Best Self-hosted Alternatives to Sysdig Monitor

Netdata is an open-source, agent-based observability platform that collects, stores, and visualizes per-second metrics across infrastructure and applications. It combines a lightweight edge agent, a tiered time-series store, and optional centralized Cloud/Parent components for unified views and collaboration.

Key Features

• Per-second, real-time metrics collection with millisecond responsiveness and auto-generated dashboards.
• Edge-based machine learning: unsupervised anomaly detection and per-metric ML models running on the agent.
• Tiered, high-efficiency time-series storage (compact samples, ZSTD compression) with configurable retention and archiving.
• Distributed Parent–Child streaming pipeline for horizontal scaling, multi-node aggregation, and long-term retention.
• Broad integrations (800+ collectors) and export/archival targets including Prometheus, InfluxDB, OpenTSDB, and Graphite.
• Low resource footprint (designed for minimal CPU/RAM impact) and zero-configuration auto-discovery on supported platforms.

Use Cases

• Infrastructure and system monitoring: per-second visibility into CPU, memory, disks, network, sensors, and kernel metrics.
• Container and Kubernetes observability: native containerd/Docker and Kubernetes integrations for pod, node, and cluster troubleshooting.
• Incident troubleshooting and AIOps: anomaly detection, root-cause analysis, blast-radius identification, and automated reporting to accelerate incident resolution.

Limitations and Considerations

• The Netdata UI and Netdata Cloud components are delivered as closed-source offerings while the Agent is open-source; organizations requiring fully open-source stacks should evaluate this split.
• OpenTelemetry support is noted as "coming soon" in documentation; users relying heavily on OpenTelemetry may need to plan integrations or use exporters.
• Feature parity varies by platform (Linux has the most comprehensive coverage); some platform-specific collectors or deep kernel metrics are not available everywhere.

Netdata offers a high-resolution, low-overhead approach to full-stack monitoring with built-in ML and flexible scaling via Parents and Netdata Cloud. It is well-suited for teams needing real-time troubleshooting, container/Kubernetes visibility, and efficient time-series retention while weighing the tradeoffs of closed-source UI/cloud components.

Prometheus is an open-source systems and service monitoring platform built around a time-series database. It collects metrics from instrumented targets, lets you query them with PromQL, and supports alerting based on rules.

Key Features

• Multi-dimensional time series data model using labels for flexible filtering and aggregation
• PromQL query language for ad-hoc analysis, dashboards, and alert conditions
• Pull-based metric scraping over HTTP with support for static configs and service discovery
• Alert rule evaluation with alert generation (commonly paired with Alertmanager)
• Federation support for hierarchical and cross-environment aggregation
• Remote write/read integrations for long-term storage and interoperability

Use Cases

• Monitoring Kubernetes clusters and cloud-native services via dynamic service discovery
• Application and infrastructure telemetry for SRE/DevOps dashboards and alerting
• Central metrics collection for microservices, batch jobs (via push gateway patterns), and exporters

Limitations and Considerations

• Built-in storage is optimized for a single-node TSDB; long-term retention and global scale typically require external remote storage integrations

Prometheus is a strong fit when you want a reliable, standards-based metrics platform with powerful querying and a broad ecosystem of exporters and integrations. It is widely used for cloud-native monitoring and alert-driven operations.

Glances is an open-source, cross-platform system monitoring tool designed as an alternative to tools like top/htop. It provides real-time insights into system resources and processes, and supports local or remote monitoring via terminal, web interface, and APIs.

