{ "data": { "a": { "slug": "hugging-face-inference-providers", "name": "Hugging Face Inference Providers", "vendor": "Hugging Face, Inc.", "vendorUrl": "https://huggingface.co", "kind": "router", "category": "inference", "summary": "Hugging Face's serverless router that sends model calls to partner inference providers such as Groq, Cerebras, Together and Fireworks through one Hugging Face token, with an OpenAI-compatible API at router.huggingface.co/v1 and Python and JavaScript clients.", "url": "https://www.anchorterminal.com/tools/hugging-face-inference-providers", "markdownUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-providers.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-providers.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-providers.json", "dns": "hugging-face-inference-providers.grade.anchorterminal.com", "repo": "https://github.com/huggingface/huggingface_hub", "license": "Proprietary service under the Hugging Face Terms of Service, which name Inference Providers. The `huggingface_hub` Python client is Apache-2.0 and `@huggingface/inference` is MIT", "transports": [ "http" ], "remoteUrl": "https://router.huggingface.co/v1", "packages": [ { "registry": "pypi", "name": "huggingface_hub" }, { "registry": "npm", "name": "@huggingface/inference" } ], "auth": "api-key", "authNotes": "Hugging Face user access token sent as `Authorization: Bearer $HF_TOKEN`. The docs recommend a fine-grained token with only the `Make calls to Inference Providers` permission. Tokens are made in account settings in a browser, can be rotated and revoked, and a leaked token can be revoked by anyone through `POST /api/credentials/revoke`. A provider's own key can be stored in settings instead, and Hugging Face swaps it in when routing.", "pricing": "usage", "pricingNotes": "Pay as you go at each provider's own per-token rates with no Hugging Face markup, drawn from prepaid credits on the Hugging Face account. Free users get no included credits since 7 October 2026 and must buy credits. PRO users and Team or Enterprise seats get $2.00 a month of compute credits. Requests with a custom provider key are billed by the provider (https://huggingface.co/docs/inference-providers/pricing, checked 2026-11-06).", "priceSummary": "$2 / mo", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the Inference Providers docs, llms.txt or the router's model list. Calls draw on prepaid Hugging Face credits bought in the account's billing settings (checked 2026-11-06).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": 483470, "pypiWeekly": 71518708, "asOf": "2026-11-06" }, "docsUrl": "https://huggingface.co/docs/inference-providers/index", "llmsTxt": "https://huggingface.co/docs/inference-providers/llms.txt", "capabilities": [ "inference.router", "inference.open-weights", "inference.llm" ], "tags": [ "hosted", "router", "usage", "prepaid", "openai-compatible", "llms-txt", "python", "javascript", "status-page", "soc2" ], "lastRelease": "2026-10-29", "graded": true, "anchor": { "graded": true, "score": 54, "grade": "C", "agentReady": false, "rank": 926, "ranked": true, "rankOf": 1510, "categoryRank": 28, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 58, "maintenance": 80, "payments": 20, "reliability": 25, "schema": 62, "security": 79, "transparency": 71 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": 0, "verdict": "One Hugging Face token reaches open models on 18 partner providers at the providers' own prices, with a public model list that shows each provider's price, context and tool support. Router rate limits, error responses and its own status component were not found in the reviewed documentation, and free accounts have had no included credits since 7 October 2026.", "bestFor": "Agents and developers who want open-weight models from several hosts on one token and one bill, with a public price and capability list per provider.", "strengths": [ "router.huggingface.co/v1/models lists every routed model with each provider's price, context, tool support, latency and throughput, without a token", "Provider choice by suffix (`:fastest`, `:cheapest`, `:preferred` or a provider name) on an OpenAI-compatible endpoint", "Fine-grained tokens can be limited to the `Make calls to Inference Providers` permission and revoked at once", "No markup on provider rates, and request bodies and responses are not stored when routed", "Official clients in Python (`huggingface_hub` 2.4.0, 29 October 2026) and JavaScript (`@huggingface/inference` 4.13.32)" ], "weaknesses": [ "No rate limits, 429 behaviour or error responses documented for the router", "status.huggingface.co has no component for Inference Providers or the router", "Free users have had no included credits since 7 October 2026, and buying credits needs a person in the account settings", "No OpenAPI document for the router, no batch API, and the Responses API is beta", "Data handling past the router depends on each provider's own policy" ], "agentNotes": [ "Read https://router.huggingface.co/v1/models before choosing a provider; it shows price, context and `supports_tools` per provider", "Pin a provider with `model:\u003cprovider\u003e` when tool calling or structured output matters, since support varies by provider", "Use a fine-grained token with only the Inference Providers permission, and send `X-HF-Bill-To` to bill an organisation", "Back off on 429 without assuming a Retry-After header, since none is documented for the router" ], "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": 54 } ], "editorialScores": { "ergonomics": 58, "maintenance": 80, "payments": 20, "reliability": 25, "schema": 62, "security": 79, "transparency": 58 }, "provenanceScore": 84, "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": "pip install huggingface_hub # or: pip install openai, with base_url https://router.huggingface.co/v1", "http": "curl https://router.huggingface.co/v1/chat/completions \\\n -H \"Authorization: Bearer $HF_TOKEN\" -H \"Content-Type: application/json\" \\\n -d '{\"model\":\"openai/gpt-oss-120b:fastest\",\"messages\":[{\"role\":\"user\",\"content\":\"How many Gs in huggingface?