Upload a JMeter JTL — get a verdict with root cause in under 60 seconds.
CPU hit 92% at T+2m14s. 847 threads blocked, heap grew 68%, GC taking 11% CPU. P99 saturated to 3,100 ms — every user affected.
CPU (P90)
92%
above 80% threshold
JVM Heap
1,840 MB
+68% growth
Threads
847
+340% growth
GC Pause
112 ms/s
~11% CPU in GC
P99 Latency
3,100 ms
3.1× SLA
Error Rate
3.1%
HTTP 500 errors
✓ payment-svc PASS
⚠ inventory-svc WARN
Three signal layers. One root cause.
Drop your JTL.
Get this.
Latencio correlates your load test results with infrastructure metrics and APM traces — so you get the exact line of code, the exact database query, the exact moment it broke.
APM · New Relic / Datadog
Root cause: slow DB query
order-svc P99 hit 3,100 ms. Distributed trace shows 2.4 s spent in a single inventory_items query — not the app, not the network.
Infrastructure · Prometheus
CPU saturation confirmed
CPU sustained at 92% from T+2m, correlating exactly with connect-time P95 breaching 500 ms. Connection pool exhausted.
Dependency chain · Phase 5
Cascade: one service broke three
inventory-svc bottleneck propagated upstream — order-svc and payment-svc SLA breaches are a cascading effect, not independent failures.
Connection pool exhausted at T+2m14s → 847 threads blocked → CPU at 92% → P99 saturated to 3,100 ms. Every user affected during peak.
order-svc
P99 3,100ms
P50 180ms
Tail 17×
Err 3.1%
↘ collapsing
inventory-svc
P99 980ms
P50 100ms
Tail 9.8×
Err 0.4%
→ stable
payment-svc
P99 210ms
P50 95ms
Tail 2.2×
Err 0.0%
→ stable
order-svc latency profile
Tail ratio 17× · CV 89% · highly variable — bimodal distribution likely
Rule findings
P99 3,100 ms — 3.1× above 1,000 ms SLA
97%
conf
Connect P95 exceeded 500 ms on 14% of requests
93%
conf
+312% response time within 30 s at T+2m14s
88%
conf
P99/P50 ratio = 9.8× (threshold: 5×)
71%
conf
Action plan
Increase connection pool size on order-svc
Add GC log correlation at T+2m14s spike
Instrument inventory-svc with APM tracing
How it works
Five phases. One verdict.
Each phase builds on the last. Phases 1–3 run on your JTL alone.
Ingest
Drop your JTL. Services detected automatically.
Profile
Percentiles, throughput, error rates computed.
Detect
SLA breaches, tail latency, degradation patterns.
Correlate
Cross-signal with CPU, memory, APM traces.
Verdict
PASS / WARN / FAIL — with a full evidence chain.
Phase 4 unlocks when you connect an infrastructure or APM tool. View integrations →
Three signal layers
One upload. Three layers of insight.
Each layer answers a different question. Together they form a complete root cause.
Load Test Results
What was slow?
- Per-request timing
- Error rates by API
- Throughput over time
- Concurrency curve
Infrastructure Metrics
Where was the stress?
- CPU, memory, GC
- Disk I/O, network
- Thread pool size
- Container throttle
APM + Logs
Why did it fail?
- Slow DB queries
- Downstream latency
- Error log spikes
- Stack traces
Why Latencio
Built for engineers tired of guessing.
2–4 hours → 60 seconds
Percentile analysis, dashboard cross-referencing, report — automated in one upload.
Evidence, not opinions
"P99 hit 3,100 ms at 14:23 — 3.1× above SLA" — not "latency seems high".
Real regressions only
Mann-Whitney U significance testing filters noise. Only real changes, never normal variance.
Root cause, not symptoms
Correlates response time with CPU, GC, and DB query data across three signal layers.
Load test tools
JMeter live. More tools on the way.
Upload any JMeter .jtl file today. k6, Gatling, Locust and Artillery are next.
JMeter
.jtl
live
k6
.json
v1.1
Gatling
.log
v1.1Locust
.csv
v1.2Artillery
.json
v1.2Observability integrations — coming soon
Prometheus
InfrastructureCPU, memory, GC per service
AWS CloudWatch
InfrastructureEC2, ECS, RDS, ALB metrics
New Relic
APMNRQL · traces · DB queries
Datadog
APMMetrics + APM traces
Grafana Loki
LogsLogQL error log correlation
Jaeger
APMDistributed trace query
Your next load test result
deserves a real verdict.
Upload a JMeter result file and get your first analysis in under 60 seconds.
Got questions or feedback?
We're actively building Latencio. Whether you have a feature request, a bug report, or just want to share how your team does load testing — we'd love to hear from you.
team.latencio@gmail.com