu-hour", "usd": 3.75, "note": "$0.0625 a minute" }, { "item": "A10G 24 GiB", "unit": "gpu-hour", "usd": 1.21, "note": "$0.02012 a minute" }, { "item": "L4 24 GiB", "unit": "gpu-hour", "usd": 0.85, "note": "$0.01414 a minute" }, { "item": "T4 16 GiB", "unit": "gpu-hour", "usd": 0.63, "note": "$0.01052 a minute" } ], "provenance": { "legalEntity": "Baseten Labs, Inc.", "domain": "baseten.co", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://www.baseten.co/terms-and-conditions/", "privacy": "https://www.baseten.co/privacy-policy/", "statusPage": "https://status.baseten.co", "changelog": "https://www.baseten.co/changelog/", "securityTxt": "none", "checked": "2026-09-30", "notes": [ "Terms name Baseten Labs, Inc. under California law with venue in San Francisco. The privacy policy gives 560 Davis St., Suite 250, San Francisco.", "Inference runs on model-\u003cid\u003e.api.baseten.co, a subdomain of the vendor domain.", "www.baseten.co/.well-known/security.txt returns 404.", "The .co registry's RDAP server couldn't be reached, so the registration date is unrecorded." ], "score": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/baseten.json", "live": { "slug": "baseten", "probe": { "target": "https://api.baseten.co", "method": "get", "lastAt": "2026-10-09T11:46:23.124800635Z", "lastOk": true, "lastStatus": 202, "lastMs": 428, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 459, "p95ms24h": 502, "samples24h": 259, "samples30d": 2109, "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": 125, "ok": 125 } ] }, "vendorStatus": { "page": "https://status.baseten.co", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-09T11:37:35.358650342Z" }, "versions": [ { "registry": "github", "name": "basetenlabs/truss", "version": "v0.18.33", "released": "2026-10-06", "seenAt": "2026-10-08T16:02:10.626982738Z" }, { "registry": "pypi", "name": "truss", "version": "0.18.33", "released": "2026-10-06", "seenAt": "2026-10-08T16:02:06.626291704Z" } ], "githubStars": 1214, "pypiWeekly": 62737, "securityTxt": { "url": "https://baseten.co/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:38:37.892521999Z" }, "llmsTxt": { "url": "https://docs.baseten.co/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:06.558742297Z" }, "domain": { "domain": "baseten.co", "checkedAt": "2026-10-04T13:09:05.701625104Z" }, "pages": [ { "url": "https://www.baseten.co/changelog/", "kind": "changelog", "status": 200, "checkedAt": "2026-10-08T18:26:31.551352111Z", "changedAt": "2026-10-06T16:14:05.469135663Z", "fingerprint": "fcc7d4bab97d" }, { "url": "https://www.baseten.co/pricing/", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:26:33.809519828Z", "changedAt": "2026-10-06T16:14:07.823033015Z", "fingerprint": "e41ad7119a6b" }, { "url": "https://www.baseten.co/privacy-policy/", "kind": "privacy", "status": 200, "checkedAt": "2026-10-08T18:26:35.678713031Z", "changedAt": "2026-10-06T16:14:09.752979771Z", "fingerprint": "09df4e188f65" }, { "url": "https://www.baseten.co/terms-and-conditions/", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:26:38.014111749Z", "changedAt": "2026-10-06T16:14:11.853258349Z", "fingerprint": "a8b301619021" } ], "updatedAt": "2026-10-09T11:46:23.124800635Z" } }, "answer": "Baseten scores 66.5 (B) on agent readiness against Hugging Face Inference Endpoints's 64.5 (B), and leads in 4 of 7 scored categories. Hugging Face Inference Endpoints leads on agent ergonomics.", "b": { "slug": "hugging-face-inference-endpoints", "name": "Hugging Face Inference Endpoints", "vendor": "Hugging Face, Inc.", "vendorUrl": "https://huggingface.co", "kind": "http-api", "category": "gpu-compute", "summary": "Managed Hugging Face service that deploys a Hub model as a dedicated, autoscaling HTTPS endpoint on AWS, Azure or Google Cloud, using vLLM, TGI, SGLang, llama.cpp, TEI or a custom container. Managed by REST API, Python client, CLI or MCP.", "url": "https://www.anchorterminal.com/tools/hugging-face-inference-endpoints", "markdownUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-endpoints.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-endpoints.