\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.router", "tool": "https://letme.dev/hugging-face-inference-providers" }, "sameCompany": [ "hugging-face-inference-endpoints", "smolagents", "carbon-x", "smollm3-3b" ], "area": "models", "unitPrices": [ { "item": "openai/gpt-oss-120b input, cheapest provider listed (DeepInfra)", "unit": "1m-tokens", "usd": 0.037, "note": "Output $0.17. Groq lists $0.15/$0.75 and Cerebras $0.35/$0.75, per the router's model list of 6 November 2026" }, { "item": "PRO monthly compute credits", "unit": "month", "usd": 2, "note": "Also $2.00 per seat for Team and Enterprise organisations" } ], "provenance": { "legalEntity": "Hugging Face, Inc.", "domain": "huggingface.co", "domainRegistered": "", "endpointOnVendorDomain": true, "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": "2026-11-06", "notes": [ "The Terms of Service (effective 15 September 2022) name Hugging Face, Inc. and define Inference Providers as a hosted service for running inference through an API on Hub models.", "huggingface.co/robots.txt and router.huggingface.co/robots.txt allow all. status.huggingface.co serves an empty robots.txt.", "status.huggingface.co is a Better Stack page with components for the Hub, git hosting, Inference Endpoints, Spaces, the CDNs and Jobs, and none for Inference Providers or the router.", "huggingface.co/changelog covers the whole Hub and had no Inference Providers entry on its first page on 6 November 2026. Dated changes to the docs are in the hub-docs repository's history.", "security.txt, privacy policy and subprocessor list as recorded on the Inference Endpoints listing (checked 8 October and 2 November 2026); not re-read for this listing." ], "score": 84 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-providers.json", "live": { "slug": "hugging-face-inference-providers", "probe": { "target": "https://router.huggingface.co/v1", "method": "get", "lastAt": "2026-11-07T19:22:12.474650545Z", "lastOk": true, "lastStatus": 404, "lastMs": 145, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 109, "p95ms24h": 196, "samples24h": 210, "samples30d": 210, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 13 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "vendorStatus": { "page": "https://status.huggingface.co", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-07T04:47:55.542550557Z" }, "versions": [ { "registry": "github", "name": "huggingface/huggingface_hub", "version": "v2.4.0", "released": "2026-10-29", "seenAt": "2026-11-06T23:33:27.894225298Z" }, { "registry": "npm", "name": "@huggingface/inference", "version": "4.13.32", "seenAt": "2026-11-06T23:33:27.102558197Z" }, { "registry": "pypi", "name": "huggingface_hub", "version": "2.4.0", "released": "2026-10-29", "seenAt": "2026-11-06T23:33:26.910773744Z" } ], "githubStars": 4034, "npmWeekly": 483470, "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-providers/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:44.910268229Z" }, "pages": [ { "url": "https://huggingface.co/docs/inference-providers/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-07T14:11:37.221950067Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "2cbe821ca9d2" } ], "updatedAt": "2026-11-07T19:22:12.474650545Z" } }, "answer": "LiteLLM scores 62.4 (B) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in 5 of 7 scored categories. Hugging Face Inference Providers leads on security \u0026 auth and transparency \u0026 trust.", "b": { "slug": "litellm", "name": "LiteLLM", "vendor": "BerriAI", "vendorUrl": "https://www.litellm.ai", "kind": "router", "category": "inference", "summary": "Open-source Python SDK and self-hosted AI gateway from BerriAI that puts 100+ model providers behind one OpenAI-compatible API, with virtual keys, spend tracking, budgets, fallbacks, guardrails and an MCP gateway.", "url": "https://www.anchorterminal.com/tools/litellm", "markdownUrl": "https://www.anchorterminal.com/tools/litellm.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/litellm.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/litellm.json", "dns": "litellm.grade.anchorterminal.com", "repo": "https://github.com/BerriAI/litellm", "license": "MIT, except the enterprise/ directory, which is under its own licence and needs an Enterprise licence key", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "litellm" }, { "registry": "docker", "name": "docker.litellm.ai/berriai/litellm" } ], "auth": "api-key", "authNotes": "The gateway takes a Bearer key. `LITELLM_MASTER_KEY` is required at start-up and has full admin rights. Virtual keys, stored hashed, carry model, route, MCP server, budget, rate-limit and expiry limits and can be blocked. Key rotation with a grace period, SSO beyond five users, SCIM and JWT/OIDC auth need an Enterprise licence. `/key/info` takes the key being looked up as a `?key=` query parameter.", "pricing": "freemium", "pricingNotes": "Free to self-host under MIT, with no account or card. Model calls bill at each upstream provider's own prices on the operator's keys. Enterprise (SSO, audit logs, key rotation, team-scoped guardrails, support SLAs) is an annual licence priced through sales, with a 30-day trial key emailed after a form and no card (https://www.litellm.ai/pricing, https://www.litellm.ai/enterprise).", "priceSummary": "Freemium", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs index or the llms-full.txt file, read 2026-10-06. The gateway forwards to providers on the operator's keys.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 62841, "npmWeekly": null, "pypiWeekly": 22493278, "asOf": "2026-11-06" }, "docsUrl": "https://docs.litellm.ai", "llmsTxt": "https://docs.litellm.ai/llms.txt", "openapi": "https://docs.litellm.ai/api-reference/", "capabilities": [ "inference.router", "obs.gateway", "agent.mcp-client", "guard.pii" ], "tags": [ "self-hosted", "open-source", "router", "gateway", "mcp", "openapi", "llms-txt", "python", "docker", "bug-bounty", "soc2" ], "lastRelease": "2026-11-05", "graded": true, "anchor": { "graded": true, "score": 62.4, "grade": "B", "agentReady": false, "rank": 559, "ranked": true, "rankOf": 1510, "categoryRank": 20, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 79, "maintenance": 82, "payments": 60, "reliability": 70, "schema": 91, "security": 72, "transparency": 63 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": -12, "negativeNotes": [ "2026-03-24, -6. litellm 1.82.7 and 1.82.8 were uploaded to PyPI with a credential stealer that read environment variables, SSH keys, cloud and Kubernetes credentials and sent them to models.litellm.cloud. They were live for about 40 minutes before PyPI quarantined them, after a token leaked through the Trivy compromise. BerriAI published indicators, checksums and a rebuilt pipeline (v1.83.0, 30 March). A supply-chain incident that reached users, fixed, documented and six months old. https://docs.litellm.ai/blog/security-update-march-2026, https://api.osv.dev/v1/vulns/GHSA-5mg7-485q-xm76", "2026-04-24, -2. GHSA-r75f-5x8p-qvmc (CVE-2026-42208, critical). An unauthenticated SQL injection through the `Authorization` header in proxy key verification, affecting 1.81.16 up to 1.83.7. Fixed and published, inside six months. https://api.osv.dev/v1/vulns/GHSA-r75f-5x8p-qvmc", "2026-06-16, -2. GHSA-4xpc-pv4p-pm3w (CVE-2026-49468, critical). A crafted Host header could reach protected management routes without authentication where nothing upstream normalises Host. Fixed in 1.84.0. https://api.osv.dev/v1/vulns/GHSA-4xpc-pv4p-pm3w", "2026-08-27, -2. GHSA-6wvf-77m9-58rm (CVE-2026-37004, critical). Server-side template injection in /prompts/test let unauthenticated callers run OS commands in 1.82.4 and earlier, fixed in 1.83.7. Published months after the fix. https://api.osv.dev/v1/vulns/GHSA-6wvf-77m9-58rm", "Not deducted separately. 