See Every Incident’s Complete Impact - From First Spark to Full Cascade

Netdata reveals dependencies dynamically through real-time temporal correlation. When cascading failures occur, per-second anomaly detection captures the exact sequence - Node A fails at t=0, Node B at t+5s, Node C at t+10s. Sequential patterns reveal dependency chains, simultaneous patterns indicate shared resource failures. This approach discovers actual relationships from behavior rather than relying on static documentation that becomes outdated when infrastructure changes.

Traditional monitoring requires manual investigation across multiple dashboards and tools. Netdata’s specialized scoring engine automatically evaluates thousands of metrics simultaneously across all affected nodes, comparing incident windows to baseline periods using multiple methods (KS2 for distributions, Volume for spikes, Anomaly Rate for ML patterns, Value for raw changes). Results return ordered by severity in sub-second timeframes, with root cause typically surfacing in the top 30-50 metrics - eliminating hours of manual correlation work.

Yes, natively. Netdata automatically tracks full Kubernetes label hierarchy from cluster → node → namespace → controller → pod → container. Filter by namespace to isolate blast radius to specific applications, group by controller to see replica failures, or drill down to understand exactly which components are affected. Temporal analysis reveals rolling update patterns and deployment-triggered cascading failures with complete context, without requiring service mesh configuration.

Netdata’s combination of per-second granularity (catches transient issues traditional monitoring misses), automated correlation (eliminates manual investigation across fragmented tools), infrastructure-wide visualization (instant scope assessment via NAR charts), and ML-powered anomaly detection (99% false positive reduction in anomaly signals) enables engineers to identify root causes in minutes rather than hours. The 80% MTTR reduction represents typical improvement versus traditional multi-tool approaches requiring manual correlation.

No. Blast radius detection works automatically from installation. ML models train themselves within 15 minutes (18 per metric, no tuning required), Node Anomaly Rate charts generate automatically, correlation engine activates without setup, and relationships discover themselves from actual behavior. No query languages to learn, no dashboards to build, no dependency maps to maintain, no threshold configuration needed. Junior and senior engineers access identical powerful correlation capabilities from day one.

Traditional minute-level monitoring obscures cascade sequences and misses transient issues. Per-second collection with per-second anomaly detection captures exact temporal relationships - revealing which system failed first, how problems propagated, and distinguishing root causes from cascading effects. This precision catches microbursts, connection errors, and temporary spikes lasting 2-10 seconds that traditional monitoring averages away, enabling accurate understanding of causality versus correlation during cascading failures.

Yes, with zero-latency correlation. Global datetime picker and highlighted timeframe synchronize metrics, logs, alerts, and anomalies to the same moment across all views. Click any chart timestamp and logs seek automatically to that exact moment, alert transitions align temporally, and anomaly bits reveal ML-detected patterns - all from the same source without manual timestamp matching. This unified time-synchronized approach eliminates context switching between fragmented tools during incident response.

Traditional dependency mapping tools require building and maintaining static topology diagrams that become outdated when infrastructure changes. Netdata discovers relationships dynamically through temporal correlation - dependencies reveal themselves from actual behavior during incidents. This eliminates the operational overhead of maintaining topology databases while providing more accurate visibility into real-world relationships. For formal CMDB requirements, Netdata complements (rather than replaces) ITSM platforms by providing real-time operational blast radius detection.

Netdata’s distributed edge architecture scales linearly without performance degradation. Each Agent processes its own data with local ML training, while Parents aggregate streams without becoming bottlenecks. Active-active Parent clustering with automatic work distribution enables horizontal scaling. Netdata benchmark testing at 4.6 million metrics/second shows 36% less CPU, 88% less RAM, and 97% less disk I/O than Prometheus while maintaining sub-2-second latency and sub-second correlation results.

Currently, Netdata focuses on temporal correlation and dynamic relationship discovery rather than static visual topology maps. Network connection data is tracked comprehensively and available via API, but not yet visualized as flow diagrams. The Node Anomaly Rate charts, correlation engine results, and hierarchical drill-down provide powerful blast radius visibility through data-driven analysis. Visual topology enhancement is under consideration for future releases based on customer feedback.

Absolutely. Two paths: (1) Open Source Agent (free) - install in 60 seconds with wget -O /tmp/netdata-kickstart.sh https://get.netdata.cloud/kickstart.sh && sh /tmp/netdata-kickstart.sh. All blast radius capabilities included (ML, correlation, NAR charts). (2) Netdata Cloud Free Trial (14 days) - full Business plan trial with unlimited nodes, infrastructure-wide NAR charts, and AI insights. Start with agent on test systems, trigger incidents, see blast radius detection in action immediately.

Everything at flat per-node pricing: ML-based anomaly detection (18 models per metric), automated correlation engine, infrastructure-wide NAR charts, temporal analysis with per-second granularity, logs correlation, Kubernetes relationship tracking, network connection monitoring, AI-powered root cause analysis, unlimited metrics/users/dashboards. No per-metric fees, no volume penalties, no feature gating. This represents 90% cost reduction versus traditional multi-platform approaches requiring separate dependency mapping, APM, and log analysis tools.

Netdata trains 18 k-means models (k=2) per metric on different time windows (6-hour windows, 3-hour stagger). Anomalies are flagged ONLY when ALL 18 models agree unanimously, achieving theoretical false positive rate of 10^-36 (1% per model ^18 models). Real-world deployments show 99% false positive reduction in anomaly detection. This consensus-based approach provides accurate anomaly signals during cascading failures without overwhelming noise, enabling confident blast radius assessment and faster root cause identification.

Not yet. Distributed tracing support is planned for Q2 2026 via OpenTelemetry. Currently, Netdata’s per-second temporal correlation often reveals the same dependency information without requiring application instrumentation - sequential anomaly spikes show dependency chains (A→B→C), simultaneous spikes indicate shared resource failures. For request-level granularity across microservices today, complement Netdata with a distributed tracing solution. Netdata excels at infrastructure-level blast radius detection with comprehensive metrics and logs correlation.