Systems that stay fast under load and honest under failure

Performance work runs on evidence: distributed traces and profilers — down to flame graphs where needed — to find where time actually goes, k6 or Locust load tests that replay realistic traffic shapes, and database work on query plans, indexing, and connection pooling, which is where a large share of real-world latency problems live. Reliability work is architectural: retries with exponential backoff and jitter, circuit breakers, timeouts chosen deliberately, bulkheads between critical and best-effort paths, and failover that gets rehearsed, not just diagrammed.

Production success is quantified: p99 latency under defined load, error budgets tracked against SLOs, and recovery times demonstrated in game-day exercises rather than estimated in documents. Just as important is what the team inherits — the load test suite runs in CI so regressions are caught at merge time, and capacity planning becomes arithmetic on measured trends instead of a guess before the big launch.

Scoping starts with a baseline. Before recommending anything we measure what the system does today under representative load — latency percentiles, error rates, saturation of each tier — and agree with you on the target, whether that is an upcoming event, an SLO, or simply making a slow page acceptable. Trade-offs get discussed with numbers attached: a cache reduces database load and introduces staleness, a read replica buys capacity and adds replication lag, and aggressive retries can turn a small outage into a self-inflicted one. Some fixes are cheap and quick; others are architectural, and we say which is which before you commit.

Performance work typically goes wrong when it is done once and forgotten, or when a fix is tested in isolation and regresses under real concurrency. We design against both: load tests live in CI with thresholds that fail the build, and changes are verified under the same traffic shape that exposed the problem. Reliability work goes wrong when failover is assumed rather than exercised, so game days become a recurring calendar item rather than a one-time exercise. At handover, your team owns the load test suite, the SLO dashboards, and the capacity model, and has run at least one game day themselves.