The guide
Build for the peak you can predict.
Over-engineering for traffic that never arrives wastes budget. Under-engineering for a peak you can see coming costs revenue and trust. The right architecture matches the actual pattern.
Separate predictable peaks from genuine spikes
A product launch or seasonal sale is forecastable and should be load-tested in advance. A viral moment is not, and needs an architecture that scales without a human in the loop.
Cache aggressively at every layer
CDN, application and database caching each remove load before it reaches the layer most likely to fail. Most traffic-spike outages are cache failures, not compute failures.
Load test against realistic, not average, traffic
Testing at expected volume proves nothing about peak behaviour. Load test at multiples of your best estimate, including realistic user paths, not just homepage hits.
Instrument for observability before you need it
When a spike hits, you need to see what's failing in real time. Logging, tracing and alerting set up after the fact only help the next incident.
Design the graceful degradation path
Decide in advance what the system does under extreme load — queue checkout, serve a cached page, shed non-critical requests — rather than letting it fail unpredictably.