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-endpoints.json", "repo": "https://github.com/huggingface/hf-endpoints-documentation", "license": "Proprietary service under the Hugging Face Terms of Service. The `huggingface_hub` Python client and CLI are Apache-2.0", "transports": [ "http" ], "remoteUrl": "https://api.endpoints.huggingface.cloud", "packages": [ { "registry": "pypi", "name": "huggingface_hub" } ], "auth": "mixed", "authNotes": "Hugging Face access token sent as `Authorization: Bearer $HF_TOKEN` to the management API and to each endpoint. Tokens are created in the account settings in a browser and can be fine-grained, read or write. The MCP server uses OAuth through huggingface.co (authorisation code with PKCE, device code, dynamic client registration) with `read-endpoints` and `write-endpoints` scopes. `GET /v2/provider` and the catalogue list need no token. Access is self-serve, with quota requests for larger instances.", "pricing": "usage", "pricingNotes": "Usage priced by instance hour, billed per minute while a replica is initialising or running. GPUs run from $0.50 an hour (T4) to $10 (H100 on GCP), CPUs from $0.033. No free tier or trial was found. The docs require a payment method and credits before deploying, and the pricing page says an active subscription. Paused endpoints and endpoints at zero replicas aren't billed for compute (https://huggingface.co/docs/inference-endpoints/support/pricing).", "priceSummary": "$0.033 / vCPU-hr", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the Inference Endpoints docs, the two OpenAPI documents or the pricing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": 19, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": 60014944, "asOf": "2026-10-08" }, "docsUrl": "https://huggingface.co/docs/inference-endpoints/index", "llmsTxt": "https://huggingface.co/docs/inference-endpoints/llms.txt", "openapi": "https://api.endpoints.huggingface.cloud/openapi.json", "capabilities": [ "compute.gpu", "compute.endpoints", "compute.containers" ], "tags": [ "hosted", "usage-priced", "python", "cli", "mcp", "oauth", "openapi", "llms-txt", "status-page", "soc2", "enterprise" ], "lastRelease": "2026-10-08", "graded": true, "anchor": { "graded": true, "score": 64.5, "grade": "B", "agentReady": false, "rank": 314, "ranked": true, "rankOf": 842, "categoryRank": 3, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 62, "maintenance": 80, "payments": 20, "reliability": 63, "schema": 73, "security": 83, "transparency": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "OAuth scopes separate reading endpoints from writing them, both OpenAPI documents are public, and the unauthenticated `/v2/provider` route lists every instance with its hourly price. An account needs a payment method and credits before the first deployment, no rate limits or SLA were found for the management API, and the docs price table disagrees with the live list in places.", "bestFor": "Teams whose models already live on the Hugging Face Hub and who want a dedicated endpoint on a named cloud and region with standard open-source engines.", "strengths": [ "Public OpenAPI 3.1 documents for the management API (46 operations) and the catalogue API (3), plus llms.txt and a Markdown twin of every docs page", "The MCP server at endpoints.huggingface.co/mcp uses OAuth with `read-endpoints` and `write-endpoints` scopes, PKCE and dynamic client registration", "`GET /v2/provider` needs no token and returns each instance type by cloud and region with status and price per hour", "The MCP `delete_endpoint` tool returns a preview and deletes only on a second call with `confirm: true`", "The Inference Endpoints API component on status.huggingface.co shows 100 per cent uptime over the 90 days to 8 October 2026" ], "weaknesses": [ "No free tier. The docs require a payment method and credits, and replicas are billed while initialising as well as running", "No rate limits, SLA or idempotency keys were found for the management API, and its OpenAPI document lists only 200 responses on 43 of 46 operations", "The docs price table and the live provider list disagree. Inferentia2 x1 is $0.75 in the docs and $1.95 in the