A practical guide to building an AI-powered monitoring dashboard that shows EC2 health, web platform status, CI/CD quality checks, and uses Claude to interpret anomalies in plain English.
The complete deployment guide: Docker multi-stage builds, Kubernetes orchestration, CI/CD with GitHub Actions, zero-downtime deploys, go-live checklist, production monitoring with Prometheus/Grafana, and the operational runbook that keeps voice AI running at scale.
Launch day is just the beginning. Privacy-compliant analytics, crash reporting that respects child data, and a feedback loop that actually improves the product.
Scaling from 10 sessions/week to 200 concurrent. The enrichment bottleneck (30,000 API calls), session recovery for dropped WebRTC connections, provider failover, and the operational metrics that keep it all visible.
Real-time per-minute cost tracking, provider comparison (OpenAI Realtime ~$0.053/min vs Gemini Live ~$0.029/min), budget enforcement with soft/hard limits, and the self-hosting math that saves 90% on transport.
Build comprehensive observability: custom CloudWatch metrics and dashboards, X-Ray distributed tracing across Lambda and Fargate, structured logging with Lambda Powertools, alerting strategies, cost dashboards, and anomaly detection.
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