26 other GitHub advisories published between 6 November 2025 and 6 November 2026 (one more critical, ten high, six moderate and nine low), among them an OIDC cache-key collision (GHSA-jjhc-v7c2-5hh6), authenticated credential exfiltration through request-body routing (GHSA-3cv6-jpf6-8222, 30 September 2026) and, on 27 October 2026, privilege escalation from internal_user to proxy_admin through reuse of the salt key (GHSA-7hp6-4w63-5g45, high) and an arbitrary local file read by internal_user_viewer (GHSA-g5ff-637f-6q2m, high). All handled in public with fixes. https://api.osv.dev/v1/query" ], "verdict": "An MIT-licensed gateway that puts 100+ model providers behind one OpenAI-compatible API, with virtual keys, budgets, fallbacks and an MCP gateway, released several times a week. Its security record is the main limitation. Two PyPI releases carried a credential stealer on 24 March 2026, and 30 GitHub advisories followed in 12 months, five of them critical.", "bestFor": "Teams that want one OpenAI-compatible endpoint over many providers on their own infrastructure, with per-key budgets, model allowlists and an MCP gateway.", "strengths": [ "MIT core, self-hosted, with no telemetry when self-hosted per the data security page", "Virtual keys limited by model, route, MCP server, budget, rate and expiry, and blockable at once", "Every proxy serves Swagger, ReDoc and /openapi.json, and the docs have llms.txt with a Markdown twin of every page", "Error reference tells a gateway 429 (with `retry-after`, `rate_limit_type`, `reset_at`) from a provider 429", "v1.108.1 on 5 November 2026, 125 tags since 8 August, and four stable minor lines supported with patches" ], "weaknesses": [ "litellm 1.82.7 and 1.82.8 on PyPI carried a credential stealer for about 40 minutes on 24 March 2026", "30 GitHub advisories from 6 November 2025 to 6 November 2026, five critical, including unauthenticated SQL injection fixed in 1.83.7", "Key rotation, audit logs and SSO beyond five users need an Enterprise licence, priced only through sales", "Breaking changes ship in weekly minor releases on the 1.x line, and lines older than the latest four stop getting fixes", "The default Docker image runs as root, and the Swagger, ReDoc and OpenAPI pages are public until switched off" ], "agentNotes": [ "Pin an exact litellm version or image digest, verify the cosign signature, and never install an unpinned latest in CI", "Give each agent a virtual key limited to its models, routes and MCP servers, with a budget and an expiry", "Treat a 429 with `retry-after` as the gateway's own limit and wait that long; a 429 without it came from the provider", "Set `NO_DOCS`, `NO_REDOC` and `NO_OPENAPI` to True in production, and use the `-non_root` image variant", "Set `LITELLM_LOCAL_MODEL_COST_MAP=True` where egress is blocked, since start-up otherwise fetches the price map from GitHub" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.4 } ], "editorialScores": { "ergonomics": 79, "maintenance": 82, "payments": 60, "reliability": 70, "schema": 91, "security": 72, "transparency": 77 }, "provenanceScore": 49, "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": "pip install litellm # SDK; gateway: curl -sSLO https://github.com/BerriAI/litellm/raw/main/docker/docker-compose.quickstart.yml \u0026\u0026 docker compose -f docker-compose.quickstart.yml up -d", "http": "curl http://localhost:4000/v1/chat/completions \\\n -H 'Authorization: Bearer sk-\u003cyour-virtual-key\u003e' \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"model\": \"gpt-5.6-terra\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Say hello in five words.\"}]\n }'", "claudeCode": "export ANTHROPIC_BASE_URL=\"http://localhost:4000\"\nexport ANTHROPIC_AUTH_TOKEN=\"sk-\u003cyour-virtual-key\u003e\"\nexport ANTHROPIC_MODEL=\"\u003ca model_name from your config\u003e\"\nclaude" }, "letme": { "capability": "https://letme.dev/inference.router", "tool": "https://letme.dev/litellm" }, "area": "models", "provenance": { "legalEntity": "Berrie AI Incorporated", "domain": "litellm.ai", "domainRegistered": "2023-08-07", "endpointOnVendorDomain": false, "terms": "", "privacy": "https://docs.litellm.ai/docs/data_security", "statusPage": "", "changelog": "https://docs.litellm.ai/release_notes", "securityTxt": "none", "checked": "2026-11-06", "notes": [ "The data security page names the legal entity as Berrie AI Incorporated, with krrish@berri.ai as the security contact.", "The gateway is software the operator runs, so there's no vendor endpoint and no status page.", "No terms of service or privacy policy found on www.litellm.ai (/terms, /privacy and /legal return 404). The data security page in the docs covers self-hosted data handling.", "www.litellm.ai/.well-known/security.txt and the docs host both return 404. Reports go to GitHub private vulnerability reporting under security.md.", "RDAP for litellm.ai gives a registration date of 2023-08-07.", "Rechecked 2026-11-06: www.litellm.ai/terms, /privacy, /legal, /terms-of-service and /privacy-policy all return 404, and neither host serves security.txt. Both robots.txt files allow all." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/litellm.json", "live": { "slug": "litellm", "versions": [ { "registry": "github", "name": "BerriAI/litellm", "version": "v1.108.2", "released": "2026-11-06", "seenAt": "2026-11-06T23:40:24.985518384Z" }, { "registry": "pypi", "name": "litellm", "version": "1.108.2", "released": "2026-11-06", "seenAt": "2026-11-06T23:40:24.735967257Z" } ], "githubStars": 62845, "pypiWeekly": 22493278, "securityTxt": { "url": "https://litellm.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:01:25.60777972Z" }, "llmsTxt": { "url": "https://docs.litellm.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:52.845519783Z" }, "pages": [ { "url": "https://docs.litellm.ai/release_notes", "kind": "changelog", "status": 200, "checkedAt": "2026-11-07T14:06:55.608946935Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "e493aadf92b2" }, { "url": "https://www.litellm.ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-07T14:30:56.79408028Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "45e60222905c" }, { "url": "https://docs.litellm.ai/docs/data_security", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:06:53.531435979Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "a2363610f7ad" } ], "updatedAt": "2026-11-07T16:01:25.60777972Z" } }, "edges": [ { "by": 45, "category": "reliability", "leads": "litellm", "name": "Reliability", "reasons": { "a": "Hosted lines. status.huggingface.co is a Better Stack page with 90-day component history for the Hub, Inference Endpoints, Spaces, the CDNs and Jobs, but no component for Inference Providers or the router (10 of 20). With no component, there is no readable incident history for the surface agents call, and the feed's 23 items from 10 June to 7 October 2026 don't name it (5). No rate limits for the router were found; the Hub rate-limits page gives numbers for Hub APIs, resolvers and pages only (0). No 429 or overload guidance for the router was found (0). No SLA found (0). Chat completions on the router is generally available; the Responses API is labelled beta (10). Routing between providers with `:fastest` or `:cheapest` softens a single provider's outage, which this checklist doesn't score.", "b": "Read with the local-software lines, since the gateway runs on the operator's hardware and BerriAI hosts nothing an agent calls. litellm on PyPI states Python 3.10 to 3.14, and the gateway ships as a Docker image, a Helm chart and Terraform modules, with GHCR images signed by cosign (20). 51 GitHub Actions workflows plus a CircleCI config cover unit, proxy-database, end-to-end, chaos, mutation and UI tests. The GitHub API was rate-limited for our reader on 6 November and Actions pages refused it on 6 October, so we couldn't confirm main is passing (15 of 25). The issue tracker showed 1,674 open issues on 6 November, with eight bug reports among the 15 newest, seven of them with a reply, against a project that tags several releases a day (10 of 25, down from an unchecked 12). Release notes carry breaking-change bullets, but breaking changes land in weekly minor releases on the 1.x line, so version numbers don't signal them (10 of 15). 1.108.1, past 1.0 (15)." }, "scores": { "a": 25, "b": 70 } }, { "by": 40, "category": "payments", "leads": "litellm", "name": "Payments \u0026 pricing", "reasons": { "a": "No x402, MPP or L402 (0). Per-token prices for every model and provider published without a login in the router's /v1/models and on model pages (20). No free tier since 7 October 2026, when free users lost their included monthly credits; PRO's $2.00 of credits needs a subscription (0). Sign-up, token creation and credit purchase are browser steps (0).", "b": "Read with the self-hosted rule, since an agent uses the free gateway and Enterprise is a sales-led licence for the same software. No x402, MPP or L402 (0). MIT with no account and no card, so 20, 20 and 20 on the last three lines. Model calls cost what the operator's provider keys cost." }, "scores": { "a": 20, "b": 60 } }, { "by": 29, "category": "schema", "leads": "litellm", "name": "Schema \u0026 documentation", "reasons": { "a": "No OpenAPI document for the router (openapi.json paths answer 404). The task reference pages are generated from typed JSON Schema specs in huggingface.js, and the router's /v1/models gives a machine-readable catalogue, so 12 of 25. llms.txt for the Inference Providers docs and a Markdown twin of every page via `.md` (10). Guides say what each task and policy is for, with a quick setup page for coding agents (14 of 20). Parameter tables with types for chat completion, though `response_format` and `tool_choice` show as unknown types (9 of 15). Examples in Python, JavaScript and curl on every task page, but no documented error responses for the router (9 of 15). No changelog of its own; the Hub changelog doesn't cover it and dated changes are only in the hub-docs history (8 of 15).", "b": "Every gateway serves Swagger at /, ReDoc at /redoc and the raw schema at /openapi.json, and the docs host a public Swagger explorer (25). llms.txt, llms-full.txt and a Markdown twin of every page via `.md` (10). Pages say what a feature is for and where its limits are, and the key-auth page explains how permissions are inherited and where they aren't, but the reference is very large for a model to navigate (16 of 20). OpenAI-shaped request schemas from Pydantic models, with `metadata`, `litellm_params` and config blocks left as free-form objects (10 of 15). A full error reference with the envelope, every status, response headers and how to tell gateway limits from provider limits, plus examples on every page (15). Release notes per version with dates, newest v1.108.0 and v1.106.7 on 4 November 2026 (15)." }, "scores": { "a": 62, "b": 91 } }, { "by": 21, "category": "ergonomics", "leads": "litellm", "name": "Agent ergonomics", "reasons": { "a": "Router reading of the model checklist (tool use, structured output, caching, context, batch, SDKs, errors), as for openrouter. Tool calling through the OpenAI-compatible API with `tool_choice`, support flagged per provider in /v1/models (15 of 20). Structured output documented, support flagged per provider and false on some (10 of 15). No prompt caching guidance found, though the Enterprise usage API counts cached input tokens (3 of 15). Routes to models with up to 1,048,576 tokens of context per the model list, a judgement call since a router has no default model (15). No batch API (0 of 10). Official clients in Python and JavaScript, plus any OpenAI SDK (10). Errors not documented (5 of 15).", "b": "Read with the API lines for the gateway an agent calls. Responses size through the usual `max_tokens` and streaming, and the MCP gateway can replace a large tool catalogue with four virtual tools (tool search) or filter tools by semantic relevance, which cuts context cost (20 of 25). Key, team and spend listings page and filter, though we didn't check every list call (15 of 20). Errors keep the OpenAI envelope with a string `code`, gateway 429s carry `retry-after`, `rate_limit_type` and `reset_at`, management routes answer RFC 9457 problem+json, and a table says which errors are safe to retry (20). Retries, cooldowns and fallbacks run inside the gateway and the docs say which errors to retry, but there are no idempotency keys for the management writes (12 of 20). Any OpenAI or Anthropic SDK works by changing the base URL, but the only first-party client is the Python SDK, and keys and spend need Postgres and a master key before the first call (12 of 15)." }, "scores": { "a": 58, "b": 79 } }, { "by": 8, "category": "transparency", "leads": "hugging-face-inference-providers", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed service under Terms of Service that name Inference Providers, with an Apache-2.0 Python client and an MIT JavaScript client (20). The security page and the docs agree on no training, no stored bodies and 30-day logs, but the privacy policy dates from 28 March 2023 and each provider's own data policy governs the rest (18 of 30). No deprecation policy found. Provider support changes and the 7 October removal of free credits are dated only in the docs repository history (5 of 20). The privacy policy lists 11 subprocessors with countries per the Inference Endpoints check, and the docs name every provider a request can reach, though not where each runs (15 of 20).", "b": "MIT for everything