Parks plans deployment capacity before launching a summer festival chatbot expected to draw heavy traffic. What should capacity-aware deployment include?

Parks launching a summer festival chatbot expected to draw heavy traffic must plan Foundry deployment capacity for anticipated festival load before launch rather than discovering saturation mid-event. Capacity-aware deployment is proactive sizing and headroom planning for known civic peaks. Festival traffic is predictable enough to design for; waiting for outages is not a capacity strategy when the calendar already shows the crowd. Provisioning additional Azure AI Content Safety quota for the anticipated festival traffic before launch to avoid filtering delays fails readiness—the safety quota governs content filtering, not model deployment throughput, so festival prompts will still hit saturation mid-event. Enabling Foundry prompt caching for the anticipated festival queries before launch to handle the expected repeated traffic surge fails because caching optimizes latency and cost for repeated prompts but does not add throughput capacity, so the festival surge still saturates the deployment mid-event. Reserving extra Foundry agent thread capacity for the anticipated festival sessions before launch to sustain concurrent interactions fails because thread capacity holds conversation state, not inference throughput, so festival requests still throttle at the saturated deployment mid-event. Exam caveat: capacity plans should include dependency limits such as model quotas and downstream APIs, not only front-door web instances, or the weakest link still saturates. Operational check: estimate concurrent festival sessions, load-test to that profile plus headroom, configure scale rules and alerts, and schedule a go-live capacity review before festival gates open.