Best Self Hosted Alternatives to AppDynamics

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. (netdata.cloud)

Key Features

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

Use Cases

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

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. (raw.githubusercontent.com)
• OpenTelemetry support is noted as "coming soon" in documentation; users relying heavily on OpenTelemetry may need to plan integrations or use exporters. (raw.githubusercontent.com)
• Feature parity varies by platform (Linux has the most comprehensive coverage); some platform-specific collectors or deep kernel metrics are not available everywhere. (raw.githubusercontent.com)

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.

Grafana is an open source observability and data visualization platform for querying, visualizing, and alerting on metrics, logs, and traces across many backends. It provides interactive dashboards and exploration workflows so teams can monitor systems and troubleshoot issues from a single interface.

Key Features

• Dashboards with flexible visualizations and templating for reusable views
• Explore workflows for ad-hoc querying and drilldowns across time ranges and data sources
• Unified alerting with rule evaluation and multi-channel notifications
• Pluggable data source and panel ecosystem to integrate with many metrics, log, and trace systems
• Sharing and collaboration features for teams (dashboards, annotations, and permissions)

Use Cases

• Infrastructure and Kubernetes monitoring using time-series backends
• Centralized log exploration and correlation with metrics for incident response
• Application observability by visualizing traces and service performance trends

Limitations and Considerations

• The experience and capabilities depend heavily on the chosen data sources and plugins
• Operating at very large scale can require careful tuning of storage backends and dashboard/query design

Grafana is well-suited for organizations that want a single “pane of glass” across diverse telemetry sources. Its extensible plugin model and alerting make it a common foundation for observability stacks in both homelabs and enterprise environments.

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.

Sentry is a debugging platform that helps developers detect, trace, and fix application issues by connecting errors with performance and runtime context. It supports many SDKs and integrates with common development workflows to speed up investigation and resolution.

Key Features

• Error and exception aggregation with stack traces and release context
• Application Performance Monitoring (APM) with distributed tracing and transaction breakdowns
• Alerting and issue triage tools to prioritize impactful problems
• Source code and deployment context support (for example commits and releases)
• Broad SDK ecosystem across languages and frameworks for capturing events and traces

Use Cases

• Monitor production applications for crashes and regressions after releases
• Investigate latency and bottlenecks using traces and transaction performance data
• Centralize error reporting across multi-service, multi-language environments

Limitations and Considerations

• Full-feature deployments typically require multiple components and supporting services, increasing operational complexity

Sentry is well-suited for teams that want a single platform to correlate errors, traces, and performance signals. It provides actionable context to reduce time-to-diagnosis and improve application reliability.

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.

SigNoz is an open-source observability platform designed to collect, store, and visualize logs, metrics, and traces in a single interface. Built on OpenTelemetry, SigNoz enables correlated signals and unified dashboards, with ClickHouse serving as the log datastore. (github.com)

Key Features

• Unified observability across logs, metrics, and traces
• OpenTelemetry-native ingestion with semantic conventions
• ClickHouse-backed log storage for fast queries
• DIY query builder, PromQL support, and flexible dashboards
• Alerts across signals with anomaly detection capabilities
• Tracing visuals including flamegraphs and detailed span views

Use Cases

• Instrumenting applications with OpenTelemetry to achieve end-to-end visibility across services
• Correlating logs, metrics, and traces to troubleshoot microservices and distributed systems
• Providing centralized observability for cloud-native environments with unified dashboards

Conclusion: SigNoz offers a single, OpenTelemetry-native platform to observe modern applications through correlated signals, scalable storage, and flexible visualization and alerting capabilities. It emphasizes openness, data correlation, and end-to-end debugging across logs, metrics, and traces.

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.

Beszel is a lightweight server monitoring platform built around a central hub and per-host agents. It collects historical system metrics and container statistics and presents them in a simple web interface with alerting.

Key Features

• Hub-and-agent architecture for monitoring multiple systems from a single dashboard
• Historical metrics for host resources (CPU, memory, disk usage and I/O, network, load)
• Docker and Podman container stats with per-container CPU, memory, and network history
• Configurable alerts for CPU, memory, disk, bandwidth, temperature, load average, and system status
• Multi-user support with admin sharing of monitored systems
• OAuth2/OIDC authentication with optional password-auth disablement
• Automatic backups with restore support, including S3-compatible storage targets
• REST API for integrating metrics and management into scripts and applications

Use Cases

• Homelab or small fleet monitoring with minimal resource overhead
• Tracking server and container performance trends over time
• Basic alerting for capacity and health signals (disk, bandwidth, temperature, uptime)

Beszel fits teams and individuals who want straightforward monitoring without the complexity of larger observability stacks. Its small footprint, container awareness, and built-in backups make it practical for self-managed environments.

VictoriaMetrics is a high-performance time series database designed for monitoring and observability workloads. It can act as long-term storage for Prometheus and integrates well with common metrics ecosystems such as Grafana.

