This is like a 24/7 digital guard that constantly watches your systems, apps, and data, and only calls you when something looks wrong or important enough to act on.
Traditional monitoring generates too many noisy alerts and requires humans to constantly watch dashboards. AI-driven monitoring and alerting aims to automatically detect real issues earlier, reduce false positives, and route the right alerts to the right people so teams can focus on fixing problems instead of watching screens.
Tight integration with existing infrastructure and logs/metrics, tuned detection models on historical incident data, and embedding into incident-management workflows can create strong switching costs and differentiated performance over time.
Hybrid
Time-Series DB
Medium (Integration logic)
High-volume telemetry streams (logs, metrics, traces) create storage and compute pressure for real-time anomaly detection and alerting; tuning to reduce false positives without missing true incidents is an ongoing challenge.
Early Majority
Differentiation typically comes from combining robust, low-latency anomaly detection on time-series data with intelligent alert routing, deduplication, and summarization (often via LLMs) that plug smoothly into DevOps/ITSM tools rather than just generating more alerts.