Key Features

• Terminal-based dashboard showing CPU, memory, load, processes, disk I/O, network, filesystem, uptime, and system info
• Built-in web UI for monitoring from a browser on any device
• Client/server modes for remote monitoring, including discovery of available Glances servers
• RESTful JSON API and XML-RPC server for integrations and automation
• Pluggable architecture with plugins for sensors and hardware metrics (e.g., temperatures and fan speeds)
• Container monitoring support (notably Docker and Podman)
• Export metrics to external systems or files, including CSV and JSON outputs

Use Cases

• Monitoring a single server or workstation interactively from the terminal
• Remote monitoring of multiple machines via web UI or API integrations
• Exporting system metrics to external databases or monitoring pipelines

Limitations and Considerations

• Some functionality (web UI, specific plugins, exports) requires optional dependencies beyond the minimal installation

Glances fits well when you want a lightweight, interactive overview of system health while also enabling programmatic access and metric exports for broader observability workflows. Its cross-platform support makes it practical for mixed OS environments.

cAdvisor (Container Advisor) is a daemon that monitors running containers and the host machine to provide visibility into resource usage and performance characteristics. It collects, aggregates, processes, and exports container-level and machine-wide statistics.

Key Features

• Per-container metrics including CPU, memory, filesystem, and network statistics
• Historical resource usage with summaries and histograms
• Built-in web UI for interactive inspection of container and host metrics
• Versioned remote REST API for raw and processed stats
• Export support via storage plugins for external backends
• Works with Docker and is designed to support other container runtimes via a container abstraction model

Use Cases

• Monitoring container resource consumption on single hosts
• Feeding container metrics into an observability pipeline via supported exports
• Troubleshooting noisy-neighbor and resource isolation issues in container environments

Limitations and Considerations

• Accurate collection typically requires elevated access and visibility into host/container runtime paths and kernel interfaces
• Feature availability and metric detail can vary depending on container runtime and host OS configuration

cAdvisor is a practical foundation for container monitoring, offering both a lightweight UI for quick inspection and APIs/exports for integration into larger monitoring stacks. It is commonly deployed as a host-level daemon, including in orchestrated environments where per-node metrics are needed.

Zabbix is an enterprise-class, open-source distributed monitoring and observability solution for tracking performance and availability across IT and OT environments. It collects metrics from agents and agentless sources and provides centralized visibility, alerting, and reporting.

Key Features

• Agent-based and agentless metric collection for servers, network devices, services, and applications
• Automatic discovery and template-based monitoring for rapid onboarding
• Real-time problem detection, correlation, and root-cause analysis workflows
• Flexible alerting and notifications with multiple delivery channels and integrations
• Dashboards and visualizations including graphs, maps, and topology views
• Distributed monitoring for remote sites and large environments, including multi-tenant use
• Built-in reporting, auditing, SLA calculations, and HTTP-based data streaming

Use Cases

• Infrastructure monitoring for networks, servers, virtual machines, and container platforms
• Application and service monitoring with proactive alerting and SLA tracking
• Centralized observability for multi-site or managed service provider environments

Zabbix is a mature, scalable platform suited for organizations that need deep visibility across diverse systems with strong alerting and flexible data collection options. It can serve as a unified monitoring backbone for both small deployments and large, distributed environments.

Pulse is a unified monitoring platform that brings Proxmox (VE/PBS/PMG), Docker/Podman, and Kubernetes visibility into a single dashboard. It combines real-time health, historical metrics, and alerting, with optional AI-assisted insights for troubleshooting and root-cause analysis.

Key Features

• Unified dashboard for nodes, VMs, containers, and Kubernetes workloads
• Agent-based monitoring with platform auto-detection
• Persistent metrics history with configurable retention
• Smart alerting with webhook-based notifications and integrations
• Proxmox-focused capabilities like backup visibility (PBS) and related infrastructure views
• Optional AI assistant features for natural-language querying and alert/finding analysis
• Security-oriented design including credential encryption at rest and scoped access
• SSO support via OIDC for centralized authentication

Use Cases

• Monitor a homelab or SMB stack running Proxmox plus Docker and/or Kubernetes
• Consolidate multiple hosts/clusters into a “single pane of glass” dashboard
• Reduce noisy alerting by correlating issues and investigating incidents faster

Pulse is well-suited for operators who want practical infrastructure monitoring without building a large, complex observability stack. Its unified agent and Proxmox-first focus make it particularly attractive for Proxmox-centric environments.

