OBSERVABILITY + LOAD TESTING

360° Observability
needs more than signals

Observability shows how your systems behave in production.Load testing adds the missing piece: controlled, repeatable load that turns telemetry into insight.

Metrics, logs, and traces explain what happened. Load testing explains why it happened, and what will happen next.

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Native integrations with leading observability solutions

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Signals are necessary. Stimulus makes them usable.

Observability captures production truth, but production is noisy. When performance degrades, teams still can’t answer the questions that prevent incidents:

What exact workload triggers p99 inflation?
Which layer saturates first: edge, gateway, service, dependency?
Is this a regression or production variance?
Can we reproduce the failure mode on demand?

Without stimulus, telemetry is descriptive. With controlled load, telemetry becomes diagnostic.

Load testing alone shows symptoms like slow response times, but not where time is actually spent. Observability alone shows production behavior, but doesn’t reproduce failure conditions on demand. When you correlate both, you can trace performance across every layer under controlled load. That’s what makes results actionable.

Mateusz Piasta

Inpost

CORRELATION UNDER LOAD

What load testing unlocks inside your observability strategy

Controlled load turns observability from monitoring into diagnosis.

Illustration Modern load testing for performance-driven teams

Reproducible performance investigations

Turn incidents into experiments: replay the same workload and make telemetry comparable across runs, environments, and releases.

Measured system limits, not assumptions

Quantify where saturation begins: latency percentiles diverge, errors cliff, queueing starts, scaling stalls.

Continuous regression signals

Track performance drift across builds using controlled baselines, so regressions become measurable and attributable, not “we think it got slower.”

Bottleneck attribution across layers

Share a Pinpoint where the system breaks first. Correlate percentile inflation with infra KPIs + traces to isolate the limiting layer quickly. source of truth across Dev, QA, and Ops

Observability-driven performance engineering use cases

When teams test high-impact workflows, integrating load testing into observability lets them reproduce issues, isolate bottlenecks, and validate system limits with real telemetry.

Edge and ingress saturation analysis

Validate load balancers, API gateways, ingress controllers, WAF/CDN limits under concurrency. Detect queueing, dropped connections, TLS handshake inflation, and connection exhaustion, even when APM looks “healthy.”

Tail latency amplification (p99 / p99.9) under stress

Expose where tail latency explodes: GC pauses, thread pool starvation, lock contention, downstream timeouts, retry storms. Correlate percentile inflation with infra metrics and trace spans to isolate the limiting layer.

Regression detection across releases

Run identical workloads per build and track deltas as a continuous observability signal. Compare percentiles, errors, throughput, and resource profiles across versions to catch performance drift before production.

Dependency bottleneck isolation

Stress critical paths and correlate slowdowns with distributed traces. Identify which dependency drives latency inflation (database, cache, third-party APIs) and where time is lost under contention.

Capacity planning with scaling validation

Ramp traffic while monitoring autoscaling behavior, saturation metrics, queue depth, and throughput ceilings. Quantify headroom, safe operating range, and non-linear scaling zones, with evidence in your monitoring dashboard.

SLO verification under controlled traffic mixes

Prove that latency and error budgets hold under realistic peak patterns before release. Validate thresholds against sustained load and baseline comparisons, not production guesswork.

REPLAY