Key Features

• Single-node and clustered deployment options
• Prometheus-compatible ingestion (including remote write) and querying, with support for PromQL and MetricsQL
• Multi-protocol ingestion support, including Graphite, InfluxDB line protocol, OpenTSDB, CSV, and JSON line formats
• High ingestion throughput and efficient storage compression for large cardinality metrics
• Stream aggregation for transforming and aggregating incoming metrics
• Built-in features for operational safety such as relabeling and cardinality limiting

Use Cases

• Cost-effective long-term storage backend for Prometheus metrics
• Centralized metrics ingestion from many sources (Kubernetes, IoT, APM) with unified querying
• High-volume telemetry storage and analytics where resource efficiency is critical

VictoriaMetrics is well-suited for teams that need a Prometheus-compatible TSDB with strong performance characteristics, flexible ingestion options, and scalable deployment models.

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.

LoggiFly is a lightweight log-monitoring service that watches container logs for predefined keywords or regular expressions and sends notifications when matches occur. It is designed for fast, targeted alerting on errors, security events, or application-specific log patterns across local and remote container hosts.

Key Features

• Plain text, regex, and multi-line log pattern detection
• Notifications via ntfy or Apprise (supports many notification providers) and optional custom endpoints
• Optional log attachments included with alerts for context
• Trigger container stop or restart on matched patterns to mitigate crash loops or critical errors
• Configuration via YAML, environment variables, or Docker container labels
• Automatic reload when configuration changes are detected
• Support for multiple remote hosts and compatible with Docker, Docker Swarm, and Podman

Use Cases

• Alert on suspicious activity such as repeated failed login attempts in service logs
• Notify on application crashes or critical exceptions with attached log context
• Automatically restart or stop a container when a known fatal error pattern appears

LoggiFly fits well in homelabs and production-like setups where simple, actionable log-based alerting is needed without running a full observability stack. It focuses on flexible matching, straightforward configuration, and reliable notifications.

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.

LogForge is a developer-focused monitoring and alerting dashboard for Docker environments. It autodetects containers, streams live logs and provides UI-driven rules, notifications and safe remediation actions for containerised services.

Key Features

• Automatic Docker service discovery and status (running, crashed, stopped)
• Real-time log streaming and filtering per container
• Interactive per-container terminal access and file system viewer
• UI-driven Alert Engine with one-click rule templates and scoped rules
• Safe auto-remediation (restart/stop/kill/start/run scripts) with cooldowns, backoff and verification delays
• Multi-step actions and notification channels (Email, Slack, Discord, Telegram, Gotify and others)
• Alert history, acknowledgement, duplicate-rule protection and noise controls (case sensitivity, AND/OR matches, ignore lists)
• Test notifications, health/self-check endpoints and configurable container grouping
• Docker Compose friendly deployment and minimal operational overhead

Use Cases

• Local development and staging: tail container logs, open interactive shells, and diagnose crashes without SSH.
• Small teams running Dockerized services: set up keyword- and event-based alerts to detect regressions and performance issues quickly.
• Automated incident response: define safe, guardrailed remediation workflows to restart or run validated scripts when containers fail.

Limitations and Considerations

• Core backend is source-available and interacts directly with the Docker socket; several non-core components (Alert Engine, Notifier and other tooling) are proprietary/restricted per the project's licensing notes.
• Designed primarily for Docker-first workflows; integrations with large-scale observability stacks (e.g., Loki/ELK) may require additional tooling or customization.

LogForge provides a compact, self-hosted alternative to heavyweight observability stacks with an emphasis on developer workflows and safe automation. It is intended for teams that want quick visibility and guarded remediation for Docker container fleets.

Meshping is a lightweight network monitoring service that concurrently pings multiple targets and records detailed latency histograms. It runs traceroutes, performs Path MTU discovery for each hop, and renders visual maps and heatmaps to help locate weak links and routing issues.

Key Features

• Concurrent ICMP probing of many targets with per-target latency histograms (not simple averages)
• Traceroute integration with hop-by-hop Path MTU discovery and AS information
• SVG network topology maps that highlight outage locations and routing loops
• Heatmaps and histogram visualizations for spotting multimodal latency distributions
• Prometheus-compatible /metrics endpoint for scraping and integration with existing monitoring stacks
• Dynamic target management (add/remove targets at runtime) and optional peering between Meshping instances
• Docker images and a simple web UI including a mobile-friendly layout

Use Cases

• Troubleshooting WAN and datacenter connectivity by pinpointing where latency or packet loss occurs
• Comparing latency across multiple endpoints or monitoring locations for capacity planning and performance trends
• Distributed measurements via peered Meshping instances to observe end-to-end behavior from multiple vantage points

Limitations and Considerations

• ICMP probing and traceroute functionality require privileges or container capabilities (e.g., CAP_NET_RAW) to send raw packets
• Default local storage uses SQLite, which may not scale for very large deployments or extremely high sampling rates
• Meshping focuses on measurement and visualization; it has no built-in advanced alerting engine and relies on Prometheus or external tools for alerting
• When deployed behind some proxies, specific proxy settings may be required for good UI responsiveness

Meshping is suited for network operators and engineers who need detailed, visual latency and path visibility without heavy infrastructure. It complements metric and alerting stacks by providing raw-response histograms, traceroutes, and topology visualizations for root-cause analysis.