Are there any observability tools for test environments that focus on finding bottlenecks under load?

Yes. A few observability and performance-testing tools are specifically useful for test/staging environments when your goal is to find bottlenecks under load:

Load testing + bottleneck discovery

• k6 — Modern load testing tool; great for scriptable load generation and spotting latency/throughput issues.
• Apache JMeter — Classic and widely used for load/stress testing; can reveal app/server bottlenecks.
• Gatling — High-performance load testing with detailed reports and good CI/CD integration.
• Locust — Python-based, easy to model realistic user behavior and ramp load gradually.

Observability during tests

These help you see why the bottleneck is happening:

• Grafana + Prometheus — Metrics visualization and alerting; very common in test environments.
• OpenTelemetry + backend (Jaeger/Tempo/Prometheus/etc.) — Tracing, metrics, and logs correlation to pinpoint slow services or calls.
• Datadog — APM, infrastructure metrics, logs, and traces all in one; very good for test/staging performance analysis.
• New Relic — Similar APM/observability platform for tracing bottlenecks and resource contention.
• Dynatrace — Strong automatic discovery of slow services, dependencies, and resource hotspots.

Specialized performance analysis

• Pyroscope / Grafana Phlare — Continuous profiling; useful for finding CPU/memory bottlenecks while load testing.
• Parca — Another continuous profiling option.
• eBPF-based tools like Pixie or Beyla — Can provide deep runtime visibility with low overhead.

Good setup for test environments

A common pattern is:

1. Use k6/Gatling/JMeter/Locust to generate load.
2. Collect metrics + traces + logs + profiles with OpenTelemetry + Grafana stack or a SaaS APM.
3. Use continuous profiling to identify CPU hot paths and memory pressure.
4. Compare baseline vs. loaded runs to isolate regressions.

If you want, I can recommend a best stack depending on your environment:

• Kubernetes vs. VMs
• open-source vs. SaaS
• microservices vs. monolith
• web app vs. API vs. mobile backend