API, and AWS H200 is listed in the docs and marked deprecated in the API", "A start from zero replicas takes minutes by the docs' own account, and the proxy answers 503 until a replica is ready", "The Hub outage of 16 July 2026 took the Inference Endpoints UI down for 1 hour 37 minutes" ], "agentNotes": [ "Call `GET https://api.endpoints.huggingface.cloud/v2/provider` first and pick an instance whose `status` is `available`. The docs table lists types the API marks deprecated or not available", "Send `X-Scale-Up-Timeout: 600` on requests to an endpoint that scales to zero, or handle 503 while the first replica starts", "Set `scaleToZeroTimeout` yourself. The docs give a default of 1 hour and the OpenAPI document says 15 minutes", "Pause or delete an endpoint when the job is done. Billing covers every minute a replica is initialising or running", "Give the agent a fine-grained token or the `read-endpoints` scope unless it must deploy. Endpoints are private by default and take the same Hugging Face token as a bearer" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.5 } ], "editorialScores": { "ergonomics": 62, "maintenance": 80, "payments": 20, "reliability": 63, "schema": 73, "security": 83, "transparency": 68 }, "provenanceScore": 67 }, "connect": { "install": "pip install huggingface_hub", "http": "curl \"https://api.endpoints.huggingface.cloud/v2/endpoint/$NAMESPACE\" \\\n -H \"Authorization: Bearer $HF_TOKEN\"" }, "letme": { "capability": "https://letme.dev/compute.gpu", "tool": "https://letme.dev/hugging-face-inference-endpoints" }, "area": "models", "unitPrices": [ { "item": "NVIDIA T4 16 GB x1 (AWS, GCP)", "unit": "gpu-hour", "usd": 0.5, "note": "Billed per minute while initialising or running" }, { "item": "NVIDIA L4 24 GB x1 (AWS)", "unit": "gpu-hour", "usd": 0.8, "note": "$0.70 on GCP us-east4" }, { "item": "NVIDIA A10G 24 GB x1 (AWS)", "unit": "gpu-hour", "usd": 1, "note": "us-east-1 and eu-west-1" }, { "item": "NVIDIA L40S 48 GB x1 (AWS)", "unit": "gpu-hour", "usd": 1.8, "note": "us-east-1" }, { "item": "NVIDIA A100 80 GB x1 (AWS)", "unit": "gpu-hour", "usd": 2.5, "note": "$3.60 on GCP us-east4" }, { "item": "NVIDIA RTX PRO 6000 Blackwell 96 GB x1 (AWS)", "unit": "gpu-hour", "usd": 2.75, "note": "us-east-2, in the live provider list and absent from the docs table" }, { "item": "NVIDIA H200 141 GB x1 (GCP)", "unit": "gpu-hour", "usd": 5, "note": "us-south1. The AWS H200 in us-west-2 is marked deprecated in the API" }, { "item": "NVIDIA H100 80 GB x1 (GCP)", "unit": "gpu-hour", "usd": 10, "note": "us-east4. The AWS H100 at $4.50 is deprecated from December 2025" }, { "item": "Intel Sapphire Rapids x1, 1 vCPU and 2 GB (AWS)", "unit": "vcpu-hour", "usd": 0.033, "note": "$0.050 on GCP and $0.060 on Azure Intel Xeon" } ], "provenance": { "legalEntity": "Hugging Face, Inc.", "domain": "huggingface.co", "domainRegistered": "", "endpointOnVendorDomain": false, "terms": "https://huggingface.co/terms-of-service", "privacy": "https://huggingface.co/privacy", "statusPage": "https://status.huggingface.co", "changelog": "https://huggingface.co/changelog", "securityTxt": "valid", "checked": "2026-10-08", "notes": [ "The Terms of Service (effective 15 September 2022) name Hugging Face, Inc., a Delaware corporation, list Inference Endpoints among the services they cover and are governed by New York law. They link Supplemental Terms (effective 28 April 2025) as a PDF, of which our reader extracted only the first page.", "The privacy policy (effective 28 March 2023) names Hugging Face, Inc. and its EU establishment Hugging Face, SAS, 9 rue des Colonnes, 75002 Paris, and lists 11 subprocessors with countries. The Inference Endpoints security page points to it.", "The management API answers at api.endpoints.huggingface.cloud and deployed endpoints at subdomains of endpoints.huggingface.cloud, a second domain of the vendor's. The catalogue API and the MCP server are on endpoints.huggingface.co.", "huggingface.co/.well-known/security.txt gives security@huggingface.co and expires on 1 July 2030. endpoints.huggingface.co/.well-known/security.txt returns 404.", "status.huggingface.co is a