Building an OpenTelemetry-first observability stack
A practical path to instrumenting a production system with OpenTelemetry and wiring it into Prometheus, Grafana, Tempo, and Loki without boiling the ocean.
Teams often try to instrument everything at once when adopting OpenTelemetry: every service, all three signal types, a fully built out collector pipeline, on day one. That approach usually stalls before it ships. A narrower, sequenced rollout gets useful signal into production much faster.
Why OpenTelemetry over a vendor SDK
The main argument for OTel isn't philosophical, it's practical: instrumenting against a vendor-neutral API means the backend, Grafana Tempo today, something else in three years, can change without re-instrumenting every service. Given how often observability vendors get re-evaluated, that alone tends to pay for the migration cost.
Instrument traces first
Distributed tracing gives the most immediate value because it shows request flow across services in a way logs and metrics can't on their own. Auto-instrumentation libraries cover the common frameworks and HTTP clients, so most services get useful traces with a few lines of SDK setup before any custom spans are written. Metrics and structured logs correlated to trace IDs come next, once the trace backbone is in place.
Wiring the collector
The OpenTelemetry Collector sits between your services and your backends, doing batching, sampling decisions, and protocol translation in one place instead of inside every service. A minimal pipeline, an OTLP receiver, a tail-sampling processor to keep interesting traces without keeping everything, and exporters to Tempo, Prometheus, and Loki, covers most production needs without becoming its own maintenance project.
Defining SLOs once you can see the system
SLOs written before there's real telemetry to base them on tend to be guesses. Once traces and metrics are flowing, latency and error-rate SLOs can be set against actual observed distributions instead of round numbers that sound reasonable. That's also when alerting rules stop being noisy, because they're tuned to how the system actually behaves.
The stack matters less than the sequence. Traces first, then metrics and logs tied to the same trace context, then SLOs defined against real data, gets a team to a genuinely useful observability platform faster than trying to stand up every component simultaneously.