{"id":"intelaipg-openvino-model-server","name":"openvino-model-server","homepage":"https://hub.docker.com/r/intelaipg/openvino-model-server","repo_url":"https://hub.docker.com/r/intelaipg/openvino-model-server","category":"ai-ml","subcategories":[],"tags":["ai-ml","inference","openvino","model-serving","http-api","edge","computer-vision"],"what_it_does":"OpenVINO Model Server exposes OpenVINO-IR (and related) models over an HTTP API to run inference, typically supporting multiple backends/devices (e.g., CPU/GPU/VPU) and model variants for deployment.","use_cases":["Serving OpenVINO models for low-latency inference in production","Deploying computer-vision models (object detection, classification, segmentation) using OpenVINO","Batching or repeatedly querying model inference from applications via HTTP","Edge/embedded inference deployment where OpenVINO is preferred"],"not_for":["Training or fine-tuning models","If you need a model-serving platform that natively manages model lifecycle (versioning/rollbacks) beyond what the server provides","If you require built-in enterprise auth/tenant isolation features"],"best_when":"You have OpenVINO models you want to serve and you want a straightforward server-side inference endpoint.","avoid_when":"You need strong, documented security controls (authn/authz), SLAs, and agent-friendly API contracts without additional engineering work.","alternatives":["OpenVINO Runtime with custom REST wrappers","NVIDIA Triton Inference Server (for non-OpenVINO environments)","TorchServe (PyTorch-centric)","FastAPI/Flask custom inference services","KServe / ModelMesh (higher-level model serving platforms)"],"af_score":35.2,"security_score":31.2,"reliability_score":35.0,"package_type":"mcp_server","discovery_source":["docker_mcp"],"priority":"low","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-04-04T19:34:55.839469+00:00","interface":{"has_rest_api":true,"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":["No auth documented/required (typical for local/dev inference servers)","Possible basic HTTP mechanisms depending on deployment configuration (not confirmed from provided info)"],"oauth":false,"scopes":false,"notes":"From the provided package info, explicit auth mechanisms (API keys/OAuth, scopes) are not verifiable. Treat as potentially unauthenticated unless you add an API gateway/reverse proxy with TLS and auth."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"Open-source style package; pricing not applicable."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":35.2,"security_score":31.2,"reliability_score":35.0,"mcp_server_quality":0.0,"documentation_accuracy":35.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":50.0,"rate_limit_clarity":0.0,"tls_enforcement":30.0,"auth_strength":20.0,"scope_granularity":0.0,"dependency_hygiene":55.0,"secret_handling":60.0,"security_notes":"Most model-serving deployments rely on external reverse proxies for TLS/auth. Without confirmed built-in auth and scope controls, assume weaker security posture. Ensure you enforce HTTPS, restrict network access, and do not expose the inference API publicly without gateway protections.","uptime_documented":0.0,"version_stability":55.0,"breaking_changes_history":45.0,"error_recovery":40.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["Inference endpoints are often not idempotent when they involve streaming, dynamic batching, or side effects; treat POST calls carefully.","Model warmup/cold-start and device compilation can introduce higher first-request latency; agents may need to tolerate timeouts.","Payload sizes (images/tensors) can be large; agents should implement streaming/chunking or size checks if supported."]}}