Better Stack page with separate components for the Inference Endpoints UI and API.", "The changelog at huggingface.co/changelog covers the whole Hub. Inference Endpoints has no changelog of its own. Dated changes are in the docs repository's commit history.", "rdap.org returned 404 for huggingface.co, so the registration date is unrecorded." ], "score": 67 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-endpoints.json", "live": { "slug": "hugging-face-inference-endpoints", "probe": { "target": "https://api.endpoints.huggingface.cloud", "method": "get", "lastAt": "2026-10-09T11:46:30.653783864Z", "lastOk": true, "lastStatus": 200, "lastMs": 257, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 255, "p95ms24h": 300, "samples24h": 44, "samples30d": 44, "days": [ { "date": "2026-10-09", "probes": 44, "ok": 44 } ] }, "vendorStatus": { "page": "https://status.huggingface.co", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-09T07:58:03.654181492Z" }, "updatedAt": "2026-10-09T11:46:30.653783864Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Baseten", "b": "Hugging Face, Inc.", "name": "Vendor" }, { "a": "https://api.baseten.co", "b": "https://api.endpoints.huggingface.cloud", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "OAuth or key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Proprietary service under the Hugging Face Terms of Service. The `huggingface_hub` Python client and CLI are Apache-2.0", "name": "Licence" }, { "a": "none", "b": "19", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-09-28", "b": "2026-10-08", "name": "Last release" }, { "a": "no date given", "b": "2022-09-15", "name": "Terms last updated" }, { "a": "no date given", "b": "2023-03-28", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "yes", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "1.2k stars, 74k PyPI/wk", "b": "60M PyPI/wk", "name": "Popularity" }, { "a": "3.5/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Baseten scores 66.5 (B) on agent readiness against Hugging Face Inference Endpoints's 64.5 (B), and leads in 4 of 7 scored categories. Hugging Face Inference Endpoints leads on agent ergonomics.", "question": "Which is better for AI agents, Baseten or Hugging Face Inference Endpoints?" }, { "answer": "Baseten needs an API key. Hugging Face Inference Endpoints takes an API key or an OAuth sign-in.", "question": "Do Baseten and Hugging Face Inference Endpoints need an API key?" }, { "answer": "Yes. Baseten has a hosted endpoint at https://api.baseten.co and Hugging Face Inference Endpoints at https://api.endpoints.huggingface.cloud.", "question": "Can an agent call Baseten and Hugging Face Inference Endpoints without installing anything?" }, { "answer": "Baseten is open source (MIT). No open-source release is listed for Hugging Face Inference Endpoints.", "question": "Are Baseten and Hugging Face Inference Endpoints open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 80 against 63", "Schema \u0026 documentation, 84 against 73", "Payments \u0026 pricing, 40 against 20", "Maintenance \u0026 community, 90 against 80" ], "also": [ "Open source" ], "goodFor": "Teams that want one model behind a production endpoint with real autoscaling knobs, environments and scoped keys.", "slug": "baseten", "watchFor": "H100 at $6.50 and A100 at $4.00 an hour, and start-up and idle replica time are billed" }, { "aheadOn": [ "Agent ergonomics, 62 against 55" ], "also": [ "No incidents deducted, where Baseten loses 5 points for them" ], "goodFor": "Teams whose models already live on the Hugging Face Hub and who want a dedicated endpoint on a named cloud and region with standard open-source engines.", "slug": "hugging-face-inference-endpoints", "watchFor": "No free tier. The docs require a payment method and credits, and replicas are billed while initialising as well as running" } ], "job": { "capability": "compute.gpu", "name": "Compute gpu" }, "others": [ { "json": "https://www.anchorterminal.com/compare/baseten-vs-beam.json", "title": "Baseten vs Beam", "url": "https://www.anchorterminal.com/compare/baseten-vs-beam" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-cerebrium.json", "title": "Baseten