Dashdot is a modern server dashboard designed for smaller private servers. It provides a real-time overview of host metrics and system status via a polished glassy UI.

Key Features

• Real-time system metrics including CPU, memory, disk, and network usage presented in a responsive dashboard
• Web-based UI built with React and Node.js, designed for easy self-hosted deployment
• Docker-based quick-install with multi-architecture images (AMD64 and ARM)
• Lightweight, glassmorphism design with customizable widgets
• Comprehensive installation and configuration options documented on the official site
• Live demo available for exploration in the project’s official repository's demo

Use Cases

• Monitoring small private servers and home labs
• Observability of multiple VPS or private servers from a single dashboard
• Quick on-boarding for admins needing at-a-glance status of disks, networks, memory, and CPU

Limitations and Considerations

• The speed test feature can consume significant bandwidth; you can reduce impact by adjusting the speed test interval via an environment variable as described in the installation docs

Conclusion

Dashdot provides real-time server metrics through a modern, self-hosted dashboard. It can be deployed via Docker and explored via a live demo; official docs cover installation and configuration.

PeaNUT is a lightweight web dashboard for monitoring UPS devices through a Network UPS Tools (NUT) server. It provides a clean UI for live status and statistics, plus an HTTP API for integrations and automation.

Key Features

• Monitor one or multiple UPS devices exposed by a NUT server
• Real-time device statistics and status display
• Execute supported NUT commands and change writable variables
• Configurable via web UI with optional YAML-based configuration
• Built-in REST API endpoints for device data, commands, and health checks
• Prometheus metrics endpoint for monitoring and alerting
• Optional InfluxDB v2 integration for time-series dashboards (for example with Grafana)
• WebSocket terminal-style access for direct communication with the NUT server
• Optional Basic Authentication for UI and API access

Use Cases

• Homelab dashboard to track battery charge, runtime, load, and UPS state
• Integrate UPS status into monitoring/alerting systems via Prometheus
• Centralize visibility of multiple UPS devices across a network

PeaNUT is well-suited for users who already run NUT and want a modern, minimal web UI plus integration-friendly endpoints. It focuses on UPS visibility and control while remaining lightweight and easy to deploy.

Scraparr is a Prometheus exporter that collects and exposes metrics from the *arr suite (Sonarr, Radarr, Lidarr and similar services). It provides a scrapeable HTTP metrics endpoint intended for integration with Prometheus and visualization with Grafana.

Key Features

• Exposes detailed metrics for *arr services (requests, queue, backlog, import/scan status, per-series details when enabled)
• Prometheus-compatible /metrics HTTP endpoint (default port 7100)
• Configurable via config.yaml or environment variables; supports multiple service instances via config file aliases
• Lightweight Python implementation with Docker and Docker Compose deployment options
• Built for extensibility and community contributions; supports detailed per-series metrics when enabled
• Suitable for integration into alerting and dashboarding stacks (Prometheus + Grafana)

Use Cases

• Monitor health, API availability, and backlog of Sonarr/Radarr/Lidarr instances
• Feed metrics into Prometheus for alerting on failed downloads, stalled queues, or connectivity issues
• Provide a Grafana dashboard view of *arr performance and activity across multiple instances

Limitations and Considerations

• Environment variables do not support configuring multiple instances; multiple services require the config.yaml with aliases to avoid metric name collisions
• Requires proper API keys and reachable URLs for each *arr service; Docker variants may need host network adjustments for local service access
• Community-maintained Helm and Unraid templates exist but may not be officially maintained by the project

Scraparr is a focused tool for exporting *arr application metrics to Prometheus. It is lightweight and configuration-driven, making it easy to add to existing monitoring stacks for visibility into media automation components.