Directory Observability & MonitoringAPM & Distributed TracingSigNoz

SigNoz

Open Source

About SigNoz

SigNoz provides an observability platform covering APM, logs, traces, infrastructure, LLM telemetry, alerts, and dashboards. It is built on OpenTelemetry instrumentation rather than vendor-specific SDKs. Telemetry data is queried on a columnar datastore using query builder, PromQL, or ClickHouse SQL. SigNoz offers an MCP server and an AI assistant called Noz for working with telemetry inside coding agents or SigNoz Cloud. The product is available as SigNoz Cloud or as Self-Hosted SigNoz.

Products

APM: Provides P99, Apdex, database calls, and external calls per service.
Logs: Provides columnar database search with trace correlation built in.
Tracing: Loads and analyzes traces with up to a million spans.
Alerts: Supports threshold, anomaly, and Apdex alerts on any telemetry signal.
LLM Observability: Covers OpenAI, Azure OpenAI, Gemini, OpenRouter, LiteLLM, and agent telemetry.
Infra Monitoring: Covers Kubernetes, hosts, and cloud metrics next to every service.

Used for

Tracking P99 latency, Apdex, database calls, and external calls per service
Searching logs with trace correlation
Loading and analyzing traces with up to a million spans
Creating threshold, anomaly, and Apdex alerts on telemetry signals
Investigating incidents, tuning alerts, and building dashboards using Noz

Who it is for

Fast-scaling teams that need observability to scale with them.
Larger organizations that need data residency, compliance, and support.
Solo developers and small startups.
Organizations with compliance and security requirements.

From SigNoz's own website, read 2026-10-24. How we build listings.

How to evaluate APM & Distributed Tracing

This is how the CIOPages Research Team evaluates this category. It is not an assessment of SigNoz. It comes from our Observability & APM Platforms buyer guide.

22%

Telemetry Coverage & Correlation

Unified metrics, traces, logs, real-user monitoring, synthetics, and continuous profiling; automatic correlation across signals; service maps and topology; Kubernetes, container, serverless, and cloud-provider auto-discovery; breadth and quality of the integration catalog

22%

Cost Control, Cardinality & Data Governance

Pricing-unit transparency and overage terms; high-cardinality handling without runaway cost; ingest-time filtering, sampling, aggregation, and tiered retention; a native or compatible telemetry pipeline; per-team budgets, quotas, and chargeback visibility so spend is governable, not just observable

20%

Distributed Tracing & APM Depth

End-to-end trace correlation across services and async boundaries, tail-based and adaptive sampling, code-level and method-level profiling, error tracking and exception grouping, database and dependency visibility, and frictionless OpenTelemetry trace ingestion alongside any native agent

16%

AIOps, Anomaly Detection & Root Cause

Quality of automated anomaly detection and alert noise reduction, causal versus correlative root-cause analysis, forecasting and capacity signals, and emerging agentic remediation — judged on accuracy during a real incident, not on the marketing name of the AI engine

12%

OpenTelemetry & Openness

Depth of native OTel support (traces, metrics, logs) versus a proprietary agent; whether collection is genuinely vendor-neutral or quietly locks you in; query-language portability; export and data-egress freedom; and how much re-instrumentation a future migration would actually require

Scale, Reliability & Operability

Proven performance at your peak event and series volumes, multi-region availability and platform SLA, data-retention options, RBAC and SSO, and — if self-hosted — the operational burden of running and upgrading the backend at high cardinality

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