vs Cerebrium", "url": "https://www.anchorterminal.com/compare/baseten-vs-cerebrium" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-coreweave.json", "title": "Baseten vs CoreWeave", "url": "https://www.anchorterminal.com/compare/baseten-vs-coreweave" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-hyperbolic.json", "title": "Baseten vs Hyperbolic", "url": "https://www.anchorterminal.com/compare/baseten-vs-hyperbolic" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-koyeb.json", "title": "Baseten vs Koyeb", "url": "https://www.anchorterminal.com/compare/baseten-vs-koyeb" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-lambda.json", "title": "Baseten vs Lambda Cloud", "url": "https://www.anchorterminal.com/compare/baseten-vs-lambda" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-modal.json", "title": "Baseten vs Modal", "url": "https://www.anchorterminal.com/compare/baseten-vs-modal" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-nebius-ai-cloud.json", "title": "Baseten vs Nebius AI Cloud", "url": "https://www.anchorterminal.com/compare/baseten-vs-nebius-ai-cloud" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-northflank.json", "title": "Baseten vs Northflank", "url": "https://www.anchorterminal.com/compare/baseten-vs-northflank" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-replicate-deploy.json", "title": "Baseten vs Replicate Deployments", "url": "https://www.anchorterminal.com/compare/baseten-vs-replicate-deploy" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-runpod.json", "title": "Baseten vs Runpod", "url": "https://www.anchorterminal.com/compare/baseten-vs-runpod" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-thunder-compute.json", "title": "Baseten vs Thunder Compute", "url": "https://www.anchorterminal.com/compare/baseten-vs-thunder-compute" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-vast-ai.json", "title": "Baseten vs Vast.ai", "url": "https://www.anchorterminal.com/compare/baseten-vs-vast-ai" }, { "json": "https://www.anchorterminal.com/compare/baseten-vs-verda.json", "title": "Baseten vs Verda", "url": "https://www.anchorterminal.com/compare/baseten-vs-verda" }, { "json": "https://www.anchorterminal.com/compare/beam-vs-hugging-face-inference-endpoints.json", "title": "Beam vs Hugging Face Inference Endpoints", "url": "https://www.anchorterminal.com/compare/beam-vs-hugging-face-inference-endpoints" }, { "json": "https://www.anchorterminal.com/compare/cerebrium-vs-hugging-face-inference-endpoints.json", "title": "Cerebrium vs Hugging Face Inference Endpoints", "url": "https://www.anchorterminal.com/compare/cerebrium-vs-hugging-face-inference-endpoints" }, { "json": "https://www.anchorterminal.com/compare/coreweave-vs-hugging-face-inference-endpoints.json", "title": "CoreWeave vs Hugging Face Inference Endpoints", "url": "https://www.anchorterminal.com/compare/coreweave-vs-hugging-face-inference-endpoints" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic.json", "title": "Hugging Face Inference Endpoints vs Hyperbolic", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-koyeb.json", "title": "Hugging Face Inference Endpoints vs Koyeb", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-koyeb" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-lambda.json", "title": "Hugging Face Inference Endpoints vs Lambda Cloud", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-lambda" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-modal.json", "title": "Hugging Face Inference Endpoints vs Modal", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-modal" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-nebius-ai-cloud.json", "title": "Hugging Face Inference Endpoints vs Nebius AI Cloud", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-nebius-ai-cloud" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-northflank.json", "title": "Hugging Face Inference Endpoints vs Northflank", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-northflank" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-replicate-deploy.json", "title": "Hugging Face Inference Endpoints