Technologies
The tools and platforms we use to test, observe, and optimize systems — organized by how they fit into a performance engineering workflow.
Performance tools
Load and performance testing platforms for capacity validation, regression checks, and realistic traffic simulation.
k6
Modern load testing for APIs and microservices — scriptable, CI-friendly, and built for engineering teams.
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Apache JMeter
Enterprise-grade load testing for complex protocols, legacy systems, and large-scale test plans.
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LoadRunner
Enterprise performance testing for complex application stacks — from protocol scripts to full end-user emulation.
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Monitoring and observability
Metrics, dashboards, and alerting that turn test results and production signals into actionable decisions.
Infrastructure components
Core platform building blocks — orchestration, messaging, caching, and streaming — tuned for throughput and reliability.
Kubernetes
Performance engineering for container orchestration — resource limits, autoscaling, and cluster-level bottlenecks.
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RabbitMQ
Messaging performance — queue depth, consumer lag, prefetch tuning, and back-pressure under burst traffic.
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Redis
In-memory caching and data structures — hot key protection, eviction policy tuning, and stampede mitigation.
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Apache Kafka
Event streaming at scale — partition strategy, consumer lag, throughput tuning, and back-pressure under peak load.
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How we use them
Tools in service of outcomes
We pick tooling based on your stack and goals — then wire it into baselines, tests, and monitoring you can keep.
Fit-for-purpose selection
k6 vs JMeter vs LoadRunner, Prometheus vs vendor APM — chosen for your protocols, team skills, and CI constraints.
Integrated workflows
Load tests, dashboards, and alerts connected so improvements are visible end-to-end.
Knowledge transfer
Runbooks, scripts, and dashboards your team owns after the engagement.