The slow request,start to finish,in one trace.
Spans arrive over OTLP from the SDK you already run, or from eBPF with no code changes at all. The service graph, the critical path and the exceptions come with the trace rather than as a separate product.
The model call is a span inside the request, so the inference time is in the waterfall.
Included on every plan, including the free tier.
Distributed traces with the service graph and critical path derived from them, nothing to wire up.
RED metrics per service and route, so a regression shows up before someone opens a trace.
Exceptions attached to the span that threw, with the logs for that span one click away.
GenAI spans in the same waterfall, so inference time sits next to database time in the same request.
Three steps, then it is reporting.
What that looks like as a query.
{ resource.service.name = "checkout" && duration > 2s }
Requests over the latency budget.
{ status = error && span.http.route != nil }
Failing routes, grouped by the route itself.
{ span.db.system = "postgresql" && duration > 500ms }
Queries holding the request open.
What application teams ask first.
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- How much instrumentation does this need?
- None to start. eBPF gives RED metrics and server and client spans without code changes. SDK instrumentation buys span detail rather than basic coverage.
- Can we keep our current tracing setup?
- Yes, if it speaks OTLP. The query API is Tempo- and Jaeger-compatible, so existing trace tooling and Grafana panels keep reading.
- Do traces and logs actually connect?
- They share a tenant and a trace ID. A span links to the logs written during it because both are in the same store, not because a connector matched them up afterwards.
- What happens to sampling?
- Sampling stays in your collector, where you can see it. The agent forwards what you decide to keep, and the pipeline that makes that decision is versioned in the portal.
Start on the free tier.
20K active series, 50 GB of logs and 50 GB of traces a month, 90-day retention, and the whole of Insights.