REST and GraphQL represent two dominant approaches to API design, each with distinct philosophies, strengths, and use cases. Understanding their differences helps you choose the right approach for your specific requirements, team capabilities, and client needs.

REST: Resource-Oriented Architecture

REST (Representational State Transfer) organizes APIs around resources accessed via standard HTTP methods. Each resource has a URL, and operations map to HTTP verbs: GET retrieves, POST creates, PUT/PATCH updates, and DELETE removes resources. This aligns naturally with CRUD operations and HTTP’s design.

The simplicity of REST is its greatest strength. Developers familiar with HTTP immediately understand REST APIs. Standard HTTP features like caching, status codes, and content negotiation work seamlessly. REST APIs are stateless, with each request containing all necessary information, simplifying server design and scalability.

REST APIs typically return fixed data structures. Request /users/123 and you get a complete user object with all fields. This predictability simplifies implementation but can lead to over-fetching (receiving unused data) or under-fetching (requiring multiple requests for complete data).

Versioning in REST APIs typically uses URL paths (/v1/users) or headers, making breaking changes explicit. Old API versions can coexist with new ones, allowing gradual client migration.

GraphQL: Query-Driven Architecture

GraphQL, developed by Facebook, provides a query language allowing clients to request exactly the data they need. Instead of multiple endpoints returning fixed structures, GraphQL exposes a single endpoint where clients specify their data requirements in queries.

A GraphQL query might request a user’s name and email, their recent posts (with titles only), and comment counts—all in one request. The server returns precisely this data, nothing more, nothing less. This solves over-fetching and under-fetching while reducing the number of network requests.

The Schema defines available types and their relationships, serving as a contract between client and server. Strongly typed schemas enable powerful developer tools: autocomplete, validation, and automatic documentation. The schema acts as both documentation and validation.

Introspection allows clients to query the schema itself, discovering available types, fields, and operations programmatically. This powers tools like GraphiQL, providing interactive API exploration and documentation.

Performance Characteristics

REST APIs are simple and cacheable. Standard HTTP caching works transparently: CDNs, proxies, and browsers cache GET responses based on HTTP headers. This dramatically improves performance for cacheable resources without custom implementation.

GraphQL’s flexibility complicates caching. Since requests are POST queries to a single endpoint, standard HTTP caching doesn’t apply. Client-side caching requires sophisticated libraries like Apollo Client, which normalize responses and cache at the field level. Server-side caching must understand query semantics to cache effectively.

For mobile clients with limited bandwidth, GraphQL’s precision reduces payload sizes significantly. Fetching exactly the needed data minimizes bytes transferred, improving performance on slow networks. REST often returns excess data that mobile clients discard.

Development Experience

REST’s simplicity means less learning curve. HTTP knowledge transfers directly to REST API design. However, as applications grow, maintaining numerous endpoints for different client needs becomes tedious. Frontend developers often request new endpoints for specific views, leading to endpoint proliferation.

GraphQL centralizes this complexity in the query language. Frontend developers write queries for their specific needs without backend changes, increasing autonomy and velocity. However, this requires learning GraphQL syntax and concepts, increasing initial onboarding time.

Tooling for GraphQL is exceptional. Strongly typed schemas enable code generation, creating type-safe client code automatically. REST API tooling exists (OpenAPI/Swagger) but isn’t as tightly integrated into the development workflow.

Backend Complexity

REST backends are straightforward: implement endpoints that serialize database queries or service calls to JSON. Most frameworks provide excellent REST support out of the box.

GraphQL backends require more infrastructure: schema definition, resolver functions for each field, and dataloader patterns to avoid N+1 queries. However, this structure can lead to cleaner, more maintainable code by centralizing data-fetching logic.

N+1 query problems plague naive GraphQL implementations. Fetching users and their posts might execute one query for users and N queries for posts (one per user). Dataloaders batch and cache these queries, but this requires careful implementation.

Use Cases

Choose REST when building public APIs consumed by diverse clients, when extensive HTTP caching is valuable, when simplicity is prioritized over flexibility, or when the team lacks GraphQL expertise. REST’s maturity and tooling make it a safe default choice.

Choose GraphQL when clients need flexible data querying, when you’re building for specific client applications (especially mobile), when reducing network requests matters, or when the development team can invest in GraphQL tooling and best practices.

Real-Time Updates favor GraphQL’s subscription feature, allowing clients to subscribe to data changes and receive real-time updates. REST requires separate mechanisms like WebSockets or Server-Sent Events.

Hybrid Approaches

Many systems use both: REST for public APIs and simple CRUD operations, GraphQL for sophisticated client applications requiring flexible querying. This leverages each approach’s strengths while mitigating weaknesses.

GraphQL over REST wraps existing REST APIs with a GraphQL layer, providing GraphQL benefits without rewriting backends. Tools like Apollo’s RESTDataSource simplify this pattern.

Security Considerations

REST’s simplicity makes rate limiting and authentication straightforward: check Authorization headers, apply rate limits per endpoint and method. GraphQL’s single endpoint and variable query complexity make rate limiting more challenging. Queries can be arbitrarily expensive, requiring query complexity analysis to prevent abuse.

Both approaches support standard authentication (JWT, OAuth), but GraphQL requires additional consideration around query depth limits and complexity scoring to prevent denial-of-service attacks through expensive queries.

Making the Choice

Evaluate your specific requirements: client flexibility needs, team expertise, caching requirements, and performance characteristics. For many applications, REST’s simplicity and maturity make it the right choice. For applications with sophisticated client requirements and the team capability to leverage it, GraphQL provides powerful advantages.

Consider starting with REST and migrating specific use cases to GraphQL as needs arise. This incremental approach provides pragmatic benefits without requiring all-in adoption. The goal isn’t choosing the “better” technology but rather the more appropriate one for your context, requirements, and team.