IoT monitoring systems, from traffic and pedestrian sensing in smart cities to building energy metering and industrial robot telemetry, increasingly run as containerised microservices on Kubernetes (K8s) clusters that span constrained edge nodes and cloud servers. Cognitive orchestration layers extend K8s with network-, data- and energy-aware placement, running on the same nodes as the monitoring workloads they manage, with their own footprint rarely reported. This paper proposes a component-level cost-attribution methodology and an automation-footprint trade-off analysis that relates the manual interventions removed to deployment latency, CPU, memory and estimated power consumed by each of its functional roles. We apply it to Cognitive Decentralised Edge–Cloud Orchestration (CODECO), an open-source cognitive orchestration framework, across six infrastructures, including three operational IoT deployments, ARM and x86 hardware, and vanilla K8s, K3s and KinD. The automated CODECO and CODEF workflow reduces the number of manual interventions from 72 to 9 (87.5%) under our action-count definition, with a total of 4.57 GB memory overhead, the most significant observed trade-off. Decomposing these costs by architectural layer reveals that within the tested cluster sizes, the installation time of the scheduling component shows no measurable growth and that of the learning component grows slowly, while cross-layer network management contributes the largest share of installation time and idle CPU consumption. Results show that orchestration costs arise in different architectural layers, indicating they can be placed and optimised independently without sacrificing automation benefits.