End-to-end performance engineering across applications, databases, and infrastructure — grounded in Site Reliability Engineering principles, not guesswork.
Performance problems are rarely where teams assume they are.
We instrument applications, databases, and infrastructure end-to-end using APM tooling and distributed tracing, then apply queueing-theory and capacity-planning fundamentals to find the actual bottleneck — not just the symptom users report. Every engagement establishes clear Service Level Indicators (SLIs) and Objectives (SLOs) so "fast enough" becomes a measured, agreed target instead of a subjective debate.
We optimize for the metric that matters to the business — latency, throughput, or cost per transaction — and validate every change against a controlled before/after benchmark.
Google's SRE discipline — SLIs, SLOs, and error budgets — applied to balance velocity and stability.
Structured capacity and availability management practices from the ITIL service lifecycle.
Mathematical foundation for capacity planning: the relationship between concurrency, throughput, and latency.
A continuous loop, not a one-time project — performance regresses without ongoing monitoring.
Performance work is never "done" — SRE treats it as a continuous loop, with error-budget monitoring feeding the next baseline.
Representative results from a recent application performance engagement.
| Metric | Before | After | Result |
|---|---|---|---|
| P95 API response time | 1,240 ms | 320 ms | −74% |
| Database query time (avg) | 480 ms | 95 ms | −80% |
| Peak throughput | 1,200 req/s | 3,600 req/s | 3x |
| Infrastructure cost per 1M requests | $42 | $27 | −36% |
| SLA compliance (99.9% target) | 91.2% | 99.94% | Target met |
A measured, benchmark-driven approach — not trial and error.
Deploy distributed tracing and APM across the full request path.
Agree on the latency, throughput, or cost target that matters to the business.
Apply targeted fixes to the actual bottleneck — code, query, or infrastructure.
Track error budgets continuously so regressions are caught before users notice.