AI Observability Platform — Kafka Event Architecture for Real-Time Monitoring
Modern AI systems need observability for prompts, latency, token usage, errors, model drift, anomalies, and user-facing behavior in real time.
Event-Driven Monitoring Architecture
The platform emits structured events for model requests, responses, latency, token usage, failures, user feedback, and downstream workflow outcomes. Kafka stores those events as durable streams that multiple consumers can process independently.
AI Apps -> Event SDK -> Kafka Topics Kafka -> Spring Boot Consumers -> Metrics Store Kafka -> Anomaly Detection Worker -> Alert Manager Metrics Store -> Realtime Dashboard
Spring Boot Consumers and Anomaly Detection
Spring Boot consumers aggregate events into service-level metrics while anomaly workers watch for unusual latency spikes, high error rates, abnormal token usage, and suspicious model output patterns.
@KafkaListener(topics = "ai-observability-events")public void consume(AiEvent event) { metricsStore.record(event); if (anomalyDetector.isSuspicious(event)) { alertManager.notify(event); }}Dashboards and Alerts
The dashboard exposes latency percentiles, model cost, prompt volume, failure rate, anomalous sessions, and service health. Alerts can route to email, Slack, Telegram, or incident tooling depending on severity.
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