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Coroot
Apache-2.0Coroot replaces proprietary APM platforms like New Relic with zero-code eBPF observability, automated root-cause analysis, and self-hosted storage.
Open-source alternative to — compare it against:
Overview
New Relic meters telemetry by the gigabyte and charges per user seat, making cluster-wide observability expensive as workloads scale. Coroot observes applications from the Linux kernel via eBPF, storing metrics and traces in self-hosted storage without consumption penalties. Its automated inspections detect regressions and anomalies across microservices without modifying application code.
Why Coroot instead of New Relic
The financial split between commercial observability platforms and Coroot hinges on data throughput. New Relic bills for ingested logs, metrics, and traces alongside seat-based user tiers. When traffic surges or a cascading failure produces millions of error logs, monthly SaaS invoices spike unpredictably. Coroot decouples monitoring expenditure from data volume. Its node agent uses eBPF probes to calculate Golden Signals, TCP anomalies, and service maps at the kernel level, keeping telemetry within your own clusters without per-gigabyte surcharges or seat licenses.
New Relic counters with a broader catalog of hosted cloud integrations, synthetics, and serverless monitoring that require no local storage maintenance. Running Coroot shifts that responsibility to your team, which must operate ClickHouse for logs and traces alongside Prometheus or VictoriaMetrics for metrics. The trade-off is clear: teams eliminate code instrumentation and vendor data markup in exchange for managing their own telemetry backends.
What it does well
- Zero-code eBPF collection: Captures latency, error rates, and traffic flows from kernel probes without adding SDKs or modifying application binaries.
- Automated root-cause inspections: Evaluates telemetry against built-in heuristics for CPU throttling, OOM kills, and DNS failures, turning alert storms into single incidents.
- ClickHouse-backed correlation: Links distributed request traces directly to container logs, letting engineers move from latency spikes to relevant log lines in one view.
- Deployment tracking: Detects Kubernetes rollouts automatically and overlays release events onto latency charts to surface regressions without CI/CD webhooks.
- Continuous CPU and memory profiling: Collects in-process profiles to trace resource consumption to exact functions and lines of code.
- Infrastructure cost attribution: Attributes cloud compute usage to individual services and namespaces without connecting to cloud provider billing APIs.