vs Replicate Deployments", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-replicate-deploy" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-runpod.json", "title": "Hugging Face Inference Endpoints vs Runpod", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-runpod" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-thunder-compute.json", "title": "Hugging Face Inference Endpoints vs Thunder Compute", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-thunder-compute" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-vast-ai.json", "title": "Hugging Face Inference Endpoints vs Vast.ai", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-vast-ai" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-verda.json", "title": "Hugging Face Inference Endpoints vs Verda", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-verda" } ], "scores": [ { "baseten": 80, "by": 17, "edge": "baseten", "hugging-face-inference-endpoints": 63, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "baseten": 84, "by": 11, "edge": "baseten", "hugging-face-inference-endpoints": 73, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "baseten": 55, "by": 7, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 62, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "baseten": 82, "by": 1, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 83, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "baseten": 40, "by": 20, "edge": "baseten", "hugging-face-inference-endpoints": 20, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "baseten": 90, "by": 10, "edge": "baseten", "hugging-face-inference-endpoints": 80, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "baseten": 65, "by": 3, "edge": "hugging-face-inference-endpoints", "hugging-face-inference-endpoints": 68, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Baseten scores 66.5 (B) on agent readiness against Hugging Face Inference Endpoints's 64.5 (B), and leads in 4 of 7 scored categories. Hugging Face Inference Endpoints leads on agent ergonomics. Both do compute gpu.", "verdicts": { "baseten": "Team API keys scoped to inference-only, metrics-only or a single environment or model, plus a Viewer role since 1 September 2026. H100 at $6.50 and A100 at $4.00 an hour, and start-up and idle replica time are billed.", "hugging-face-inference-endpoints": "OAuth scopes separate reading endpoints from writing them, both OpenAPI documents are public, and the unauthenticated `/v2/provider` route lists every instance with its hourly price. An account needs a payment method and credits before the first deployment, no rate limits or SLA were found for the management API, and the docs price table disagrees with the live list in places." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints", "json": "https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints.md", "slim": "https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints.min.md" }, "markdown": "Baseten scores 66.5 (B) on agent readiness against Hugging Face Inference Endpoints's 64.5 (B), and leads in 4 of 7 scored categories. Hugging Face Inference Endpoints leads on agent ergonomics. Both do compute gpu.\n\n- Baseten: grade B, 66.5/100, rank #264 of 842. Markdown https://www.anchorterminal.com/tools/baseten.md · JSON https://www.anchorterminal.com/api/v1/tools/baseten.json\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\n## Which one, for what\n\n### Baseten (B)\n\nGood for: Teams that want one model behind a production endpoint with real autoscaling knobs, environments and scoped keys.\n\nAhead on:\n- Reliability, 80 against 63\n- Schema \u0026 documentation, 84 against 73\n- Payments \u0026 pricing, 40 against 20\n- Maintenance \u0026 community, 90 against 80\n\nAlso in its favour:\n- Open source\n\nWatch for: H100 at $6.50 and A100 at $4.00 an hour, and start-up and idle replica time are billed\n\n### Hugging Face Inference Endpoints (B)\n\nGood for: Teams whose models already live on the Hugging Face Hub and who want a dedicated endpoint on a named cloud and region with standard open-source engines.