Better Stack (Log Management)
Cloud-based log management solution for aggregating, searching, and analyzing application logs at scale
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Instant, per‑second visibility into every infrastructure component.
Netdata delivers per‑second metrics, zero‑config deployment, edge‑based processing, and built‑in ML anomaly detection, giving teams instant insight across servers, containers, and cloud services.
Netdata is a real‑time monitoring platform that captures every metric every second, providing developers, SREs, and operations teams with immediate visibility into system health. Its zero‑configuration agent auto‑discovers services, containers, and cloud resources, while edge‑resident ML models continuously learn normal behavior and flag anomalies without external dependencies.
The agent runs on Linux, Windows, macOS, and embedded devices with minimal CPU and RAM impact, storing data locally using a highly efficient tiered format (~0.5 bytes per sample). Horizontal scaling is achieved through a parent‑child architecture, allowing seamless expansion from a single node to multi‑cloud or IoT fleets. Visualization is available via an interactive UI, and optional Cloud features add centralized alerting and role‑based access without moving raw metrics.
Netdata’s design emphasizes low overhead, instant insights, and extensibility, making it suitable for both small‑scale environments and large, distributed infrastructures.
When teams consider Netdata, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Production outage investigation
Engineers pinpoint the exact second a resource spike occurred, reducing mean time to resolution.
Capacity planning for microservices
Per‑second usage trends reveal scaling patterns, informing right‑sizing of compute resources.
Anomaly detection in IoT fleets
Edge‑resident ML models automatically alert on sensor drift or hardware failures without central analysis.
Multi‑cloud cost optimization
Unified dashboards compare resource consumption across AWS, GCP, and Azure, highlighting inefficiencies.
It gathers system resources, storage, network, hardware sensors, processes, containers, VMs, logs, synthetic checks, and many packaged applications.
A single binary or package can be run on the target host; the agent starts automatically and begins monitoring without further configuration.
By default data is stored locally on each node; optional Cloud or external exporters can forward metrics for centralized alerting.
The agent is designed for minimal CPU and RAM usage, typically consuming less than 1 % of a modern server’s resources.
Yes, it can export data via Prometheus, OpenMetrics, StatsD, and other protocols for downstream consumption.
Project at a glance
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