REST vs GraphQL: A Practical Comparison

The REST versus GraphQL debate has generated more heat than light. Advocates on both sides overstate their case, leading teams to make decisions based on perceived industry trends rather than actual requirements.

Both approaches work. Both power successful applications at scale. The right choice depends on your specific situation, not on which technology is currently more fashionable.

What REST Actually Is

REST (Representational State Transfer) is an architectural style built around resources and HTTP methods. You have resources—users, orders, products—each with a URL. You use HTTP verbs (GET, POST, PUT, DELETE) to operate on them. The server returns representations of resources, typically as JSON.

A REST API might look like:

• GET /users/123 returns user 123
• POST /users creates a new user
• GET /users/123/orders returns orders for user 123

REST APIs are stateless—each request contains everything needed to process it. Responses can be cached based on HTTP headers. The approach leverages HTTP’s built-in features for caching, authentication, and content negotiation.

REST isn’t a specification but a set of constraints and conventions. This flexibility means “REST API” covers a wide range of implementations, from strictly RESTful designs to APIs that loosely follow REST conventions.

What GraphQL Actually Is

GraphQL is a query language for APIs. Instead of multiple endpoints returning fixed data structures, GraphQL provides a single endpoint where clients specify exactly what data they want.

A GraphQL query might look like:

query {
  user(id: "123") {
    name
    email
    orders(last: 5) {
      total
      status
    }
  }
}

The server returns exactly what was requested—no more, no less. The client controls the shape of the response.

GraphQL includes a type system that defines what data is available and how it’s structured. This schema serves as a contract between client and server, enabling tooling for validation, documentation, and code generation.

Where REST Works Well

Simple CRUD applications. For straightforward create-read-update-delete operations, REST’s resource-oriented model maps naturally to the problem domain. The conventions are widely understood, making the API predictable without documentation.

Caching is important. HTTP caching works seamlessly with REST. CDNs, browser caches, and intermediate proxies can cache GET requests based on URLs. This is built into how the web works and requires no special handling.

Public APIs. REST APIs are easier for external developers to understand and consume. The conventions are familiar, tooling support is universal, and there’s no learning curve for a query language.

File uploads and downloads. REST handles binary data naturally through HTTP’s content-type mechanisms. GraphQL can handle files, but it requires additional conventions (multipart requests, base64 encoding) that add complexity.

Microservices communication. For service-to-service calls with known data requirements, REST’s simplicity is an advantage. There’s no need for query flexibility when the calling service knows exactly what it needs.

Where GraphQL Works Well

Mobile applications with bandwidth constraints. Mobile clients can request exactly the data they need, reducing payload size and battery consumption. Different screens can request different data shapes without requiring multiple API calls or over-fetching.

Complex, interconnected data. When clients need to traverse relationships—user’s orders, order items, product details—GraphQL can fetch all related data in a single request. REST would require multiple round trips or custom endpoints.

Rapidly evolving frontends. When UI requirements change frequently, GraphQL lets frontend teams adjust their data requirements without backend changes. New fields can be requested immediately; unused fields can be dropped without coordination.

Multiple client applications. When web, mobile, and partner applications have different data needs, GraphQL lets each client request appropriate data from the same API. REST would require either over-fetching, multiple endpoints, or field selection parameters.

API aggregation. GraphQL can serve as a unified layer over multiple backend services, presenting a coherent API to clients while handling data federation internally.

The Over-Fetching and Under-Fetching Arguments

GraphQL advocates emphasize that REST APIs often return too much data (over-fetching) or require multiple requests (under-fetching). These are real problems, but they’re often overstated.

REST can address over-fetching through sparse fieldsets (?fields=name,email), different response formats, or versioned endpoints. These solutions add complexity but are well-understood patterns.

REST can address under-fetching through endpoint design. Embedding related resources (/users/123?include=orders), composite endpoints (/user-dashboard/123), or multiple concurrent requests can reduce round trips.

GraphQL has its own fetch problems. The N+1 query problem—where a single GraphQL query triggers many database queries—requires careful handling through DataLoader or similar batching solutions. This complexity is often invisible in GraphQL discussions.

The truth is both approaches can be optimized for efficient data fetching. GraphQL makes it easier for clients; REST makes it easier for servers. Which matters more depends on your situation.

