GraphQL vs REST: When to Use Each

February 6, 2017

GraphQL emerged from Facebook in 2015 and has gained significant traction. It promises to solve problems inherent to REST APIs: over-fetching, under-fetching, and rigid endpoint structures. But GraphQL isn’t universally better than REST—each has strengths that make it suited to different contexts.

Understanding the Difference

REST: Resource-Oriented

REST organizes APIs around resources with standard HTTP methods:

GET /users/123
GET /users/123/posts
GET /posts/456/comments

Each endpoint returns a fixed structure. Clients make multiple requests to compose the data they need.

GraphQL: Query-Oriented

GraphQL provides a single endpoint where clients specify exactly what data they need:

query {
  user(id: "123") {
    name
    email
    posts(limit: 10) {
      title
      commentCount
    }
  }
}

The server returns precisely what was requested—no more, no less.

GraphQL Strengths

Eliminates Over-Fetching

REST endpoints return fixed structures. If you need just a user’s name but the endpoint returns the entire user object, you’ve over-fetched.

GraphQL clients request only fields they need:

query {
  user(id: "123") {
    name  # Only this field returned
  }
}

For bandwidth-constrained clients (mobile apps), this matters significantly.

Eliminates Under-Fetching

To display a user profile with their recent posts and follower count, REST might require:

GET /users/123
GET /users/123/posts?limit=5
GET /users/123/followers/count

Three round trips. On high-latency connections, this creates noticeable delay.

GraphQL fetches everything in one request:

query {
  user(id: "123") {
    name
    bio
    posts(limit: 5) {
      title
      createdAt
    }
    followerCount
  }
}

Schema as Contract

GraphQL APIs have explicit schemas defining available types, fields, and relationships. The schema serves as documentation and contract:

type User {
  id: ID!
  name: String!
  email: String
  posts: [Post!]!
}

type Post {
  id: ID!
  title: String!
  author: User!
}

Clients can introspect the schema to discover capabilities. Tooling validates queries against the schema before execution.

Flexible Evolution

Adding fields to GraphQL types is backward-compatible. Clients that don’t request new fields aren’t affected. This enables schema evolution without versioning.

Deprecation is explicit:

type User {
  name: String!
  fullName: String! @deprecated(reason: "Use name instead")
}

Clients see deprecation warnings; migration can happen gradually.

Strong Tooling

GraphQL’s type system enables powerful tooling:

REST Strengths

Simplicity

REST is simpler to understand and implement. HTTP semantics (GET, POST, PUT, DELETE) map naturally to CRUD operations. Most developers know REST patterns.

GraphQL requires learning a query language, understanding resolvers, and managing schema design. The learning curve is steeper.

HTTP Caching

REST leverages HTTP caching directly. GET requests for resources can be cached at CDN, proxy, and browser levels using standard HTTP headers:

GET /users/123
Cache-Control: max-age=3600
ETag: "abc123"

GraphQL queries are typically POST requests to a single endpoint, bypassing HTTP caching. GraphQL caching requires additional infrastructure (persisted queries, application-level caching).

Simpler Authorization

REST endpoints map cleanly to authorization rules:

GET /users/:id → check if user can read user
DELETE /posts/:id → check if user can delete post

GraphQL queries can request arbitrary combinations of data, making authorization more complex. You must check authorization per field or type, not per endpoint.

Monitoring and Tooling

REST APIs integrate naturally with existing infrastructure:

GraphQL’s single endpoint makes traditional monitoring approaches less useful. You need GraphQL-aware tooling to understand query patterns.

Predictable Performance

REST endpoints have predictable performance characteristics. You can profile and optimize specific endpoints.

GraphQL queries are client-controlled. A malicious or poorly-written query could request deeply nested data, causing performance problems:

query {
  users {
    posts {
      comments {
        author {
          posts {
            comments { ... }
          }
        }
      }
    }
  }
}

Protecting against this requires query complexity analysis and limits.

When to Choose GraphQL

GraphQL shines when:

Multiple clients with different data needs. Mobile, web, and third-party clients often need different views of the same data. GraphQL lets each client request exactly what it needs.

Rapidly evolving requirements. When frontend needs change frequently, GraphQL’s flexibility reduces backend changes. Frontend teams can modify queries without API changes.

Complex, interconnected data. When data relationships are complex and clients need to traverse them flexibly, GraphQL’s graph model fits naturally.

Mobile applications. Bandwidth efficiency and reduced round trips matter more on mobile. GraphQL’s precision helps.

Public APIs with diverse consumers. External developers have varied needs you can’t predict. GraphQL lets them query what they need.

When to Choose REST

REST works better when:

Simple CRUD operations. If your API is mostly create, read, update, delete on straightforward resources, REST’s simplicity is valuable.

Caching is critical. If responses are highly cacheable and CDN caching provides significant value, REST’s HTTP caching is advantageous.

Team familiarity. If your team knows REST well but not GraphQL, the learning curve cost matters.

Third-party integration. Many services and tools expect REST APIs. If integration with the existing ecosystem matters, REST is more compatible.

Strict performance requirements. If you need tight control over exactly what the server does per request, REST’s explicit endpoints provide more predictability.

Hybrid Approaches

You don’t have to choose exclusively. Many organizations use both:

The right architecture might use different approaches for different contexts.

Implementation Considerations

GraphQL Implementation

REST Implementation

Decision Framework

Ask these questions:

  1. How many different clients consume this API? More clients → GraphQL’s flexibility helps.

  2. How varied are client data requirements? More variation → GraphQL reduces over/under-fetching.

  3. How important is HTTP caching? Very important → REST’s caching advantages matter.

  4. What’s the team’s GraphQL experience? Limited → Consider REST’s lower learning curve.

  5. How complex are data relationships? Complex → GraphQL’s graph model fits.

  6. What’s the performance criticality? Very critical → REST’s predictability helps.

The answer is rarely clearly one or the other. Evaluate tradeoffs in your specific context.

Key Takeaways