{"id":"roboflow-roboflow-inference-server-jetson-4-6-1","name":"roboflow-inference-server-jetson-4.6.1","homepage":"https://hub.docker.com/r/roboflow/roboflow-inference-server-jetson-4.6.1","repo_url":"https://hub.docker.com/r/roboflow/roboflow-inference-server-jetson-4.6.1","category":"ai-ml","subcategories":[],"tags":["computer-vision","object-detection","inference-server","edge-computing","nvidia-jetson","roboflow","self-hosted","api"],"what_it_does":"Provides an inference server tailored for NVIDIA Jetson (v4.6.1) to run Roboflow-hosted or packaged computer-vision models on-device, exposing the model as a network service for image/video inference workflows.","use_cases":["On-device object detection/vision inference on NVIDIA Jetson","Deploying Roboflow models into edge pipelines (factory floors, retail analytics, field monitoring)","Serving vision inference to local applications over LAN","Low-latency inference for camera streams on edge hardware"],"not_for":["Training or fine-tuning models","Browser-based direct inference without a backend","Cloud-scale multi-tenant inference with strong hosted controls"],"best_when":"You need local/edge computer-vision inference with Jetson hardware and can operate/manage the server deployment yourself.","avoid_when":"You need a fully managed cloud API with documented SLAs, centralized auth/ratelimiting, or you cannot open inbound ports for the inference server.","alternatives":["Roboflow Inference API (hosted cloud service)","NVIDIA DeepStream for edge vision pipelines","Triton Inference Server (self-hosted model serving)","OpenVINO Model Server / Intel edge inference stacks"],"af_score":27.2,"security_score":27.8,"reliability_score":33.8,"package_type":"mcp_server","discovery_source":["docker_mcp"],"priority":"low","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-04-04T21:34:42.649261+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 evidence provided in input data","Likely none or network-layer controls (not verified)"],"oauth":false,"scopes":false,"notes":"No README/repo details were provided in the prompt to verify authentication method(s), token handling, or scope controls. Assume default/self-hosted security posture unless documented otherwise."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"Pricing cannot be determined from the provided package name/version alone; typically self-hosted deployments have no per-request vendor billing but may require model access/licensing outside this package."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":27.2,"security_score":27.8,"reliability_score":33.8,"mcp_server_quality":0.0,"documentation_accuracy":20.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":50.0,"rate_limit_clarity":0.0,"tls_enforcement":40.0,"auth_strength":20.0,"scope_granularity":0.0,"dependency_hygiene":45.0,"secret_handling":40.0,"security_notes":"Self-hosted edge inference servers frequently rely on deployment/network controls rather than strong API auth. TLS, auth mechanisms, and secret-handling behaviors are not verifiable from the provided input. If the server exposes an inference endpoint on a local network, attackers with network access may attempt inference abuse without proper auth/rate limiting. Ensure HTTPS, firewall rules, minimal privileges, and avoid embedding Roboflow API keys in logs/configs.","uptime_documented":0.0,"version_stability":55.0,"breaking_changes_history":50.0,"error_recovery":30.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["Auth and rate limiting are not verifiable from the provided data; agents may need to infer by trial and error.","Edge inference servers often have hardware/driver-related failure modes (GPU memory, CUDA/cuDNN mismatches) that may not have consistent error codes.","Image payload size and preprocessing parameters can cause 4xx/5xx responses that need careful request construction."]}}