Load testing, profiling, and architecture tuning to handle 10× growth without downtime — or runaway cloud bills.
Traffic is doubling every quarter, your dashboards are red at peak, and your cloud bill is out of control. We specialize in taking platforms from "barely surviving" to "effortlessly serving 10× the load" through profiling, targeted optimization, and smart infrastructure changes. No rip-and-replace — just measured wins.
Every engagement starts with a benchmark and ends with measurable, reproducible improvements. You keep the load test harness, the dashboards, and the playbook so your team can do this themselves next time.
Realistic k6 and JMeter scenarios hitting staging with production-like traffic shapes.
CPU, memory, and flame graphs to find the real bottlenecks — not the imagined ones.
Multi-tier caching with CDN, Redis, and application layers tuned for your workload.
Query optimization, indexing, partitioning, and read replicas where they matter.
Static asset optimization, edge caching, and latency reduction for global audiences.
Kubernetes HPA, cluster autoscaler, and serverless scaling rules that save money.
Establish a reproducible load test that captures today's peak traffic — plus the target for 10×.
Profiling under load, traces, slow query logs, and a prioritized list of things to fix first.
Hot path rewrites, N+1 kills, async work, and caching layers — measured against the benchmark.
Instance sizing, network topology, connection pools, and auto-scaling rules calibrated to load.
Re-run the benchmark at target load, iterate until green, produce a written report.
Dashboards, alerts, and a documented playbook so your team can spot regressions early.
Load test harness, baseline.
Bottleneck identification.
Code & infra improvements.
Full load test + handover.
We commit to measurable targets upfront — like "handle 10k RPS at <200ms p95" — and back it with a reproducible load test you can run yourself.
Usually down. Most of our scaling work finds 30–50% cost savings by eliminating waste before adding capacity.
Yes. We profile with sampling tools that add negligible overhead, and stage changes behind feature flags.