REST vs GraphQL: A Practical Comparison That Skips the Hype

Two main problems: over-fetching and under-fetching. Over-fetching means a REST endpoint returns more data than the client needs — a /users endpoint returns 30 fields when your UI only displays 5. Under-fetching means you need multiple requests to get all the data for one screen — first fetch the user, then the user's posts, then each post's comments. GraphQL solves both by letting clients specify exactly what fields they want in a single query. This is especially valuable for mobile clients on slow connections and for complex UIs that aggregate data from multiple related resources.

Higher upfront complexity. You need a GraphQL server, a schema, resolvers for every field, and typically a client-side library. The N+1 query problem is a major performance pitfall — if you have a resolver that fetches a user's posts, and each post resolver fetches the post's author, you can end up with hundreds of database queries for a single request. Solving this requires DataLoader or equivalent batching tools. Caching is harder because every request is a POST to the same endpoint, which breaks standard HTTP caching. Security requires careful attention — without query depth limiting, a malicious client can craft deeply nested queries that exhaust server resources.

REST is better for simple CRUD APIs with straightforward data shapes. Public APIs where simplicity and documentation matter to external consumers. APIs consumed by non-browser clients (IoT devices, scripts, CLI tools) that don't benefit from GraphQL's flexibility. Teams without GraphQL experience where the learning curve isn't justified by the complexity. Microservices communicating with each other, where each service typically has a focused, well-defined interface that doesn't need query flexibility. GraphQL pays off most when you have multiple client types (mobile, web, desktop) with different data needs, or when your API serves complex, deeply related data.