Observability & Monitoring

Distributed tracing follows requests across microservices. AI anomaly detection identifies issues faster than threshold-based alerts. Log analytics processes billions of log entries for pattern detection. Real-time metrics dashboards visualize system health. AI observability monitors model accuracy, drift, and performance. eBPF enables kernel-level monitoring without code changes.

Distributed tracing identifies latency bottlenecks across microservice architectures. AI anomaly detection surfaces issues before users report them. Log analytics correlates events across systems for root cause analysis. AI observability monitors LLM response quality, hallucination rates, and latency.

Observability platforms provide visibility into software system performance through metrics, logs, traces, and AI-powered incident detection. With 2 funders. Datadog ($40B+ market cap) and New Relic validated the category's venture potential.