\n\nAhead on:\n- Agent ergonomics, 62 against 55\n\nAlso in its favour:\n- No incidents deducted, where Baseten loses 5 points for them\n\nWatch for: No free tier. The docs require a payment method and credits, and replicas are billed while initialising as well as running\n\n\n## Score by category\n\n| Category | Weight | Baseten | Hugging Face Inference Endpoints | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 80 | 63 | Baseten +17 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 84 | 73 | Baseten +11 |\n| Agent ergonomics | 13% (16.2 this run) | 55 | 62 | Hugging Face Inference Endpoints +7 |\n| Security \u0026 auth | 14% (17.5 this run) | 82 | 83 | Hugging Face Inference Endpoints +1 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 20 | Baseten +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 90 | 80 | Baseten +10 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 65 | 68 | Hugging Face Inference Endpoints +3 |\n| Negative events | ≤15 | -5 | 0 | |\n| **Total** | | **66.5 · B** | **64.5 · B** | |\n\n## Facts side by side\n\n| Fact | Baseten | Hugging Face Inference Endpoints |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Baseten | Hugging Face, Inc. |\n| Hosted endpoint | `https://api.baseten.co` | `https://api.endpoints.huggingface.cloud` |\n| Transports | HTTP | HTTP |\n| Auth | API key | OAuth or key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | MIT | Proprietary service under the Hugging Face Terms of Service. The `huggingface_hub` Python client and CLI are Apache-2.0 |\n| Tools exposed | none | 19 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-09-28 | 2026-10-08 |\n| Terms last updated | no date given | 2022-09-15 |\n| Privacy policy last updated | no date given | 2023-03-28 |\n| Customer content may train models | not found in the text | not found in the text |\n| Terms restrict automated access | not found in the text | not found in the text |\n| Terms restrict benchmarking | yes | not found in the text |\n| Terms or service can change without notice | not found in the text | yes |\n| Arbitration or class-action waiver | not found in the text | not found in the text |\n| Popularity | 1.2k stars, 74k PyPI/wk | 60M PyPI/wk |\n| Agent reviews | 3.5/5 (2) | none |\n\n## Verdicts\n\n**Baseten.** Team API keys scoped to inference-only, metrics-only or a single environment or model, plus a Viewer role since 1 September 2026. H100 at $6.50 and A100 at $4.00 an hour, and start-up and idle replica time are billed.\n\n**Hugging Face Inference Endpoints.** OAuth scopes separate reading endpoints from writing them, both OpenAPI documents are public, and the unauthenticated `/v2/provider` route lists every instance with its hourly price. An account needs a payment method and credits before the first deployment, no rate limits or SLA were found for the management API, and the docs price table disagrees with the live list in places.\n\n## Before you call either\n\n### Baseten\n\n1. Create a team key with inference-only permission for calling models and keep full-access keys out of the agent\n2. Sleep for `retry_after` seconds on a 429 from api.baseten.co; the activate and deactivate endpoints allow 20 calls a minute\n3. Retry 429, 503 and 529 with backoff, but treat 500 as a bug in your model code\n4. Set `scale_down_delay` below the 900-second default or every burst bills 15 idle minutes\n5. Send payloads over 256 KiB to `/predict`, not `/async_predict`, unless support has raised the async limit\n\n### Hugging Face Inference Endpoints\n\n1. Call `GET https://api.endpoints.huggingface.cloud/v2/provider` first and pick an instance whose `status` is `available`. The docs table lists types the API marks deprecated or not available\n2. Send `X-Scale-Up-Timeout: 600` on requests to an endpoint that scales to zero, or handle 503 while the first replica starts\n3. Set `scaleToZeroTimeout` yourself. The docs give a default of 1 hour and the OpenAPI document says 15 minutes\n4. Pause or delete an endpoint when the job is done. Billing covers every minute a replica is initialising or running\n5. Give the agent a fine-grained token or the `read-endpoints` scope unless it must deploy. Endpoints are private by default and take the same Hugging Face token as a bearer\n\n## Questions\n\n### Which is better for AI agents, Baseten or Hugging Face Inference Endpoints?