outside enterprise/, which has its own licence (25 of 30). The data security page (updated 6 November 2026) and security.md agree that a self-hosted gateway sends no data or telemetry to BerriAI, and the gateway stores prompts and spend in the operator's own Postgres. We found no terms of service or privacy policy on litellm.ai for its site, trial or Enterprise licence (20 of 30). A version support policy since 29 June 2026 (four stable minor lines), deprecated pages labelled in the docs, and release notes with breaking-change bullets (15 of 20). Usage telemetry, new in v1.107.0, is off by default, lists every field it can send and goes only to a table or endpoint the operator sets. A main-branch commit of 23 October 2026 made the Admin UI chat post a hashed user id to chat-telemetry.litellm.ai, and it was removed on 24 October before any tagged release contained it, so it isn't deducted. The start-up fetch of the price map from GitHub is documented with an opt-out (17 of 20)." }, "scores": { "a": 71, "b": 63 } }, { "by": 7, "category": "security", "leads": "hugging-face-inference-providers", "name": "Security \u0026 auth", "reasons": { "a": "Model reading. Fine-grained user access tokens that can be limited to the `Make calls to Inference Providers` permission, rotated and revoked, plus a public revocation endpoint for leaked tokens, and organisation approval of fine-grained tokens on paid plans (30). Hugging Face says it doesn't store user data for training, but data handling past the router follows each provider's policy, with no per-request filter to exclude providers that train or retain (12 of 20). Request bodies and responses aren't stored when routed and debug logs are kept up to 30 days without user data or tokens, with provider retention outside that statement (10 of 15). Usage by model and provider in account settings, an Enterprise usage API, and Team and Enterprise admins can disable providers and set a spending limit (13 of 15). SOC 2 Type 2 for the Hub, of which the docs say Inference Providers is a feature, and security.txt valid to 2030 per the Inference Endpoints check; no bug bounty found (14 of 20).", "b": "Read with the tool checklist for self-hosted software. Virtual keys are hashed, revocable and blockable, with model, route, MCP server, budget, rate and expiry limits, and a `proxy_admin_viewer` read-only role exists. Rotation with a grace period is Enterprise only, so 25, less 10 because the docs pass a key as `?key=` on `/key/info` and on the Claude Code marketplace manifest (15 of 30). `allowed_routes`, MCP permissions by key and team, an approval queue for submitted MCP servers and stdio MCP servers off by default from 1.104.0 (15 of 20). Guardrails can run before a model call and on MCP tool results, and an OWASP LLM Top 10 page maps each risk to a control, but prompt-injection detectors such as Lakera need an Enterprise licence and all of it is off by default (12 of 15). Request and spend logs per key in the open-source build; audit logs of admin actions are Enterprise only (12 of 15). security.md with GitHub private reporting, a bug bounty from $500 to $3,000 for P0 and P1 reports, a security announcements list, SOC 2 Type II per the data security page, CodeQL, OSV and Scorecard workflows, and advisories published on GitHub. No security.txt, and reports without a video are closed unread (18 of 20)." }, "scores": { "a": 79, "b": 72 } } ], "facts": [ { "a": "Model router", "b": "Model router", "name": "Kind" }, { "a": "Hugging Face, Inc.", "b": "BerriAI", "name": "Vendor" }, { "a": "https://router.huggingface.co/v1", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary service under the Hugging Face Terms of Service, which name Inference Providers. The `huggingface_hub` Python client is Apache-2.0 and `@huggingface/inference` is MIT", "b": "MIT, except the enterprise/ directory, which is under its own licence and needs an Enterprise licence key", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-29", "b": "2026-11-05", "name": "Last release" }, { "a": "2022-09-15", "b": "no document linked", "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 read yet", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms restrict benchmarking" }, { "a": "yes", "b": "not read yet", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not read yet", "name": "Arbitration or class-action waiver" }, { "a": "483k npm/wk, 71.5M PyPI/wk", "b": "63k stars, 22.5M PyPI/wk", "name": "Popularity" } ], "faq": [ { "answer": "LiteLLM scores 62.4 (B) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in 5 of 7 scored categories. Hugging Face Inference Providers leads on security \u0026 auth and transparency \u0026 trust.", "question": "Which is better for AI agents, Hugging Face Inference Providers or LiteLLM?" }, { "answer": "Both need an API key.", "question": "Do Hugging Face Inference Providers and LiteLLM need an API key?" }, { "answer": "Hugging Face Inference Providers has a hosted endpoint at https://router.huggingface.co/v1. No hosted endpoint is listed for LiteLLM.", "question": "Can an agent call Hugging Face Inference Providers and LiteLLM without installing anything?" }, { "answer": "No open-source release is listed for Hugging Face Inference Providers. LiteLLM is open source (MIT, except the enterprise/ directory, which is under its own licence and needs an Enterprise licence key).", "question": "Are Hugging Face Inference Providers and LiteLLM open source?" } ], "goodFor": [ { "aheadOn": [ "Security \u0026 auth, 79 against 72", "Transparency \u0026 trust, 71 against 63" ], "also": [ "A hosted endpoint, with nothing to install", "No incidents deducted, where LiteLLM loses 12 points for them" ], "goodFor": "Agents and developers who want open-weight models from several hosts on one token and one bill, with a public price and capability list per provider.", "slug": "hugging-face-inference-providers", "watchFor": "No rate limits, 429 behaviour or error responses documented for the router" }, { "aheadOn": [ "Reliability, 70 against 25", "Schema \u0026 documentation, 91 against 62", "Agent ergonomics, 79 against 58", "Payments \u0026 pricing, 60 against 20" ], "also": [ "Open source" ], "goodFor": "Teams that want one OpenAI-compatible endpoint over many providers on their own infrastructure, with per-key budgets, model allowlists and an MCP gateway.", "slug": "litellm", "watchFor": "litellm 1.82.7 and 1.82.8 on PyPI carried a credential stealer for about 40 minutes on 24 March 2026" } ], "history": null, "job": { "capability": "inference.router", "name": "Model routing" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-hugging-face-inference-providers.json", "title": "Amazon Bedrock vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-hugging-face-inference-providers.json", "title": "Claude API vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-hugging-face-inference-providers" }, { "json": 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"https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-prism-inference" } ], "panel": null, "scores": [ { "by": 45, "edge": "litellm", "hugging-face-inference-providers": 25, "key": "reliability", "litellm": 70, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 29, "edge": "litellm", "hugging-face-inference-providers": 62, "key": "schema", "litellm": 91, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 21, "edge": "litellm", "hugging-face-inference-providers": 58, "key": "ergonomics", "litellm": 79, "name": "Agent ergonomics", "weight": 13 }, { "by": 7, "edge": "hugging-face-inference-providers", "hugging-face-inference-providers": 79, "key": "security", "litellm": 72, "name": "Security \u0026 auth", "weight": 14 }, { "by": 40, "edge": "litellm", "hugging-face-inference-providers": 20, "key": "payments", "litellm": 60, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 2, "edge": "litellm", "hugging-face-inference-providers": 80, "key": "maintenance", "litellm": 82, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 8, "edge": "hugging-face-inference-providers", "hugging-face-inference-providers": 71, "key": "transparency", "litellm": 63, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "LiteLLM scores 62.4 (B) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in 5 of 7 scored categories. Hugging Face Inference Providers leads on security \u0026 auth and transparency \u0026 trust. Both do model routing.", "tasks": null, "tutorials": [], "verdicts": { "hugging-face-inference-providers": "One Hugging Face token reaches open models on 18 partner providers at the providers' own prices, with a public model list that shows each provider's price, context and tool support. Router rate limits, error responses and its own status component were not found in the reviewed documentation, and free accounts have had no included credits since 7 October 2026.", "litellm": "An MIT-licensed gateway that puts 100+ model providers behind one OpenAI-compatible API, with virtual keys, budgets, fallbacks and an MCP gateway, released several times a week. Its security record is the main limitation. Two PyPI releases carried a credential stealer on 24 March 2026, and 30 GitHub advisories followed in 12 months, five of them critical." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-litellm", "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-litellm.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-litellm.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-litellm.min.md" }, "markdown": "LiteLLM scores 62.4 (B) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in 5 of 7 scored categories. Hugging Face Inference Providers leads on security \u0026 auth and transparency \u0026 trust. Both do model routing.\n\n- Hugging Face Inference Providers: grade C, 54/100, rank #926 of 1510. Markdown https://www.anchorterminal.com/tools/hugging-face-inference-providers.md · JSON https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-providers.json\n- LiteLLM: grade B, 62.4/100, rank #559 of 1510. Markdown https://www.anchorterminal.com/tools/litellm.md · JSON https://www.anchorterminal.com/api/v1/tools/litellm.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 830 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### Hugging Face Inference Providers (C)\n\nGood for: Agents and developers who want open-weight models from several hosts on one token and one bill, with a public price and capability list per provider.\n\nAhead on:\n- Security \u0026 auth, 79 against 72\n- Transparency \u0026 trust, 71 against 63\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n- No incidents deducted, where LiteLLM loses 12 points for them\n\nWatch for: No rate limits, 429 behaviour or error responses documented for the router\n\n### LiteLLM (B)\n\nGood for: Teams that want one OpenAI-compatible endpoint over many providers on their own infrastructure, with per-key budgets, model allowlists and an MCP gateway.\n\nAhead on:\n- Reliability, 70 against 25\n- Schema \u0026 documentation, 91 against 62\n- Agent ergonomics, 79 against 58\n- Payments \u0026 pricing, 60 against 20\n\nAlso in its favour:\n- Open source\n\nWatch for: litellm 1.82.7 and 1.82.8 on PyPI carried a credential stealer for about 40 minutes on 24 March 2026\n\n\n## Why the edge\n\n### Reliability: LiteLLM +45\n\n- Hugging Face Inference Providers (25): Hosted lines. status.huggingface.co is a Better Stack page with 90-day component history for the Hub, Inference Endpoints, Spaces, the CDNs and Jobs, but no component for Inference Providers or the router (10 of 20). With no component, there is no readable incident history for the surface agents call, and the feed's 23 items from 10 June to 7 October 2026 don't name it (5). No rate limits for the router were found; the Hub rate-limits page gives numbers for Hub APIs, resolvers and pages only (0). No 429 or overload guidance for the router was found (0). No SLA found (0). Chat completions on the router is generally available; the Responses API is labelled beta (10). Routing between providers with `:fastest` or `:cheapest` softens a single provider's outage, which this checklist doesn't score.\n- LiteLLM (70): Read with the local-software lines, since the gateway runs on the operator's hardware and BerriAI hosts nothing an agent calls. litellm on PyPI states Python 3.10 to 3.14, and the gateway ships as a Docker image, a Helm chart and Terraform modules, with GHCR images signed by cosign (20). 51 GitHub Actions workflows plus a CircleCI config cover unit, proxy-database, end-to-end, chaos, mutation and UI tests. The GitHub API was rate-limited for our reader on 6 November and Actions pages refused it on 6 October, so we couldn't confirm main is passing (15 of 25). The issue tracker showed 1,674 open issues on 6 November, with eight bug reports among the 15 newest, seven of them with a reply, against a project that tags several releases a day (10 of 25, down from an unchecked 12). Release notes carry breaking-change bullets, but breaking changes land in weekly minor releases on the 1.x line, so version numbers don't signal them (10 of 15). 1.108.1, past 1.0 (15).\n\n### Payments \u0026 pricing: LiteLLM +40\n\n- Hugging Face Inference Providers (20): No x402, MPP or L402 (0). Per-token prices for every model and provider published without a login in the router's /v1/models and on model pages (20). No free tier since 7 October 2026, when free users lost their included monthly credits; PRO's $2.00 of credits needs a subscription (0). Sign-up, token creation and credit purchase are browser steps (0).\n- LiteLLM (60): Read with the self-hosted rule, since an agent uses the free gateway and Enterprise is a sales-led licence for the same software. No x402, MPP or L402 (0). MIT with no account and no card, so 20, 20 and 20 on the last three lines. Model calls cost what the operator's provider keys cost.