Modern applications rely heavily on efficient and scalable APIs. Two of the most widely adopted paradigms for API design today are REST and GraphQL. While both serve the purpose of data exchange between client and server, they differ significantly in architecture, flexibility, performance, and developer experience.

This article presents a practical, side-by-side comparison of REST and GraphQL — helping backend developers, API architects, and product teams make informed decisions.

What is REST?

REST (Representational State Transfer) is an architectural style based on standard HTTP methods and structured resource endpoints. It has been the de facto standard for web APIs for over a decade.

Key Characteristics:

• URL-based endpoints (e.g., /users, /posts/123)

• Stateless communication — each request contains all required context

• Standard HTTP methods: GET, POST, PUT, DELETE

• Standard status codes for error handling

Example Request:

GET /users/123

Example Response:

{
  "id": 123,
  "name": "Jaydeep",
  "email": "jaydeep@example.com"
}

What is GraphQL?

GraphQL, developed by Facebook in 2012, is a query language and runtime that allows clients to request exactly the data they need. It is particularly popular in applications with dynamic UIs, such as SPAs and mobile apps.

Key Characteristics:

• Single endpoint (typically /graphql)

• Strongly typed schemas define the API contract

• Nested data fetching in a single request

• Client-defined queries offer precise control over responses

Example Query:

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

Example Response:

{
  "data": {
    "user": {
      "name": "Jaydeep",
      "email": "jaydeep@example.com",
      "posts": [
        { "title": "REST vs. GraphQL" },
        { "title": "Backend Design Patterns" }
      ]
    }
  }
}

REST vs. GraphQL: Side-by-Side Comparison

Practical Example: Fetching User and Posts

REST Approach:

GET /users/123
GET /users/123/posts

• Requires two network requests

• May include extra, unused fields

GraphQL Approach:

query {
  user(id: "123") {
    name
    posts {
      title
    }
  }
}

• Single request

• Returns only the requested fields

Performance & Scalability Considerations

Developer Experience

REST

• ✅ Simpler to learn and implement

• ✅ Better for standardized APIs and microservices

• ❌ Frontend teams depend on backend changes for new data

GraphQL

• ✅ Empowers frontend teams with flexibility

• ✅ Reduces iteration friction between teams

• ❌ Requires schema design, resolver logic, and deeper monitoring

When Should You Use REST?

• You're building a simple CRUD application

• Your API must support HTTP caching and content negotiation

• You’re targeting third-party integrations or public APIs

• Your team is familiar with RESTful conventions and tools

When Should You Use GraphQL?

• Your frontend needs dynamic and flexible data fetching

• You want to minimize API round-trips and reduce bandwidth

• You have complex, nested relational data

• You want to empower frontend developers without changing backend code

Hybrid Approach: Best of Both Worlds

Many modern systems — such as GitHub, Shopify, and Twitter — adopt a hybrid architecture:

• Use REST internally for microservices and backend services

• Use GraphQL at the edge as a gateway layer for aggregating and reshaping data for clients

This allows backend teams to retain REST’s simplicity while providing frontend teams with GraphQL’s flexibility.

Challenges with GraphQL

Despite its benefits, GraphQL introduces unique challenges:

• N+1 Queries: Inefficient resolvers can degrade performance

• Security: Requires query depth limiting and complexity analysis

• Caching: Lacks a standardized caching layer (needs tools like Apollo)

• Monitoring: Requires observability tooling (Apollo Studio, Sentry, etc.)

TL;DR: REST vs. GraphQL

Final Thoughts

Both REST and GraphQL are powerful in their own right.

• REST remains ideal for well-structured, cache-friendly, and resource-centric APIs.

• GraphQL shines in modern applications where clients need to query dynamic and interrelated data efficiently.

Rather than treating them as mutually exclusive, consider using them together, depending on the context of your application and team workflow.