At a Glance:
SigNoz is an open-source observability platform that unifies metrics, traces, and logs with native application performance monitoring, distributed tracing, and alerts, built on OpenTelemetry and ClickHouse as a cost-effective alternative to Datadog and New Relic.
Overview:
SigNoz is an open-source observability platform designed to consolidate application monitoring, log management, and distributed tracing into a single tool. It provides built-in application performance metrics like p99 latency, error rates, and Apdex, alongside a centralized log management system with a query builder and customizable dashboards. Distributed tracing uses OpenTelemetry to help developers track requests across microservices, with Flamegraphs and Gantt charts for identifying bottlenecks. SigNoz also supports metrics ingestion, alerting on any telemetry signal with anomaly detection, and includes dedicated capabilities for monitoring LLM applications. It uses ClickHouse as its datastore and supports instrumentation for all major programming languages compatible with OpenTelemetry.
Key Decision Points:
Unified telemetry in a single tool: SigNoz combines logs, metrics, and traces in one platform rather than requiring separate tools for each signal.
Built on OpenTelemetry: Instrumentation and data collection rely on OpenTelemetry, which means the project avoids vendor-specific agents and supports all major programming languages that OpenTelemetry covers.
ClickHouse-backed datastore: Logs and other observability data are stored in ClickHouse, which the project highlights for fast query performance and lower resource requirements compared to Elastic during ingestion.
LLM observability support: SigNoz includes dedicated features for tracking LLM calls, token usage, and AI application behavior in production.
Correlated signals for debugging: Users can correlate metrics, logs, and traces to move between signals when investigating an issue, such as jumping from k8s infrastructure metrics to related traces at a specific timestamp.
Core Features:
Application Performance Monitoring: Out-of-box charts for p99 latency, error rate, Apdex, and operations per second, with visibility into database and external calls.
Logs Management: Centralized log storage with a query builder, quick filters, and the ability to create charts and dashboards from log data.
Distributed Tracing: OpenTelemetry-powered tracing across services with Flamegraphs, Gantt Charts, and detailed span-level views to identify performance bottlenecks.
Metrics and Dashboards: Ingestion of infrastructure and application metrics with a query builder, formula support, and panel types like time-series, bar chart, and pie chart.
LLM Observability: Monitoring of LLM calls, token usage, costs, and prompt and response behavior for AI applications in production.
Alerts and Anomaly Detection: Alert creation on logs, metrics, or traces with thresholds, notification channels, alert history, and anomaly detection.
Use Cases:
Developers monitoring microservices applications and needing to trace user requests across services to locate latency issues.
Teams consolidating logs, metrics, and traces into a single open-source platform instead of running separate Prometheus, Jaeger, and log management tools.
Engineers managing LLM-based applications and requiring observability into token consumption, costs, and production behavior.
Site reliability engineers setting up proactive alerts with anomaly detection across multiple telemetry signals.
Open-Source Alternative Value:
SigNoz positions itself as an open-source alternative to commercial observability platforms like Datadog and New Relic. Its architecture relies on OpenTelemetry for instrumentation and ClickHouse for data storage, which the project states frees users from vendor lock-in. The platform can be used in a fully open-source deployment, via a cloud service, or through a mix of both. README comparisons with Prometheus, Jaeger, Elastic, and Loki indicate that SigNoz specifically targets use cases where users want to avoid stitching separate tools together for metrics, logs, and traces, while also claiming benefits like lower resource requirements during log ingestion compared to Elastic and better high-cardinality data handling compared to Loki.
