Performance engineering for startups
- Builds caching, indexing and load testing in from day one — not bolted on after your first outage.
- Finds the bottleneck — slow query, unindexed table, N+1, connection limit — while it's cheap to fix, not live during a launch.
- Proves the system holds under realistic load before your users are the ones who discover it doesn't.
- Keeps performance work inside the architecture, so speed scales with you instead of degrading.
Most performance problems aren't visible in testing — they appear at the exact moment growth starts working, when a query that's fine on a thousand rows becomes fatal on a million. Performance engineering is the discipline of designing for that ahead of time, so the system stays fast and stable as load climbs.
Caching and indexing, designed in
Caching serves repeated work from fast storage instead of recomputing it or hitting the database every time; indexing lets the database find rows without scanning whole tables. Together they're often the difference between millisecond responses and timeouts under load — and they're far cheaper to design early than to retrofit during an incident.
Efficient data access
We eliminate the patterns that quietly kill performance at scale — N+1 queries, unbounded result sets, chatty calls, missing pagination — so your data layer stays fast as the dataset and concurrency grow.
Load testing against real numbers
We load-test against realistic traffic to find where the system breaks before it breaks in production. The output isn't a vanity benchmark; it's a prioritized list of the bottlenecks that would have taken you down, with fixes.
How we work
We profile your real workload, load-test to the failure point, and fix the bottlenecks in priority order — usually indexing, caching, and query/data-access changes before adding hardware. As an infrastructure-first product engineering studio, this sits alongside microservices design and right-sized infrastructure, so fast and cost-efficient are the same plan, not competing ones.
FAQ
What is performance engineering?
Designing and tuning a system so it stays fast and stable under real load — through caching, indexing and query optimization, efficient data access, and load testing that proves the system holds before users find the limits. Unlike one-off fixes after an incident, it's built in from the start as part of the architecture.
When should a startup do load testing?
Before the traffic that would expose the problem arrives — as a standing part of the build, not a panic after the first outage. Testing against realistic numbers tells you where the system breaks while it's cheap to fix, rather than discovering it live during a launch or spike.
How do caching and indexing improve performance?
Caching serves repeated requests from fast storage instead of recomputing or hitting the database every time, cutting latency and load. Indexing lets the database find rows without scanning whole tables. Together they're often the difference between a millisecond response and a timeout under load — and cheapest to design in early.
Why is my app slow under load but fine in testing?
Because test traffic never exercised the bottleneck: an unindexed query fine on 1,000 rows and fatal on 1,000,000, an N+1 pattern, a missing cache, or a connection-pool limit that only saturates at concurrency. Realistic load testing surfaces these before users do — and the fix is usually indexing, caching or a query change, not more servers.
Want to find your bottleneck before your users do?
Book a build audit — we'll load-test against realistic numbers and show you where the system breaks first, with fixes.
Book a Build Audit