# Incidents. Investigated the Moment They Fire. > OpenObserve Incidents runs automated investigations with full context, correlating logs, metrics, and traces so teams triage and resolve incidents faster. Source: https://openobserve.ai/incidents/ --- Correlates logs, metrics, and traces the moment an incident fires, so your team triages and resolves with the full picture instead of piecing it together. - [Start Free Cloud Trial](https://cloud.openobserve.ai/web/login/) - [Talk to a Human](/demo/) ### Drastically cut MTTR by 90% Automate investigations before your team logs in ### Reduce noisy alerts Help your team with Intelligent Alert Grouping ### Knowledge That Compounds Retain perfect memory of all past incidents. ## From Alert to Root Cause, Automatically ### Autonomous Incident Analysis - **AI-Powered Root Cause Analysis** - Structure every finding into contributing factors, incident timelines, immediate actions, and long-term prevention. - **Multi-Signal Correlation Across Your Service Topology** - Correlate logs, metrics, and distributed traces in real time to map symptoms to underlying causes across your topology. [Learn More](https://openobserve.ai/docs/user-guide/analytics/incidents/#ai-powered-root-cause-analysis) ### Intelligent Alert Grouping - **Semantic Deduplication** - Group related alerts into single incidents automatically, reducing your total incident count by 50% from day one. - **Hierarchical Scope-Based Correlation** - Group alerts by cluster, namespace, and deployment. Refine incidents automatically as new signals arrive within a 30-minute window. [Learn More](https://openobserve.ai/docs/user-guide/analytics/incidents/#alert-graph) ### Historical Pattern Matching - **Instant Historical Recall** - Reference past incidents to inform real-time analysis, applying proven fixes from up to 1,000 past incidents instead of starting from scratch. - **Self-Improving Intelligence** - Enrich your knowledge base with every resolved incident, training accuracy improvements automatically. [Learn More](https://openobserve.ai/docs/user-guide/analytics/incidents/#activity-timeline-and-collaboration) ### Automated Incident Documentation - **Auto-Generated Incident Reports** - Generate comprehensive reports with root cause, contributing factors, and a complete timeline alongside evidence links. - **Institutional Knowledge Standardization** - Standardize root cause analysis across your organization, eliminating tribal knowledge and undocumented fixes. [Learn More](https://openobserve.ai/docs/user-guide/analytics/incidents/#incident-overview-dashboard) ## Measured against industry leaders Same telemetry, same workloads, one platform. Every number is OpenObserve against a named vendor - not an industry average. 8x cost reduction means you can unify your observability into a single platform. 140x storage means longer retention doesn't necessarily mean expensive bills. 5x to 15x faster queries mean dashboards load in milliseconds, not minutes. See how much you would save switching today. - [See all comparisons](/comparison/) ## Teams trust OpenObserve when it matters most ## Incidents FAQs ### How does OpenObserve reduce alert noise and correlate incidents? OpenObserve groups alerts by environment scope rather than individual workload instances. Dimension matching detects subset and superset relationships between alerts to consolidate related signals into a single focused incident. This results in an 80% to 90% reduction in alert noise from day one without any manual rule configuration. ### How are incidents automatically grouped and deduplicated? When alerts fire, the system performs dimension matching to detect subset, superset, and incompatible relationships between alert dimensions. The agent groups alerts by logical scopes like cluster and namespace rather than individual pod names. Each incident is enriched with correlated logs, metrics, and traces, then refines itself over a 30-minute window as new signals arrive. ### What does an AI-generated incident report include and should I trust it? Each report includes a root cause, contributing factors, a timeline, immediate action items, long-term prevention steps, and direct links to supporting evidence. OpenObserve enforces report quality by requiring specific contributing factors and concrete prevention steps in every RCA. Treat the output as a high-fidelity first draft and add human context before final publishing. ### How reliable is automated root cause analysis? Every RCA is grounded in the actual signals OpenObserve collects. The agent uses correlated logs, metrics, and traces to directly inform every conclusion. Unlike black-box AIOps systems, the AI SRE Agent shows exactly what data it analyzed and how it reached its findings. Engineers can validate every recommendation against the evidence to build trust in the output rather than accepting it blindly. ### Is there a cost per user or per incident? No per-user fees,add your entire team for free. You only pay for the AI Credits used during an investigation. A single incident typically consumes 20 credits. ### How does OpenObserve incident management compare to Dash0 or Datadog? OpenObserve groups related alerts into incidents, links them to the underlying logs, metrics, and traces, and hands them to the AI SRE agent for root-cause analysis, all inside the same open-source platform. Dash0 and Datadog offer comparable workflows only within their closed, cloud-only products billed per signal or per host. ## Explore guides, videos, and articles to help you get the most out of Incidents. ### Incident Management [Learn more](https://openobserve.ai/docs/user-guide/analytics/incidents/) ### Alert Correlation [Learn more](https://openobserve.ai/docs/user-guide/analytics/incidents/#automatic-alert-correlation) ### Reducing MTTR with Automated Incident Analysis [Learn more](https://openobserve.ai/blog/reduce-mttd-mttr-openobserve-alert-correlation/) - [Explore All Blogs](/blog/) ## Ready to get started? Try OpenObserve today for more efficient and performant observability. - Get Started For Free - [Schedule Demo](/demo/)