GraphQL vs. REST: Choosing the Right Approach for Your API

Key Takeaways

Architectural Philosophies: REST is resource-centric with multiple endpoints, while GraphQL is a query language for APIs with a single endpoint, allowing clients to dictate data needs.
Data Fetching Efficiency: GraphQL excels at solving over-fetching and under-fetching by enabling clients to request precise data in a single query, reducing network calls. REST often requires multiple requests for related data.
Schema & Type System: GraphQL has a built-in, strongly typed schema that acts as a contract, enhancing discoverability and validation. REST relies on external standards like OpenAPI for similar functions.
Client Flexibility vs. Server Control: GraphQL offers clients more control over data shapes; REST gives servers more control via predefined endpoints and responses.
Use Case Alignment: REST suits simple resource exposure and leverages HTTP caching well. GraphQL is ideal for complex data, mobile apps, rapid frontend iteration, and microservice aggregation.
Hybrid Approaches are Viable: Many projects benefit from combining GraphQL for aggregation with REST for backend services.

Introduction

In the dynamic landscape of API development, two architectural styles consistently emerge in discussions about building robust and scalable web services: REST (Representational State Transfer) and GraphQL. Both have proven effective, but they operate on fundamentally different principles for data fetching and API design. The choice between them can significantly influence your project's performance, flexibility, developer experience, and overall system architecture. This article, from the perspective of API7.ai, aims to demystify the distinctions between REST and GraphQL, explore their respective strengths and weaknesses, and provide a practical framework for making an informed decision tailored to your specific project needs, team expertise, and operational infrastructure.

What are REST and GraphQL APIs?

The world of API development is largely shaped by the architectural styles used to design and implement them. Among the most prevalent are REST and GraphQL. Understanding their core differences is the first step toward choosing the right tool for your project.

REST is an architectural style that adheres to a set of constraints for building distributed systems, most commonly web services. It's built around the concept of resources, which are essentially any identifiable pieces of information or functionality. Clients interact with servers by requesting or manipulating these resources using standard HTTP methods (like GET, POST, PUT, DELETE) directed at specific endpoints (URIs). Each REST endpoint typically represents a distinct resource or a collection of resources, and responses are usually delivered in formats like JSON or XML. As highlighted by resources like graphapi.com, REST is a long-standing and popular architectural style for building web services or APIs due to its simplicity and alignment with HTTP protocols.

GraphQL, on the other hand, is a query language for your API, coupled with a server-side runtime for executing those queries using a type system that you define for your data. Developed by Facebook (now Meta) and open-sourced in 2015, GraphQL offers a different paradigm. Instead of multiple endpoints for different resources, a GraphQL API typically exposes a single endpoint where clients send structured queries that precisely outline the required data fields and their relationships. This client-driven approach allows for fetching exactly the data needed, and nothing more, often in a single request. GraphQL offers greater flexibility, particularly for complex data-fetching operations and scenarios where clients have diverse data requirements.

The fundamental difference lies in their approach: REST is endpoint-centric, defining distinct URLs for distinct resources and operations, while GraphQL is query-centric, using a schema and a single endpoint to fulfill flexible client requests.

Why the GraphQL vs. REST Debate Matters: Key Differences Explained

The ongoing discussion surrounding GraphQL vs. REST is not merely about technical preferences; it's about understanding the fundamental philosophical differences that profoundly impact API design, client development workflows, and overall system architecture. Each approach offers distinct advantages and disadvantages, making an informed choice critical for project success, scalability, and maintainability.

%% GraphQL vs REST: one request, two paths
sequenceDiagram
    actor Client
    participant REST as REST Endpoint
    participant GraphQL as GraphQL Gateway
    participant DB as Data Store

    rect rgb(220,240,255)
        note left of REST: REST
        Client->>REST: GET /users/42
        REST->>DB: fetch user
        REST-->>Client: {id, name, email, ...}
        Client->>REST: GET /users/42/posts
        REST->>DB: fetch posts
        REST-->>Client: [{id, title}, ...]
    end

    rect rgb(255,240,220)
        note left of GraphQL: GraphQL
        Client->>GraphQL: POST /graphql { user(id:42) { name posts { title } } }
        GraphQL->>DB: JOIN query
        GraphQL-->>Client: { "user": { "name": "Ada", "posts": [...] } }
    end

Key Differences Driving the Decision:

