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Observability with OpenTelemetry: Understanding What Your Software Is Doing
What observability means, how logs, metrics and traces work together, and why OpenTelemetry has become the standard way to instrument modern applications.
When something goes wrong in production, the question is always the same: what happened, where and why? Observability is the ability to answer that quickly from the data your systems produce.
The three signals
- Logs: detailed records of events
- Metrics: numbers over time — request rates, error rates, latency, resource usage
- Traces: the path of a single request across services, showing where time was spent
Together they let engineers move from "something is slow" to "this database query in the payment service is slow for customers in one region".
Why OpenTelemetry
OpenTelemetry is an open, vendor-neutral standard for generating and collecting telemetry data. Instrument your code once, then send data to the monitoring tool of your choice — and switch tools later without re-instrumenting.
What to instrument
- Incoming requests and their outcomes
- Calls to databases, caches, queues and external APIs
- Background jobs
- Key business events, such as orders placed or payments failed
- AI model calls: latency, tokens and cost
Service level objectives
Define what "good" looks like for users — for example, 99.9% of checkout requests succeed within one second — and alert when you are at risk of missing it, not on every small fluctuation.
Avoiding noise
Too many alerts lead to ignored alerts. Alert on user-impacting symptoms, route alerts to owners and review them regularly.
Cost control
Telemetry data can get expensive. Sample traces sensibly, set log retention policies and drop data nobody uses.
See our DevOps and maintenance and support services.