\n\n### Schema \u0026 documentation: LiteLLM +29\n\n- Hugging Face Inference Providers (62): No OpenAPI document for the router (openapi.json paths answer 404). The task reference pages are generated from typed JSON Schema specs in huggingface.js, and the router's /v1/models gives a machine-readable catalogue, so 12 of 25. llms.txt for the Inference Providers docs and a Markdown twin of every page via `.md` (10). Guides say what each task and policy is for, with a quick setup page for coding agents (14 of 20). Parameter tables with types for chat completion, though `response_format` and `tool_choice` show as unknown types (9 of 15). Examples in Python, JavaScript and curl on every task page, but no documented error responses for the router (9 of 15). No changelog of its own; the Hub changelog doesn't cover it and dated changes are only in the hub-docs history (8 of 15).\n- LiteLLM (91): Every gateway serves Swagger at /, ReDoc at /redoc and the raw schema at /openapi.json, and the docs host a public Swagger explorer (25). llms.txt, llms-full.txt and a Markdown twin of every page via `.md` (10). Pages say what a feature is for and where its limits are, and the key-auth page explains how permissions are inherited and where they aren't, but the reference is very large for a model to navigate (16 of 20). OpenAI-shaped request schemas from Pydantic models, with `metadata`, `litellm_params` and config blocks left as free-form objects (10 of 15). A full error reference with the envelope, every status, response headers and how to tell gateway limits from provider limits, plus examples on every page (15). Release notes per version with dates, newest v1.108.0 and v1.106.7 on 4 November 2026 (15).\n\n### Agent ergonomics: LiteLLM +21\n\n- Hugging Face Inference Providers (58): Router reading of the model checklist (tool use, structured output, caching, context, batch, SDKs, errors), as for openrouter. Tool calling through the OpenAI-compatible API with `tool_choice`, support flagged per provider in /v1/models (15 of 20). Structured output documented, support flagged per provider and false on some (10 of 15). No prompt caching guidance found, though the Enterprise usage API counts cached input tokens (3 of 15). Routes to models with up to 1,048,576 tokens of context per the model list, a judgement call since a router has no default model (15). No batch API (0 of 10). Official clients in Python and JavaScript, plus any OpenAI SDK (10). Errors not documented (5 of 15).\n- LiteLLM (79): Read with the API lines for the gateway an agent calls. Responses size through the usual `max_tokens` and streaming, and the MCP gateway can replace a large tool catalogue with four virtual tools (tool search) or filter tools by semantic relevance, which cuts context cost (20 of 25). Key, team and spend listings page and filter, though we didn't check every list call (15 of 20). Errors keep the OpenAI envelope with a string `code`, gateway 429s carry `retry-after`, `rate_limit_type` and `reset_at`, management routes answer RFC 9457 problem+json, and a table says which errors are safe to retry (20). Retries, cooldowns and fallbacks run inside the gateway and the docs say which errors to retry, but there are no idempotency keys for the management writes (12 of 20). Any OpenAI or Anthropic SDK works by changing the base URL, but the only first-party client is the Python SDK, and keys and spend need Postgres and a master key before the first call (12 of 15).\n\n### Transparency \u0026 trust: Hugging Face Inference Providers +8\n\n- Hugging Face Inference Providers (71): Closed service under Terms of Service that name Inference Providers, with an Apache-2.0 Python client and an MIT JavaScript client (20). The security page and the docs agree on no training, no stored bodies and 30-day logs, but the privacy policy dates from 28 March 2023 and each provider's own data policy governs the rest (18 of 30). No deprecation policy found. Provider support changes and the 7 October removal of free credits are dated only in the docs repository history (5 of 20). The privacy policy lists 11 subprocessors with countries per the Inference Endpoints check, and the docs name every provider a request can reach, though not where each runs (15 of 20).\n- LiteLLM (63): MIT for everything outside enterprise/, which has its own licence (25 of 30). The data security page (updated 6 November 2026) and security.md agree that a self-hosted gateway sends no data or telemetry to BerriAI, and the gateway stores prompts and spend in the operator's own Postgres. We found no terms of service or privacy policy on litellm.ai for its site, trial or Enterprise licence (20 of 30). A version support policy since 29 June 2026 (four stable minor lines), deprecated pages labelled in the docs, and release notes with breaking-change bullets (15 of 20). Usage telemetry, new in v1.107.0, is off by default, lists every field it can send and goes only to a table or endpoint the operator sets. A main-branch commit of 23 October 2026 made the Admin UI chat post a hashed user id to chat-telemetry.litellm.ai, and it was removed on 24 October before any tagged release contained it, so it isn't deducted. The start-up fetch of the price map from GitHub is documented with an opt-out (17 of 20).\n\n### Security \u0026 auth: Hugging Face Inference Providers +7\n\n- Hugging Face Inference Providers (79): Model reading. Fine-grained user access tokens that can be limited to the `Make calls to Inference Providers` permission, rotated and revoked, plus a public revocation endpoint for leaked tokens, and organisation approval of fine-grained tokens on paid plans (30). Hugging Face says it doesn't store user data for training, but data handling past the router follows each provider's policy, with no per-request filter to exclude providers that train or retain (12 of 20). Request bodies and responses aren't stored when routed and debug logs are kept up to 30 days without user data or tokens, with provider retention outside that statement (10 of 15). Usage by model and provider in account settings, an Enterprise usage API, and Team and Enterprise admins can disable providers and set a spending limit (13 of 15). SOC 2 Type 2 for the Hub, of which the docs say Inference Providers is a feature, and security.txt valid to 2030 per the Inference Endpoints check; no bug bounty found (14 of 20).