GraphQL vs REST API: A Practical Decision and Implementation Guide

By the end of this guide, you will have a clear rule set for whether a new API surface should be REST, GraphQL, or a GraphQL aggregation layer over REST, plus the guardrails that keep the choice from breaking in production. You need only a list of current client screens or partners, rough traffic numbers, and a map of how your data entities connect.

1. Audit client requests and network patterns. Count how many round trips each screen or partner needs and whether the returned payloads waste bytes. A mobile home screen that calls /user, /orders, and /products in sequence is under-fetching round trips; a dashboard that receives forty fields when it needs three is over-fetching. These numbers, not the technology's marketing, decide where GraphQL pays for itself.

1. Route cacheable reads to REST. Any high-traffic, public, read-heavy resource where the URL can be the cache key—GET /products/123, GET /orders/1001—should stay REST. HTTP caching, ETag, and CDN edge rules work because GET URLs are transparent to caches; a GraphQL POST /graphql endpoint is opaque by default.

1. Put GraphQL on top as the aggregation layer. Use it where one client needs nested, interconnected data in a single request, such as a user plus their last three orders plus each order's line items. The GraphQL server can call REST services, databases, or other backends. The detail that decides success: expose only the fields clients actually use and reject arbitrary query strings.

1. Replace raw GraphQL queries with persisted queries for any public or CDN-fronted API. Store query text server-side and send GET /graphql?id=a1b2c3. This restores URL-level caching and blocks attackers from running arbitrary nested queries. Zalando, for example, disables raw GraphQL in production and accepts only hash-identified persisted queries.

1. Enforce query depth, complexity limits, and DataLoader. Without them, a client can request user -> friends -> friends -> posts -> comments and hammer the database. Depth and complexity limits reject expensive queries at the edge; DataLoader batches resolver lookups inside one request and removes the N+1 problem.

1. Handle status codes and errors per protocol. REST should return 404 for missing resources and 400/401/403/500 where the semantics expect them. GraphQL historically returns 200 OK even with an errors array, though the newer GraphQL-over-HTTP spec allows 4xx for request errors. Whichever you use, tests must assert both status and body shape.

1. Test differently. For REST, assert verbs, paths, and status codes first; then check field presence. For GraphQL, assert both that data contains the expected fields and that body.errors is absent, because a 200 response can hide resolver failures.

1. Version REST with URL versions and evolve GraphQL with deprecations. Use /v1/orders and /v2/orders for breaking REST changes. In GraphQL, deprecate fields, track production usage through the persisted-query registry, and remove fields only after they are unused.

Common mistakes:

• Using GraphQL for a simple CRUD admin and giving up free HTTP caching.
• Treating a 200 OK from GraphQL as proof the query succeeded without checking body.errors.
• Exposing raw GraphQL to the public internet without depth limits or persisted queries.
• Trying to cache GraphQL POST requests and wondering why the CDN returns the same payload for every operation.
• Adding a new REST endpoint every time a front-end screen changes instead of using GraphQL for the client slice.
• Migrating all endpoints to GraphQL because a vendor blog called it the future.