See what's actually happening in production.

No. We default to OpenTelemetry with Prometheus, Grafana, Loki, and Tempo because that stack is portable and easy on the budget. If Datadog, New Relic, Honeycomb, or Splunk suits your team better, we're happy to wire that up instead. The instrumentation layer stays portable whichever way you go.

We use cardinality control, sampling, and tiered retention. The pipeline is designed so high-volume data such as debug logs and span samples is kept only briefly, while the signals that matter are held long enough to spot trends.

Yes. We usually run the new stack alongside the old one for a few weeks to confirm the two match up, then move alerting over once your team is comfortable. There's no risky flag day.

Metrics are cheap numeric time series that tell you something is wrong, such as a rising error rate. Logs give you the detailed context of individual events. Traces follow a single request across services so you can see where the time or the failure actually happened. You need all three to answer most questions quickly.

Alert on symptoms your users would notice, such as checkout failing or latency breaching its SLO, not on raw causes like high CPU. Cause-based alerts create noise without telling you whether anyone is affected, while symptom-based alerts map directly to user pain.

Cardinality is the number of unique label combinations on your metrics, and it drives storage and query cost more than raw volume does. Putting unbounded values like user IDs into labels can explode cost overnight, so we design label sets deliberately and watch the high-cardinality offenders.

We weigh your team's appetite for running infrastructure against the convenience and price of a managed tool. Because the instrumentation is OpenTelemetry, the choice is reversible: you can start open-source and move to Datadog or Honeycomb later, or the other way around, without re-instrumenting.

Once you run more than a few services, yes. Logs and metrics tell you a service is slow, but only traces show you which downstream call is responsible across a request's whole path. For microservices and event-driven systems, tracing is what turns a multi-hour hunt into a few minutes.

We use sampling for high-volume traces, cardinality limits on metrics, and tiered retention so debug data is kept only briefly while the signals that matter are held long enough to spot trends. The pipeline is tuned so you pay for insight, not for storing noise.

The overhead from OpenTelemetry SDKs is small, typically low single-digit percentages, and tail-based sampling keeps it lower still under load. The diagnostic time you save during an incident far outweighs the marginal runtime cost of carrying the instrumentation.