Estimate CDN edge server capacity needed to serve peak content delivery traffic.
CDN effective capacity multiplies the number of edge nodes by each node's request-handling capacity, then applies the cache hit ratio — the fraction of requests a node can answer directly from cached content rather than forwarding to the origin. Note the cache hit ratio scales down the 'effectively served from edge' figure in this model to highlight origin offload; cache misses still get served to the end user, just at the cost of an origin round-trip, so origin capacity and origin-to-edge link bandwidth must also be sized to handle the miss traffic shown here.
Total effective CDN capacity
total_rps = edge_nodes × per_node_rps × cache_hit_ratio
A high cache hit ratio means most requests are answered directly from edge storage — fast and cheap — while cache misses require forwarding to the origin server, which is slower, consumes origin capacity, and uses backhaul bandwidth. A CDN with a low hit ratio effectively behaves more like a pass-through proxy than a true content cache, and origin infrastructure must be sized accordingly.
Highly dynamic or personalized content (which can't be cached the same way for every user), overly short cache TTLs, a very long tail of rarely-requested unique objects, or cache configuration mistakes (e.g. caching keyed on unnecessary query parameters that fragment the cache) are common causes worth investigating if hit ratio underperforms expectations.
Improving cache hit ratio (longer appropriate TTLs, better cache-key normalization, pre-warming caches for predictable traffic spikes) increases effective capacity from the same edge footprint, often more cost-effectively than simply adding more edge nodes.