What Is SigNoz and What Does It Do?
SigNoz is an open-source observability platform built on OpenTelemetry that consolidates traces, metrics, logs, APM, exceptions, LLM telemetry, alerts, and dashboards into a single tool. It is commonly positioned as an open-source alternative to Datadog. You can use it as SigNoz Cloud (a managed service) or run it yourself with Self-Hosted SigNoz. It fits teams that want one place to move from a symptom to the underlying evidence without switching between separate monitoring products.
The core idea: one platform instead of many tools
Most observability stacks end up as a patchwork — one vendor for APM, another for logs, a third for dashboards. SigNoz's stated goal is to collapse that into one OpenTelemetry-native platform so traces, metrics, and logs live together and stay correlated.
The practical payoff is troubleshooting speed. When a service slows down, you want to jump from a latency chart to the specific trace to the related log lines without re-authenticating into three different UIs. SigNoz is designed around that "no context switching" flow.
What you get in the platform
Based on the product's own description, the main capabilities are:
| Capability | What it covers |
|---|---|
| APM | P99 latency, Apdex, database calls, and external calls per service |
| Logs | Columnar database search with trace correlation built in |
| Tracing | Load and analyze traces with up to a million spans |
| Alerts | Threshold, anomaly, and Apdex alerts on any telemetry signal |
| LLM Observability | OpenAI, Azure OpenAI, Gemini, OpenRouter, LiteLLM, and agent telemetry |
| Infra Monitoring | Kubernetes, hosts, and cloud metrics shown next to each service |
| Dashboards | Reusable templates for services, infra, cloud, databases, and LLM usage |
Because everything is OpenTelemetry-native, instrumentation follows the open standard rather than a proprietary agent format — which matters if you want to avoid lock-in or already emit OTel data.
Cloud or self-hosted
SigNoz ships in two deployment models:
- SigNoz Cloud — a managed service, so you don't run the backend yourself. The site describes "simple usage-based pricing" and a pricing calculator for estimating monthly cost.
- Self-Hosted SigNoz — you run it on your own infrastructure, which the site frames as "the freedom to run on your infrastructure."
The choice usually comes down to operational appetite and data-residency needs: Cloud removes the maintenance burden, self-hosting keeps telemetry inside your own environment. If cost predictability as you scale is a concern, the pricing page and calculator are the place to check the actual numbers rather than assuming either option is cheaper.
Agent-native observability
A newer part of the product is aimed at AI-assisted workflows. SigNoz offers an MCP server that brings telemetry into coding agents, and an in-product AI teammate called Noz inside SigNoz Cloud that can investigate incidents, tune alerts, and build dashboards using the same production context your team sees. Example prompts shown in the product include "Why is my cache failing?", "Where can I monitor my k8s pods?", and "How can I optimize my signoz bill?"
This is relevant if your team already uses coding agents and wants them to reason over real telemetry instead of guessing.
Who it's for
SigNoz suits teams that:
- Want traces, metrics, and logs in one correlated view
- Prefer an OpenTelemetry-native, open-source foundation over a closed vendor agent
- Need either a managed service or a self-hosted deployment
- Are adding LLM or agent telemetry to their existing observability
If you only need a single narrow signal (say, just uptime checks), a full observability platform is more than you need. If you're consolidating several tools and want open standards at the base, SigNoz is built for exactly that.
Getting started
The site offers a free start path and a demo booking option. From there, the natural first steps are instrumenting a service with OpenTelemetry, confirming traces and metrics arrive, then layering in logs, alerts, and dashboards. The docs are the reference point for the concrete setup steps, since instrumentation details depend on your language and runtime.