Kubernetes network blindspots - real-time AI contextual observability

Kubernetes network blindspots - real-time AI contextual observability targets a $6.0B = 150,000 businesses running production Kubernetes x $40,000 ACV. Assumes enterprises and mid-market buying cluster-level security and network observability as a multi-year subscription. total addressable market with medium saturation and a year-over-year growth rate of 18% - market growth for cloud-native security and observability combined, driven by Kubernetes adoption and security spend.

Key trends driving demand: eBPF telemetry -- enables low-overhead, high-fidelity network and process-level data collection inside kernels, making continuous capture viable.; Kubernetes proliferation -- more clusters and microservices increase internal network traffic needing monitoring and forensics.; Shift to runtime security -- enterprises moving budget from perimeter controls to runtime detection and response, raising demand for network-level signals.; AI ops and automation -- teams want AI agents that can triage and remediate, requiring rich, correlated telemetry as input..