Cloud-native Observability: From Metrics to Logs to Traces

Key Takeaways

• Observability goes beyond monitoring: not just detecting known issues, but understanding unknown ones.
• The three signal types—metrics, logs, and traces—together form the observability triad.
• OpenTelemetry is the vendor-neutral standard for telemetry data collection.
• Distributed tracing visualizes request paths across microservices.
• Observability platform costs can reach 5–15% of cloud infrastructure spending.

Monitoring answers the question: Is my system working? Observability answers: Why isn’t it working? In distributed cloud systems with hundreds of microservices, traditional monitoring is no longer enough. Observability—the combination of metrics, logs, and traces—makes complex systems debuggable.

From Monitoring to Observability

Traditional monitoring checks known states: CPU usage, memory consumption, HTTP status codes. Dashboards show red or green. This works for monolithic applications with predictable failure modes.

In microservice architectures, this approach fails: a slow API call might stem from an overloaded service three hops away. CPU dashboards show green across all services—the issue lies in interactions. Observability enables asking arbitrary questions about the system without having to define them in advance.

The Observability Triad: Metrics, Logs, Traces

Metrics are numerical time series: request rate, latency (p50, p95, p99), error rate, queue depth. Prometheus has become the de facto standard, often paired with Grafana for visualization. Metrics answer: What is happening right now?

Logs are textual entries with context: error messages, audit trails, debug information. Structured logging (JSON instead of plain text) makes logs machine-readable. The ELK Stack (Elasticsearch, Logstash, Kibana), Loki, and Datadog Logs are common platforms. Logs answer: What happened?

Traces follow a request across all involved services. Each hop is recorded as a span—with timing, service name, and metadata. Jaeger, Zipkin, and Datadog APM visualize trace waterfalls. Traces answer: Where is the problem?

OpenTelemetry: The Universal Standard

Before OpenTelemetry, every observability tool created vendor lock-in: Datadog Agent, New Relic Agent, Dynatrace OneAgent—all required proprietary instrumentation. Switching tools meant re-instrumenting the entire codebase.

OpenTelemetry (OTel) solves this as a vendor-neutral standard. Applications are instrumented once using OTel SDKs and can send telemetry data to any compatible backend—Grafana Cloud, Datadog, Honeycomb, Jaeger. The OTel Collector receives, processes, and exports data flexibly.

OTel is a CNCF project backed by all major observability vendors. The recommendation is clear: Always use OpenTelemetry for new instrumentation—never proprietary agents.

Keeping Costs Under Control

Observability costs can spiral out of control. Datadog bills exceeding 50,000+ Euro per month are not uncommon among mid-sized companies. The main drivers: every log entry, metric, and trace incurs ingestion and storage costs.

Cost optimization strategies: Sampling – don’t trace every request, but do it intelligently (Tail-Based Sampling traces only unusual requests). Log-Level Management – use Debug logs only in development, Info level in production. Retention Policies – move logs and traces to cold storage or delete them after 30 days.

Open-source stacks (Prometheus + Grafana + Loki + Tempo) eliminate licensing fees but require operational overhead. The trade-off: SaaS (more expensive, less effort) vs. self-hosted (cheaper, more ops).

Alerting: The Right Signal at the Right Time

Too many alerts are worse than none – alert fatigue causes critical alerts to be overlooked. Best practice: base alerts on SLOs (Service Level Objectives), not individual metrics.

Instead of alerting on “CPU > 80%” (often harmless), define: “99% of requests should be answered within 200ms.” When the error budget is exhausted, the system triggers an alert – regardless of whether CPU, memory, or an external service is the root cause. Tools like Sloth and Pyrra automate SLO-based alerting using Prometheus data.

Frequently Asked Questions

Header image source: Pexels / AS Photography

Translated from the German original using artificial intelligence. The German version is authoritative.

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