OpenTelemetry - OTEL
OpenTelemetry is the second most active project in the CNCF,
with only Kubernetes being more active.
No Vendor Lock-in
Using an open standard keeps you from being tied to one vendor.
Easy to use
Using an open standard keeps you from being tied to one vendor.
All Use Cases
OpenTelemetry is your complete answer for all telemetry needs.
Standardized Observability
One standard for all telemetry signals boosts developer efficiency and teamwork consistency.
Everything OpsPilot
reads from your stack
OpsPilot ingests all four OTEL signal types and cross-correlates them to deliver expert-level analysis. Connect once via OpenTelemetry and get continuous AI-powered recommendations across your entire infrastructure.
Metrics
Numerical measurements over time. OpsPilot analyzes counters, gauges, and histograms to surface performance bottlenecks, cost waste, and degradation trends invisible to the human eye.
Logs
Structured and unstructured event records. OpsPilot mines log patterns, error rates, and severity distributions to detect anomalies and coverage gaps across your services.
Traces
End-to-end request journeys across services. OpsPilot maps trace topology, identifies latency hotspots, and detects services missing from your instrumentation coverage.
Spans
Individual units of work within a trace. OpsPilot analyzes span duration, status codes, and attribute completeness to pinpoint exactly where time is being spent.
Events
Point-in-time occurrences attached to spans. OpsPilot tracks exception events, message events, and custom annotations to reconstruct root cause timelines.
Profiles
Continuous profiling data where available. OpsPilot correlates CPU, memory, and goroutine profiles with trace anomalies to surface deep performance inefficiencies.
Performance Optimization
From metrics + spans + traces- P99 latency regressions in critical paths
- Slow database queries identified from span attributes
- N+1 query patterns detected across trace topology
- Connection pool saturation and thread contention
- Cache hit rate degradation over rolling windows
- API timeout patterns and downstream dependency lag
Cost Optimization
From metrics + resource attributes- Over-provisioned Kubernetes pods and nodes
- Unused Lambda functions with provisioned concurrency
- Idle container replicas during low-traffic periods
- Log verbosity waste (DEBUG in production)
- Redundant trace sampling at excessive rates
- Unused or stale metric time series
Error Rate Analysis
From logs + span status + events- Error rate spikes correlated across services
- New exception types not seen in baseline
- Retry storm patterns degrading downstream services
- 5xx cascades tracing back to root cause span
- Silent failures with no span error attribute set
- Error budget burn rate against SLO thresholds
Observability Gap Detection
From trace topology + log coverage- Services present in traces but emitting no logs
- Spans missing essential attributes (db.statement, etc.)
- Critical flows with incomplete trace propagation
- Services with no health or readiness metrics
- Missing SLI metrics for key user journeys
- Alert coverage gaps on high-error-rate endpoints
Alerting Effectiveness
From metrics + historical patterns- Noisy alerts with low signal-to-noise ratio
- Flapping alerts that never resolve cleanly
- Missing alerts on services with elevated error rates
- Static thresholds that don't adapt to traffic patterns
- Duplicate alert coverage on the same symptom
- Alerts with no runbook or remediation guidance
Security Posture
From logs + span attributes + traces- Anomalous authentication failure spike patterns
- Unusual service-to-service call patterns in traces
- Sensitive data leaking through log attributes
- Services calling deprecated or unpatched endpoints
- Unexpected egress in service topology
- Audit log coverage gaps on sensitive operations