REST and GraphQL are the two dominant API paradigms. REST uses fixed endpoints and HTTP verbs to model resources, while GraphQL provides a single endpoint with a flexible query language that lets clients request exactly the data they need. Choosing between them depends on your data model, client requirements, and team expertise.
| Feature | REST | GraphQL |
|---|---|---|
| Data fetching | Fixed response per endpoint | Client specifies exact fields |
| Endpoints | Multiple (one per resource) | Single endpoint |
| Caching | HTTP caching built-in (ETags, Cache-Control) | Requires client-side cache (Apollo, urql) |
| Over-fetching | Common — fixed response shapes | Eliminated — request only what you need |
| Versioning | URL or header versioning (v1, v2) | Schema evolution — deprecate fields |
| Tooling | OpenAPI/Swagger, Postman | GraphiQL, Apollo Studio, codegen |
| Learning curve | Low — standard HTTP conventions | Medium — schema, resolvers, query language |
How does data fetching differ?
REST returns a fixed data shape per endpoint. To get a user and their posts, you hit /users/1 and then /users/1/posts — two round trips. GraphQL lets you request both in a single query, specifying exactly which fields you need. This eliminates under-fetching (missing data) and over-fetching (too much data), which is especially impactful on mobile networks where bandwidth and latency matter.
Which is better for caching?
REST has a significant advantage in caching. Each endpoint maps to a URL that HTTP infrastructure (CDNs, browsers, proxies) can cache natively using ETags, Cache-Control, and Last-Modified headers. GraphQL sends all requests as POST to a single URL, bypassing HTTP caching entirely. You need a normalized client-side cache (Apollo Client, urql) or persisted queries with GET requests to regain caching benefits.
How do they handle API versioning?
REST APIs typically version via URL paths (/api/v2/users) or headers, which can lead to maintaining multiple versions simultaneously. GraphQL avoids versioning entirely by evolving the schema: new fields are added without breaking existing queries, and old fields are marked @deprecated with a reason. Clients naturally migrate as they update their queries.
What are the performance trade-offs?
REST endpoints are simple to optimize — each resolver is independent and you can add database indexes, caching, and rate limiting per route. GraphQL queries can be arbitrarily complex, making it harder to predict and optimize server load. Deeply nested queries can trigger the N+1 problem unless you use DataLoader or similar batching strategies. Query complexity analysis and depth limiting are essential for production GraphQL APIs.
Which has better type safety and tooling?
GraphQL has a strong type system built into its schema definition language. Tools like GraphQL Code Generator produce TypeScript types from your schema, ensuring end-to-end type safety. REST can achieve similar safety with OpenAPI specs and code generation, but the schema is external to the runtime and can drift from the implementation. GraphQL's introspection system also enables powerful developer tools like auto-complete in GraphiQL and automatic documentation.
When to use which?
Choose REST when you have simple CRUD operations, need strong HTTP caching, serve public APIs consumed by third parties, or when your team is more familiar with traditional HTTP APIs. REST is also better for file uploads and streaming responses.
Choose GraphQL when you have multiple client types (web, mobile, IoT) with different data needs, a complex data graph with many relationships, or when reducing network round trips is critical. GraphQL excels in frontend-driven development where UI teams need to iterate quickly without backend changes.
Key takeaways
- REST is resource-oriented with fixed endpoints; GraphQL is query-oriented with a flexible single endpoint
- REST has superior HTTP caching; GraphQL requires client-side cache solutions
- GraphQL eliminates over/under-fetching and reduces round trips for complex data needs
- GraphQL's type system enables strong codegen and tooling, but adds schema management overhead
- Many teams successfully use both — GraphQL as a gateway aggregating REST microservices