Transaction-level operations
At Level 3, you can alert on individual transactions, analyze trace-based metrics, and investigate specific request flows.
Alerting
| What to alert on | Example |
|---|---|
| Latency percentiles | P99 latency > 2s for payment flow |
| Span errors | Database span errors > threshold |
| Trace-based SLOs | Critical path success rate < 99% |
SLOs
| SLO type | Example |
|---|---|
| Transaction success | 99% of checkout flows complete successfully |
| Critical path latency | 95% of payment transactions < 1s |
| End-to-end latency | 90% of user journeys < 3s total |
Dashboards
| Dashboard type | What you see |
|---|---|
| Trace analysis | Span breakdown, latency distribution |
| Flame graphs | Where code spends time (profiling) |
| Frontend performance | Core Web Vitals, user experience metrics |
| AI/LLM metrics | Token usage, model latency, prompt analysis |
Investigation
| Tool | How you use it at Level 3 |
|---|---|
| Trace Explorer | Search traces by attributes, find slow spans |
| Trace-to-logs | Jump from trace span to related logs |
| Trace-to-profiles | See code-level performance for a trace |
| Session replay | Watch what the user actually experienced |
At Level 4, you’ll alert on custom metrics and KPIs.
Script
At Level 3, your operational practices get much more precise. Instead of alerting on service averages, you can alert on specific transaction patterns.
Imagine an alert that fires when p99 latency for the payment flow exceeds 2 seconds. Or when database span errors cross a threshold. Or when your critical path success rate drops below 99 percent. These are transaction-level alerts that catch issues service-level metrics would miss.
Your SLOs evolve too. You can set objectives like “99 percent of checkout flows complete successfully” or “95 percent of payment transactions complete in under 1 second.” These are meaningful targets that map directly to user experience.
For investigation, you’ve got powerful tools: Trace Explorer for searching traces by attributes, trace-to-logs for jumping from a span to relevant log lines, trace-to-profiles for seeing code-level performance, and session replay for watching what the user actually experienced.
At Level 4, these same concepts apply to custom metrics, but we’ll get to that next.