REST has powered web APIs for two decades and still does the job. GraphQL arrived in 2015 promising to fix what REST left rough: over-fetching, under-fetching, and the proliferation of endpoints as clients diversify. The decision you make here shapes your codebase, your team’s daily workflow, and your clients’ development experience for years.
What each approach actually does
REST (Representational State Transfer) models your API as a collection of resources. Each resource lives at a URL, and HTTP verbs describe what you want to do with it. A request to /users/42 returns a user. A request to /users/42/orders returns their orders. The shape of each response is fixed by the server.
GraphQL takes a different model. There is one endpoint, typically /graphql, and the client sends a typed query describing exactly the fields it wants. The server resolves that query against a schema and returns precisely what was asked for, nothing more.
Both are mature, well-documented, and widely supported by client libraries and hosting infrastructure. The choice is not about which one works; it is about which one fits your situation.
Where REST wins
REST is the right default for most projects.
Caching is free. HTTP caching, CDN caching, and browser caching all work out of the box. A GET request to /products/featured can be cached at the edge with no extra infrastructure. This matters most when you think carefully about where your code runs and how pages are rendered: a statically-generated or edge-rendered page paired with cacheable API responses doubles down on the same advantage. GraphQL POST queries do not cache passively; you have to build that behavior deliberately.
Tooling is universal. Every language has HTTP libraries. curl, Postman, browser DevTools, and server logs all understand REST requests without ceremony. Onboarding a new developer means no schema literacy requirement on day one.
Predictable surface area. With REST, the API is a list of endpoints with known shapes. That makes it easier to audit, document, and secure. You control what each endpoint returns, and the boundary is legible to anyone reading the route list.
Simplicity compounds. A REST API with twelve clean endpoints is easier to maintain than a sprawling type graph, and the operational burden stays low as the team grows or turns over.
REST fits public APIs, simple CRUD applications, and services consumed by third parties you do not control.
Where GraphQL wins
GraphQL earns its complexity in specific conditions.
Multiple clients with conflicting data needs. A mobile app, a desktop dashboard, and a partner integration all query the same data but need different shapes. With REST, you either over-fetch everywhere or maintain separate endpoint variants per client. GraphQL lets each client ask for exactly what it needs, which cuts payload size and eliminates the drift between what the server sends and what any given client actually uses.
Rapid UI iteration. When the frontend is evolving quickly and you want to add a field to a view without a server deployment, GraphQL makes that possible. The schema is the contract; extending it adds zero breaking changes for existing queries. Frontend engineers can write queries against a typed schema and develop against a mock server before the backend is ready.
Graph-shaped data. A product where every entity relates to several others, and views regularly need data from three or four of those relations, is where GraphQL shines. Fetching a user, their recent posts, the top comment on each post, and the commenter’s avatar in one query rather than four sequential requests is a genuine win.
GraphQL is not a performance feature. It is a coordination feature. The gains come from removing the negotiation between teams about what the API returns.
The trade-offs nobody mentions
Both options carry costs that rarely appear in comparison posts.
GraphQL shifts complexity to the schema layer. Type definitions, resolvers, and the N+1 problem (each list item triggering its own database query if resolvers are naive) require upfront investment. DataLoader or similar batching is not optional for production GraphQL; it is mandatory. Budget for it before the first sprint.
REST shifts complexity to the client layer. If your frontend needs data from three endpoints to render one view, you are making three requests, building a Backend For Frontend (BFF) endpoint that aggregates them, or living with over-fetching. None of these are free either.
Caching in GraphQL is solvable. Persisted queries, GET-based queries for public data, and normalized client-side caches (Apollo, urql) all work. But they require deliberate implementation. REST caching is passive by default; GraphQL caching is active by design.
Security surfaces differ too. REST is easier to audit because the surface is a list of endpoints. A GraphQL endpoint accepts arbitrary nested queries, and without query depth limits, cost analysis, or rate limiting per operation, it is possible to construct queries that overload your database. This is a solved problem, but it requires solving.
Which one to pick
Pick REST when your API has clear resources, caching matters, the clients are few, or the team is small. This covers a large majority of web projects. Some JAMstack architectures are an exception: a static site that needs to pull content from several sources in one build-time query sometimes benefits from a GraphQL layer, but that is the data shape driving the decision, not a preference for the technology.
Pick GraphQL when the client landscape is genuinely diverse with conflicting data needs, when the frontend team is large and moves faster than the backend, or when the product is inherently graph-shaped data. Not because it is the sophisticated choice, but because those are the conditions it was designed for.
The failure mode to avoid is reaching for GraphQL to seem modern. Complexity borrowed for a simple use case accrues interest like any other debt, and you pay it in every sprint.
How Strynal approaches API design
We start with the client’s actual data needs, not the API paradigm. For most web applications, that means a RESTful API structured around resources, designed to return what each view requires without negotiation between frontend and backend teams. When a project genuinely involves multiple clients with divergent data requirements, or a product where graph-shaped queries are the norm, GraphQL earns consideration on its merits.
Our websites and apps practice covers the full stack: front-end rendering, API design, hosting, and the decisions that sit between them. If you are starting a new build and uncertain which API shape fits your product, or inheriting a codebase where neither option is clean, that is the kind of structural conversation we have before writing the first line of code.