SigNoz — Open Source APM & Observability Platform

What it is

SigNoz is an open-source application performance monitoring (APM) and observability platform. It combines logs, distributed traces, and metrics in a single interface, replacing the need for separate tools like Datadog, New Relic, or Grafana+Loki+Tempo.

SigNoz uses ClickHouse as its storage backend for high-performance querying across all three signal types. It supports native OpenTelemetry instrumentation, so you do not need vendor-specific agents.

How it saves time or tokens

Traditional observability stacks require deploying and maintaining separate systems for logs, traces, and metrics. SigNoz unifies these into one platform, reducing operational overhead. The OpenTelemetry-native approach means your instrumentation code is portable across vendors.

The ClickHouse backend handles high-cardinality data efficiently, so dashboards and queries remain fast even at scale. For AI-assisted debugging, having all signals in one place means LLMs can correlate logs with traces without switching tools.

Additionally, the project's well-structured documentation and active community mean developers spend less time troubleshooting integration issues. When AI coding assistants generate code for this tool, they can reference established patterns from the documentation, producing correct implementations with fewer iterations and lower token costs.

How to use

1. Deploy SigNoz via Docker Compose:

git clone https://github.com/SigNoz/signoz.git
cd signoz/deploy
docker compose -f docker/clickhouse-setup/docker-compose.yaml up -d

1. Instrument your application with OpenTelemetry:

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter

trace.set_tracer_provider(TracerProvider())
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(OTLPSpanExporter(endpoint='http://localhost:4317'))
)

1. Open the SigNoz UI at http://localhost:3301 to view traces, metrics, and logs.

1. Create dashboards, set alerts, and explore service maps.

Example

# OpenTelemetry Collector config pointing to SigNoz
exporters:
  otlp:
    endpoint: 'signoz-otel-collector:4317'
    tls:
      insecure: true
service:
  pipelines:
    traces:
      exporters: [otlp]
    metrics:
      exporters: [otlp]
    logs:
      exporters: [otlp]

Related on TokRepo

• AI Tools for Monitoring — Monitoring and observability tools for production systems
• AI Tools for DevOps — DevOps tooling that integrates with observability platforms

Common pitfalls

• Underprovisioning ClickHouse storage. Traces and logs generate significant data volume. Plan for retention policies and disk capacity before going to production.
• Sending too many trace spans without sampling. Use tail-based or head-based sampling to control data volume while preserving visibility into errors and slow requests.
• Not setting up alerts from day one. Dashboards are useful for exploration, but alerts catch incidents before users report them.
• Failing to review community discussions and changelogs before upgrading. Breaking changes in major versions can disrupt existing workflows. Pin versions in production and test upgrades in staging first.