\n- LiteLLM (72): Read with the tool checklist for self-hosted software. Virtual keys are hashed, revocable and blockable, with model, route, MCP server, budget, rate and expiry limits, and a `proxy_admin_viewer` read-only role exists. Rotation with a grace period is Enterprise only, so 25, less 10 because the docs pass a key as `?key=` on `/key/info` and on the Claude Code marketplace manifest (15 of 30). `allowed_routes`, MCP permissions by key and team, an approval queue for submitted MCP servers and stdio MCP servers off by default from 1.104.0 (15 of 20). Guardrails can run before a model call and on MCP tool results, and an OWASP LLM Top 10 page maps each risk to a control, but prompt-injection detectors such as Lakera need an Enterprise licence and all of it is off by default (12 of 15). Request and spend logs per key in the open-source build; audit logs of admin actions are Enterprise only (12 of 15). security.md with GitHub private reporting, a bug bounty from $500 to $3,000 for P0 and P1 reports, a security announcements list, SOC 2 Type II per the data security page, CodeQL, OSV and Scorecard workflows, and advisories published on GitHub. No security.txt, and reports without a video are closed unread (18 of 20).\n\n## Score by category\n\n| Category | Weight | Hugging Face Inference Providers | LiteLLM | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 25 | 70 | LiteLLM +45 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 62 | 91 | LiteLLM +29 |\n| Agent ergonomics | 13% (16.2 this run) | 58 | 79 | LiteLLM +21 |\n| Security \u0026 auth | 14% (17.5 this run) | 79 | 72 | Hugging Face Inference Providers +7 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 60 | LiteLLM +40 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 80 | 82 | LiteLLM +2 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 71 | 63 | Hugging Face Inference Providers +8 |\n| Negative events | ≤15 | 0 | -12 | |\n| **Total** | | **54 · C** | **62.4 · B** | |\n\n## Facts side by side\n\n| Fact | Hugging Face Inference Providers | LiteLLM |\n| --- | --- | --- |\n| Kind | Model router | Model router |\n| Vendor | Hugging Face, Inc. | BerriAI |\n| Hosted endpoint | `https://router.huggingface.co/v1` | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Freemium |\n| x402 | no | no |\n| Licence | Proprietary service under the Hugging Face Terms of Service, which name Inference Providers. The `huggingface_hub` Python client is Apache-2.0 and `@huggingface/inference` is MIT | MIT, except the enterprise/ directory, which is under its own licence and needs an Enterprise licence key |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-29 | 2026-11-05 |\n| Terms last updated | 2022-09-15 | no document linked |\n| Privacy policy last updated | 2023-03-28 | no date given |\n| Customer content may train models | not found in the text | not read yet |\n| Terms restrict automated access | not found in the text | not read yet |\n| Terms restrict benchmarking | not found in the text | not read yet |\n| Terms or service can change without notice | yes | not read yet |\n| Arbitration or class-action waiver | not found in the text | not read yet |\n| Popularity | 483k npm/wk, 71.5M PyPI/wk | 63k stars, 22.5M PyPI/wk |\n\n## Verdicts\n\n**Hugging Face Inference Providers.** One Hugging Face token reaches open models on 18 partner providers at the providers' own prices, with a public model list that shows each provider's price, context and tool support. Router rate limits, error responses and its own status component were not found in the reviewed documentation, and free accounts have had no included credits since 7 October 2026.\n\n**LiteLLM.** An MIT-licensed gateway that puts 100+ model providers behind one OpenAI-compatible API, with virtual keys, budgets, fallbacks and an MCP gateway, released several times a week. Its security record is the main limitation. Two PyPI releases carried a credential stealer on 24 March 2026, and 30 GitHub advisories followed in 12 months, five of them critical.\n\n## Before you call either\n\n### Hugging Face Inference Providers\n\n1. Read https://router.huggingface.co/v1/models before choosing a provider; it shows price, context and `supports_tools` per provider\n2. Pin a provider with `model:\u003cprovider\u003e` when tool calling or structured output matters, since support varies by provider\n3. Use a fine-grained token with only the Inference Providers permission, and send `X-HF-Bill-To` to bill an organisation\n4. Back off on 429 without assuming a Retry-After header, since none is documented for the router\n\n### LiteLLM\n\n1. Pin an exact litellm version or image digest, verify the cosign signature, and never install an unpinned latest in CI\n2. Give each agent a virtual key limited to its models, routes and MCP servers, with a budget and an expiry\n3. Treat a 429 with `retry-after` as the gateway's own limit and wait that long; a 429 without it came from the provider\n4. Set `NO_DOCS`, `NO_REDOC` and `NO_OPENAPI` to True in production, and use the `-non_root` image variant\n5. Set `LITELLM_LOCAL_MODEL_COST_MAP=True` where egress is blocked, since start-up otherwise fetches the price map from GitHub\n\n## Questions\n\n### Which is better for AI agents, Hugging Face Inference Providers or LiteLLM?\n\nLiteLLM scores 62.4 (B) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in 5 of 7 scored categories. Hugging Face Inference Providers leads on security \u0026 auth and transparency \u0026 trust.\n\n### Do Hugging Face Inference Providers and LiteLLM need an API key?\n\nBoth need an API key.\n\n### Can an agent call Hugging Face Inference Providers and LiteLLM without installing anything?\n\nHugging Face Inference Providers has a hosted endpoint at https://router.huggingface.co/v1. No hosted endpoint is listed for LiteLLM.\n\n### Are Hugging Face Inference Providers and LiteLLM open source?\n\nNo open-source release is listed for Hugging Face Inference Providers. LiteLLM is open source (MIT, except the enterprise/ directory, which is under its own licence and needs an Enterprise licence key).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-litellm.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-litellm.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hugging-face-inference-providers\", \"b\": \"litellm\"}`. From a terminal: `anchor compare hugging-face-inference-providers litellm`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-providers.json and https://www.anchorterminal.com/api/v1/tools/litellm.json\n\n## Other comparisons with Hugging Face Inference Providers or LiteLLM\n\n- [Amazon Bedrock vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/amazon-bedrock-vs-hugging-face-inference-providers.md)\n- [Claude API vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/anthropic-api-vs-hugging-face-inference-providers.md)\n- [Azure OpenAI in Foundry Models vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/azure-openai-vs-hugging-face-inference-providers.md)\n- [BlockRun.AI vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/blockrun-ai-vs-hugging-face-inference-providers.md)\n- [Cerebras Inference vs Hugging Face Inference 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