Performance Considerations

GraphQL requests are harder to cache. POST requests to a single endpoint don’t benefit from HTTP caching the way GET requests to distinct URLs do. GraphQL caching requires application-level solutions (persisted queries, response caching) rather than infrastructure-level caching.

GraphQL puts more work on the server. Parsing queries, validating against the schema, and resolving fields adds overhead that simple REST endpoints don’t have. For high-volume APIs, this matters.

GraphQL can reduce round trips. Fetching complex data in one request versus multiple REST calls can improve perceived performance, especially over high-latency connections. This advantage depends heavily on access patterns.

REST APIs can be highly optimized. Known endpoints with predictable data requirements can be optimized specifically—query optimization, caching, even pre-computation. GraphQL’s flexibility makes such optimization harder.

Performance generalizations are dangerous. Either approach can be fast or slow depending on implementation. Measure your specific use case rather than relying on theoretical advantages.

Complexity Trade-offs

GraphQL adds frontend complexity. You need a GraphQL client library, query management, caching strategies, and understanding of the query language. For simple applications, this overhead may not be justified.

GraphQL moves complexity to the server. Schema design, resolver implementation, authorization at the field level, query cost analysis, and N+1 prevention all require careful attention. This complexity exists even if clients find the API easier to use.

REST complexity grows with API surface. As REST APIs evolve, managing versions, documenting endpoints, and coordinating changes between clients and servers becomes challenging. GraphQL’s schema and introspection can help with this evolution.

GraphQL requires different expertise. Teams familiar with REST may need time to learn GraphQL patterns, tooling, and best practices. This learning curve has a cost.

Security Considerations

GraphQL exposes more attack surface. Malicious clients can craft expensive queries, request deeply nested data, or exploit introspection to understand your data model. You need query depth limiting, cost analysis, and potentially disabled introspection in production.

REST security is well-understood. Rate limiting, authentication, and authorization patterns for REST are mature and widely implemented. Standard security tools understand REST APIs.

Authorization is different. REST authorization is typically endpoint-based: can this user access this endpoint? GraphQL authorization must be field-based: can this user access this field on this type? Field-level authorization is more granular but more complex to implement correctly.

Both approaches can be secured effectively, but GraphQL requires more explicit attention to query complexity and cost.

When to Choose REST

• Your API is primarily CRUD operations on resources
• Caching is important for performance or cost
• You’re building a public API for external developers
• Your team is experienced with REST and new to GraphQL
• Your clients have straightforward, predictable data needs
• You’re building microservices that communicate with each other

When to Choose GraphQL

• Multiple clients need different data from the same API
• Your data is highly interconnected and clients traverse relationships
• Frontend and backend teams want to work more independently
• Mobile performance and bandwidth efficiency are priorities
• Your API evolves frequently and versioning is painful
• You need to aggregate data from multiple backend services

The Hybrid Approach

Many organizations use both. Public APIs and internal service communication use REST; client-facing APIs for web and mobile use GraphQL. The BFF (Backend for Frontend) pattern puts a GraphQL layer in front of REST services, giving clients query flexibility while backends remain simple.

There’s no requirement to pick one approach exclusively. Use what fits each situation.

Making the Decision

1. Start with your requirements. What clients will consume this API? What are their data access patterns? What are your performance constraints?

2. Consider your team. What does your team know? What will they maintain effectively? A well-implemented REST API beats a poorly implemented GraphQL API.

3. Evaluate complexity honestly. GraphQL isn’t free. Make sure the benefits justify the additional complexity for your specific case.

4. Don’t follow hype. GraphQL is popular, but popularity doesn’t mean it’s right for your situation. Many successful applications use REST effectively.

5. Plan for evolution. Either choice will need to evolve. Consider how each approach handles API changes over time.

The Bottom Line

REST and GraphQL are both valid approaches to API design. REST is simpler, leverages HTTP better, and works well for many applications. GraphQL offers flexibility that benefits specific scenarios, particularly complex client applications with varied data needs.

The worst outcome is choosing based on what’s trendy rather than what fits your requirements. Understand the trade-offs, evaluate your specific situation, and choose the approach that makes your team most effective.