Self-Hosted-Backends für Distributed Tracing

Grafana Tempo is a high-scale distributed tracing backend and columnar trace database. It serves as an observability data store that persists and queries spans and traces using OpenTelemetry standards, allowing for the analysis of request flows across microservices. The system distinguishes itself by using an object-store based backend with columnar Parquet storage. This architecture enables efficient attribute searching and large-scale data retrieval through dedicated attribute columnization and block-based data partitioning. It includes a specialized TraceQL query engine for filtering trace

Jaeger is a distributed tracing platform used for collecting, storing, and visualizing request flows across microservices. It identifies performance bottlenecks and errors by tracking requests as they move through multiple service boundaries. The system includes telemetry collectors, a multi-tenant backend, and a trace visualizer. The platform provides a multi-tenant tracing infrastructure that isolates data and queries by tenant to support shared environments. It supports standardized telemetry ingestion via the OpenTelemetry Protocol over gRPC and HTTP. To manage storage costs and overhead,

SigNoz is a full-stack observability platform designed to collect, store, and visualize metrics, logs, and distributed traces in a unified environment. It leverages OpenTelemetry-based data collection to ingest telemetry from diverse sources using vendor-neutral protocols, ensuring interoperability across complex microservices architectures. The platform utilizes a high-performance columnar storage engine to enable rapid aggregation and filtering, providing a centralized backend for monitoring application health and performance. What distinguishes the platform is its focus on automated instru

Uptrace is an OpenTelemetry-based observability platform designed to collect, store, and analyze distributed traces, metrics, and logs. It functions as a centralized logging backend, a distributed tracing system, and a metrics engine to monitor application performance and system health. The platform is distinguished by AI-powered operational capabilities, allowing users to query telemetry data and manage monitoring dashboards using natural language. It specifically includes specialized monitoring for generative AI pipelines, tracking token usage and response quality for LLM interactions and r

Zipkin is an open-source distributed tracing system designed to collect, store, and visualize timing data across complex service architectures. It provides a platform for monitoring request lifecycles, enabling developers to identify latency bottlenecks and performance issues by tracking operations as they move through heterogeneous service environments. The system distinguishes itself through a standardized data model and a pluggable storage architecture that supports various backend databases. It utilizes sampling strategies to manage telemetry volume and employs asynchronous collection met

SkyWalking is a comprehensive observability stack and application performance monitoring platform. It functions as a distributed tracing system and an AI application monitor, providing a centralized suite for collecting and analyzing logs, metrics, and traces to maintain the health of containerized architectures. The platform distinguishes itself through a service topology visualizer that renders interactive maps of infrastructure dependencies and communication patterns. It also includes specialized capabilities for generative AI workflow observation to track the execution flow and performanc

HyperDX is an OpenTelemetry observability platform that provides centralized log management, distributed tracing, and a self-hosted monitoring stack. It functions as a unified system for collecting, indexing, and visualizing logs, metrics, and traces from cloud and container environments. The platform distinguishes itself with specialized tooling for large language model monitoring and session replay, allowing user interactions in the browser to be linked to backend telemetry. It employs schema-less JSON parsing to index structured logs dynamically and uses source maps to resolve minified sta

SkyWalking is an application performance monitoring system and observability platform designed to collect and analyze metrics, traces, and logs from distributed microservices. It functions as a distributed tracing platform and a telemetry data pipeline that ingests and aggregates observability data from various language agents. The project features an AI-powered anomaly detector that uses machine learning to calculate metric baselines and identify irregular URI patterns. It includes an eBPF performance profiler for diagnosing CPU and network bottlenecks at the kernel level and generates inter

Quickwit is a cloud-native, distributed search engine designed for observability data such as logs, traces, and metrics. It functions as an observability backend that decouples compute from storage by persisting indices directly in S3-compatible cloud object stores. The system is distinguished by its compatibility with the Elasticsearch REST API, allowing it to integrate with existing clients and log shippers without reconfiguration. It also serves as an OpenTelemetry data indexer, ingesting technical data via the OpenTelemetry Protocol using gRPC and HTTP. The engine utilizes a hybrid of co

Pinpoint is a distributed application performance management tool designed to trace requests and monitor metrics across large-scale distributed architectures. It functions as a request tracer, topology mapper, and JVM application monitor, providing a backend capable of collecting and visualizing trace data from OpenTelemetry compatible sources. The system distinguishes itself through a combination of bytecode-based instrumentation via a Java agent and topology-based visualization that renders live maps of service interconnections. It captures execution flow across asynchronous boundaries, suc

This project is a comprehensive software observability suite and application performance monitoring platform designed to track runtime errors, performance bottlenecks, and system health. It functions as a centralized diagnostic service that aggregates and categorizes exceptions, providing the infrastructure necessary to visualize complex execution paths across distributed systems and microservices. The platform distinguishes itself through a high-throughput distributed event ingestion pipeline and a columnar storage analytics engine that enables rapid aggregation of large-scale performance me

Coroot is an observability platform and Kubernetes performance monitor that utilizes eBPF to automatically collect metrics, logs, and traces without requiring manual code instrumentation. It functions as an OpenTelemetry trace analyzer and an LLM observability gateway, exposing system health data to large language models through the Model Context Protocol. The platform differentiates itself by combining automated root cause analysis and AI-driven diagnostics to investigate performance regressions. It also includes a cloud cost monitoring tool that attributes infrastructure spending to specifi

Pinpoint is a distributed application performance monitoring and tracing system. It functions as an application performance monitor and topology visualizer designed to analyze the execution behavior of large-scale distributed applications. The system uses bytecode instrumentation to monitor applications without requiring changes to the original source code. It captures call stacks and request flows across interconnected services to visualize system dependencies and generate real-time architectural maps of communication patterns. The platform covers a broad range of observability capabilities

OpenObserve is a unified observability data platform designed to ingest, store, and analyze logs, metrics, and traces. It functions as a cloud-native monitoring tool that centralizes telemetry from diverse sources, including standard collectors and cloud service providers, into a single, scalable system. By utilizing a columnar storage engine backed by object storage, the platform enables efficient long-term data retention and high-performance analytical querying. The platform distinguishes itself through deep integration with artificial intelligence, allowing users to query data using natura

Highlight is a full-stack observability platform and monitoring system that aggregates logs, errors, and distributed traces to provide a unified view of application health. It functions as a distributed tracing system, an error monitoring service, and a session replay tool. The platform is available as a dockerized monitoring stack for self-hosted deployments on Linux. It distinguishes itself by combining backend observability with a visual recording system that captures document object model changes and network requests to replay user interactions. The system covers several core capability

Cat is a distributed application performance monitoring tool and tracing framework designed to track transactions, latency, and health across distributed services. It functions as a Kubernetes-native monitoring stack that utilizes multi-language monitoring clients and a real-time alerting system to maintain system visibility. The system provides monitoring clients for Java, Go, Python, Node.js, and C++ to collect performance metrics and trace data. It distinguishes itself by sampling request flows to record call chains and identify bottlenecks, while using a monitoring engine to trigger immed