{"id":"nutanix-nai-kserve-huggingfaceserver","name":"nai-kserve-huggingfaceserver","homepage":"https://hub.docker.com/r/nutanix/nai-kserve-huggingfaceserver","repo_url":"https://hub.docker.com/r/nutanix/nai-kserve-huggingfaceserver","category":"ai-ml","subcategories":[],"tags":["kserve","huggingface","inference","kubernetes","model-serving","mlops"],"what_it_does":"The nai-kserve-huggingfaceserver package provides a KServe server integration for serving Hugging Face models. It is intended to run Hugging Face model inference behind a KServe-compatible endpoint.","use_cases":["Serving Hugging Face Transformer models via KServe for inference","Deploying model endpoints in Kubernetes using KServe","Providing a standardized request/response surface for ML inference"],"not_for":["Training or fine-tuning models","Use cases requiring a fully managed, SaaS-style inference API without Kubernetes","Applications needing first-class, language-specific SDKs out of the box"],"best_when":"You already use Kubernetes and KServe, and want to deploy Hugging Face models with minimal custom serving code.","avoid_when":"You do not operate Kubernetes/KServe, or you need a turnkey hosted API with built-in usage analytics and rate-limiting policies.","alternatives":["KServe built-in model serving backends (where applicable) for other frameworks","Hugging Face Text Generation Inference (TGI) container deployments","TorchServe / Seldon Core serving stacks","Custom KServe predictor implementations"],"af_score":31.5,"security_score":31.5,"reliability_score":17.5,"package_type":"mcp_server","discovery_source":["docker_mcp"],"priority":"low","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-04-04T21:34:16.842749+00:00","interface":{"has_rest_api":false,"has_graphql":false,"has_grpc":false,"has_mcp_server":false,"mcp_server_url":null,"has_sdk":false,"sdk_languages":[],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":[],"oauth":false,"scopes":false,"notes":"No authentication details were provided in the supplied information. In typical KServe deployments, auth is handled by the ingress/gateway rather than the serving container itself."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"As a deployment/server integration for KServe, costs are primarily infrastructure-related (Kubernetes/GPU/compute). No pricing information was provided."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":31.5,"security_score":31.5,"reliability_score":17.5,"mcp_server_quality":0.0,"documentation_accuracy":30.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":60.0,"rate_limit_clarity":10.0,"tls_enforcement":50.0,"auth_strength":30.0,"scope_granularity":0.0,"dependency_hygiene":40.0,"secret_handling":40.0,"security_notes":"No repo-specific security documentation was provided. In many KServe setups, transport security (TLS) and request-level auth/rate limiting are enforced at the Kubernetes ingress/gateway. Ensure you configure TLS, authentication, and secret management for any model access tokens (e.g., Hugging Face) used by the deployment.","uptime_documented":0.0,"version_stability":40.0,"breaking_changes_history":0.0,"error_recovery":30.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["No evidence of an agent-facing interface contract (OpenAPI/SDK/MCP) in the provided information","KServe deployments often rely on Kubernetes ingress for auth/rate limits, so agent callers must align with the ingress behavior"]}}