Best Self Hosted Alternatives to Lightstep Observability

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.

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.

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.

Parseable is a full-stack observability platform built to ingest, analyze and extract insights from all types of telemetry (MELT) data. It can run locally, in the cloud, or as a managed service, providing a unified way to explore signals across the stack.

Key Features

• Unified signals across MELT data for a single source of truth
• Predictive analytics and anomaly forecasting to anticipate issues
• Natural language and SQL querying across telemetry
• Hybrid execution engine with columnar storage and indexing for fast queries
• Granular access control and federated IAM
• Open standards and vendor-neutral design (OTel, Parquet compatibility)
• Cloud-ready with BYOC options

Use Cases

• Full-stack observability of applications, databases, infrastructure and networks
• AI workloads observability for telemetry from AI models and LLMs
• Product observability to analyze user behavior, feature adoption, and performance

Conclusion Parseable provides predictive observability with a unified data model, enabling faster insights and proactive incident response across the full telemetry stack.

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.