Infrastructure
Scalability Engineering
Load tests, sharding, and caching — before traffic finds the weak spot.
Scaling isn't turning up instance sizes. It's knowing your p99 under 2× traffic, where the database locks, and what breaks first. We run structured load tests, fix bottlenecks, and leave you with capacity models you can plan against.
10×
Headroom at launch
3–10 wk
Typical timeline
60+
Load tests run
<2×
P99 growth at 5× load
Stack
k6GatlingLocustPostgresRedisKubernetesDatadogGrafana
ALL SYSTEMS OPERATIONAL
Uptime SLA99.99%
Avg deploy time< 4 min
P99 latency< 50 ms
MTTR< 15 min
10× traffic headroom proven in load test before every major launch we've supported.
Get a proposal What's included
Load test design
Realistic traffic models from production metrics — not synthetic hammering that misses the real failure mode.
Bottleneck analysis
CPU, memory, I/O, and query profiling under load — we find the constraint, not guess.
Caching strategy
Redis, CDN, and application-level caches with invalidation rules that don't serve stale data in production.
Database scaling
Read replicas, connection pooling, query optimization, and sharding plans when vertical scale stops working.
Autoscaling configuration
HPA, cluster autoscaler, and queue-based scaling tuned on real load curves — not default thresholds.
Capacity planning
Growth models, cost projections, and scaling runbooks — so you know when to provision before the spike.
How we work
Week 1
Baseline & goals
Current p50/p95/p99, error rates, and target headroom defined with stakeholders.
Week 2–4
Load test & profile
Staged load tests in staging, then production-like environment. Bottlenecks documented with fixes ranked.
Week 4–7
Remediate
Fixes implemented and re-tested until headroom targets are met.
Week 7+
Launch support
War room for launch day, real-time dashboards, and post-launch capacity report.
From Evolve Edge
“Good infrastructure should be boring. The goal is to build it once, document it well, and never think about it in a crisis.”
FAQ
Can you load test production?
We prefer production-like staging. Controlled canary load in prod is possible with feature flags and off-peak windows.
We don't know our traffic patterns yet.
We model from comparable products and your funnel — then validate with canary traffic after launch.
Is this only for launches?
No. Annual capacity reviews, post-incident scaling, and pre-fundraise diligence are common triggers.
What tools do you use?
k6 for most API load tests, Gatling for complex scenarios, and your existing APM for correlation.
Related services
Ready to scope this?
Start your Scalability Engineering engagement
A senior engineer will review your project and reply within one business day with a clear next step.