An open-source observability and APM tool with AI-powered root cause analysis, combining metrics, logs, traces, profiling, and SLO-based alerting.
Coroot is an open-source observability and application performance monitoring (APM) platform that transforms telemetry data into actionable insights. It uses eBPF for zero-instrumentation data collection and provides AI-powered root cause analysis, predefined dashboards, and automated issue identification to help teams quickly understand and resolve system issues.
DevOps engineers, SREs, and platform teams managing Kubernetes-based applications who need comprehensive observability without extensive manual instrumentation. It's also suitable for developers seeking to monitor application performance, costs, and deployments in cloud environments.
Developers choose Coroot for its zero-instrumentation approach using eBPF, which automatically gathers metrics, logs, traces, and profiles without code changes, eliminating blind spots. Its unique selling point is built-in expertise that automatically identifies over 80% of issues and provides consolidated alerts with inspection results, reducing manual investigation time.
Coroot is an open-source observability and APM tool with AI-powered Root Cause Analysis. It combines metrics, logs, traces, continuous profiling, and SLO-based alerting with predefined dashboards and inspections.
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Uses eBPF to automatically gather metrics, logs, traces, and profiles without code changes, providing a complete service map with no blind spots, as stated in the README.
Automatically identifies over 80% of issues and sends consolidated alerts with inspection results when SLOs are breached, reducing manual investigation time.
Tracks cloud costs for AWS, GCP, and Azure down to individual applications without requiring access to cloud accounts, offering financial insights alongside performance data.
Monitors application rollouts in Kubernetes automatically, compares releases for performance degradation, and integrates cost monitoring without CI/CD pipeline integration.
Requires a compatible Linux kernel with eBPF support, which may not be available in all environments and could introduce security or stability concerns.
Heavily focused on Kubernetes, with limited support for other deployment models like serverless or traditional VMs, as implied by its features.
Deployment via Docker or Kubernetes requires infrastructure expertise, and eBPF-based instrumentation might need additional tuning for non-standard setups.