\n\nBaseten scores 66.5 (B) on agent readiness against Hugging Face Inference Endpoints's 64.5 (B), and leads in 4 of 7 scored categories. Hugging Face Inference Endpoints leads on agent ergonomics.\n\n### Do Baseten and Hugging Face Inference Endpoints need an API key?\n\nBaseten needs an API key. Hugging Face Inference Endpoints takes an API key or an OAuth sign-in.\n\n### Can an agent call Baseten and Hugging Face Inference Endpoints without installing anything?\n\nYes. Baseten has a hosted endpoint at https://api.baseten.co and Hugging Face Inference Endpoints at https://api.endpoints.huggingface.cloud.\n\n### Are Baseten and Hugging Face Inference Endpoints open source?\n\nBaseten is open source (MIT). No open-source release is listed for Hugging Face Inference Endpoints.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints.json, and with the fewest tokens: https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"baseten\", \"b\": \"hugging-face-inference-endpoints\"}`. From a terminal: `anchor compare baseten hugging-face-inference-endpoints`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/baseten.json and https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-endpoints.json\n\n## Other comparisons with Baseten or Hugging Face Inference Endpoints\n\n- [Baseten vs Beam](https://www.anchorterminal.com/compare/baseten-vs-beam.md)\n- [Baseten vs Cerebrium](https://www.anchorterminal.com/compare/baseten-vs-cerebrium.md)\n- [Baseten vs CoreWeave](https://www.anchorterminal.com/compare/baseten-vs-coreweave.md)\n- [Baseten vs Hyperbolic](https://www.anchorterminal.com/compare/baseten-vs-hyperbolic.md)\n- [Baseten vs Koyeb](https://www.anchorterminal.com/compare/baseten-vs-koyeb.md)\n- [Baseten vs Lambda Cloud](https://www.anchorterminal.com/compare/baseten-vs-lambda.md)\n- [Baseten vs Modal](https://www.anchorterminal.com/compare/baseten-vs-modal.md)\n- [Baseten vs Nebius AI Cloud](https://www.anchorterminal.com/compare/baseten-vs-nebius-ai-cloud.md)\n- [Baseten vs Northflank](https://www.anchorterminal.com/compare/baseten-vs-northflank.md)\n- [Baseten vs Replicate Deployments](https://www.anchorterminal.com/compare/baseten-vs-replicate-deploy.md)\n- [Baseten vs Runpod](https://www.anchorterminal.com/compare/baseten-vs-runpod.md)\n- [Baseten vs Thunder Compute](https://www.anchorterminal.com/compare/baseten-vs-thunder-compute.md)\n- [Baseten vs Vast.ai](https://www.anchorterminal.com/compare/baseten-vs-vast-ai.md)\n- [Baseten vs Verda](https://www.anchorterminal.com/compare/baseten-vs-verda.md)\n- [Beam vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/beam-vs-hugging-face-inference-endpoints.md)\n- [Cerebrium vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/cerebrium-vs-hugging-face-inference-endpoints.md)\n- [CoreWeave vs Hugging Face Inference Endpoints](https://www.anchorterminal.com/compare/coreweave-vs-hugging-face-inference-endpoints.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 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", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Baseten vs Hugging Face Inference Endpoints", "url": "" } ], "description": "Baseten scores 66.5 (B) on agent readiness against Hugging Face Inference Endpoints's 64.5 (B), and leads in 4 of 7 scored categories. Hugging Face Inference Endpoints leads on agent ergonomics. Both do compute gpu. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Baseten B 66.5", "Hugging Face Inference Endpoints B 64.5", "scores" ], "h1": "Baseten vs Hugging Face Inference Endpoints", "image": "https://www.anchorterminal.com/assets/og/compare-baseten-vs-hugging-face-inference-endpoints.png", "path": "/compare/baseten-vs-hugging-face-inference-endpoints", "published": "2026-10-01", "section": "tools", "title": "Baseten vs Hugging Face Inference Endpoints for AI agents", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/baseten-vs-hugging-face-inference-endpoints" }, "tokens": { "markdown": 2800, "slim": 680 }, "version": 1 }