Returning a list of items from an API sounds simple until the list has ten thousand items. At that point, the response is too large to transfer efficiently, too slow to serialize, and too expensive to compute. Pagination is how APIs break large result sets into manageable chunks that clients can fetch incrementally. Choosing the wrong pagination pattern — or implementing the right one incorrectly — creates problems that do not appear until production load reveals them.

API performance problems fall into two categories: the ones that appear immediately during development and are fixed before they matter, and the ones that are invisible during development and catastrophic at production scale. The second category is where the interesting work is. Understanding where APIs slow down under load — and the specific techniques that address each cause — is the difference between an API that performs at scale and one that requires emergency remediation after launch.

The standard REST pattern is one resource per request. Create one user, update one order, fetch one product. This works until an integrator needs to create ten thousand users, update five hundred orders, or sync an entire product catalog. Making ten thousand individual API calls is slow, expensive in terms of rate limit quota, and brittle — a network failure on request 7,432 leaves the integrator with a partially completed operation and no clean recovery path.

Caching is one of the highest-leverage performance improvements available to an API, and one of the least consistently implemented. A response that is cached at the right layer — in a CDN, a reverse proxy, or the client itself — eliminates a server round trip entirely. At scale, that elimination compounds: fewer database queries, lower infrastructure cost, faster responses for every consumer. The HTTP specification provides a complete, standardized caching system. Most APIs use it only partially, leaving significant headroom unrealized.