Data Fetching Paradigm:
- REST: Relies on a resource-oriented model with multiple endpoints. To retrieve related data (e.g., fetching an order and then its items), a client might need to make several sequential HTTP requests (often dubbed the "N+1 problem"). For instance, to get a user and their posts, you might first query /users/{userId} and then /users/{userId}/posts. This can lead to inefficient data retrieval if not carefully managed.
- GraphQL: Allows clients to request precisely the data they need in a single, structured query. A client can specify nested data requirements, enabling it to fetch an order, its items, and the associated product details all in one go. This significantly reduces the number of network round trips, minimizing latency and improving performance, especially for complex data graphs.
Endpoint Structure:
- REST: Characterized by a multitude of endpoints, each typically representing a specific resource or a collection of resources (e.g., /users, /users/{id}, /posts, /posts/{id}/comments). This structure makes resources easily discoverable through their URLs.
- GraphQL: Typically exposes a single endpoint (e.g., /graphql) that handles all types of requests: queries (for reading data), mutations (for writing data), and subscriptions (for real-time data updates). The nature of the operation and the specific data are defined within the query payload itself.
Over-fetching and Under-fetching:
- REST: Is inherently prone to both issues.
  - Over-fetching occurs when an endpoint returns more data than the client requires (e.g., fetching a user's full profile when only the name is needed).
  - Under-fetching occurs when an endpoint doesn't return enough data, forcing clients to make additional requests, as seen in the N+1 problem.
- GraphQL: Solves these problems by design. Clients specify exactly the fields they need, eliminating over-fetching. Its ability to fetch nested related data in a single query mitigates under-fetching and reduces the number of client-server interactions significantly.
Schema and Type System:
- REST: While standards like OpenAPI (formerly Swagger) can define schemas and provide documentation, these are often external specifications rather than an intrinsic part of the API's operational contract. Enforcement relies on conforming to the spec.
- GraphQL: Is built around a strongly typed schema. This schema acts as a definitive contract between the client and server, defining what queries are possible, the types of data returned, and the structure of that data. This schema is central to GraphQL, providing robust validation and enabling powerful developer tooling.
Client Control vs. Server Control:
- REST: The server dictates the structure and amount of data returned by each endpoint. Clients consume what the server provides.
- GraphQL: The client has significantly more control, specifying precisely the data required and its shape. This flexibility empowers client developers but requires careful server-side implementation to manage query complexity and performance.
API Evolution and Versioning:
- REST: API evolution is often managed through versioning (e.g., /v1/users, /v2/users). Modifying or deprecating fields/endpoints requires careful planning to avoid breaking existing clients.
- GraphQL: Can evolve more gracefully. New fields can be added to the schema without breaking existing clients that don't request them. Fields can also be marked as deprecated, providing a clear signal for clients to migrate away, facilitating smoother versionless evolution.

Evaluating Your Needs: When to Choose GraphQL or REST

The decision between adopting GraphQL or REST for your API project is highly context-dependent. Both architectural styles have proven effective in building robust and scalable web services, but they offer distinct advantages and disadvantages that make one approach more suitable than the other depending on specific project requirements, team expertise, and infrastructure considerations. Understanding when each approach excels is key to making an informed architectural choice that aligns with your project's unique needs and long-term objectives.

When REST Might Be a Better Fit:

Simple Resource Exposure and CRUD Operations: For APIs that primarily expose straightforward Create, Read, Update, and Delete (CRUD) operations on well-defined, distinct resources with limited and simple relationships between them, REST's resource-centric model and extensive tooling support can be highly effective and easier to implement initially. For example, an API for managing simple user profiles or product catalogs that do not involve complex relational fetching might be perfectly served by REST. Its inherent simplicity is often an advantage when the domain model is straightforward and the interactions are largely independent of each other.
Leveraging Existing Infrastructure and Expertise: If your organization already possesses a mature RESTful API ecosystem, has invested heavily in REST-specific tooling (e.g., monitoring solutions, security scanning tools, client libraries), established best practices, and if your development team has deep and broad expertise in RESTful principles, continuing with REST for new, complementary services might be more pragmatic and cost-effective. This approach avoids the overhead of introducing a new paradigm, potentially requiring extensive retraining or specialized hiring, and maximizes the return on existing investments.
Leveraging HTTP Caching Effectively: For APIs that are predominantly read-heavy and where responses are static or change infrequently, REST's natural alignment with HTTP's well-established caching mechanisms can offer significant performance advantages. Browsers, intermediate proxies, and Content Delivery Networks (CDNs) can effectively cache RESTful GET responses based on standard HTTP caching headers (e.g., Cache-Control, ETag, Last-Modified), reducing server load and improving client-side latency without requiring custom implementation beyond standard HTTP practices.
Public APIs with Broadest Consumer Reach: REST's familiarity and ubiquitous adoption mean that a vast number of developers across the industry are already comfortable consuming REST APIs. For public-facing APIs intended for broad adoption by external developers who may not be deeply specialized in specific API technologies, REST's widespread understanding can be a significant advantage, lowering the barrier to entry and accelerating integration by a wider audience due to its established conventions.
Strict Adherence to HTTP Semantics: If your design philosophy mandates strict adherence to HTTP semantics—such as fully leveraging the spectrum of HTTP status codes (e.g., 200 OK, 201 Created, 400 Bad Request, 404 Not Found) for distinct operational outcomes, or ensuring specific HTTP verbs accurately represent the intended action on a resource—REST aligns naturally with these principles, making the API's behavior more predictable based on standard web conventions.

When GraphQL Might Be a Better Fit:

Complex Data Relationships and Nested Data Requirements: When your data model involves deeply nested structures or interconnected entities (e.g., fetching a customer, their historical orders, and the details of items within those orders), GraphQL excels. It allows clients to efficiently fetch all required data in a single, precisely defined query, thereby circumventing the "N+1 problem" commonly encountered in RESTful architectures and dramatically reducing network latency. For example, a social media feed might require fetching user profiles, their posts, and likes for each post, all in one GraphQL query, showcasing its power in aggregating related data efficiently.
Mobile Applications and Frontend-Heavy Workloads: For mobile applications or Single Page Applications (SPAs) that need to fetch specific data efficiently to render complex user interfaces with minimal network requests, GraphQL's ability to request exactly the data needed is a significant advantage. It empowers frontend teams to optimize data loading, improve app performance, and enhance the overall user experience without constant backend intervention for new data requirements or changes to response structures.
Rapid Frontend Iteration and Flexibility: GraphQL's schema-first approach and flexible querying capabilities empower frontend teams to iterate rapidly on UI features and data requirements. They can fetch new data fields or explore different data combinations as needed without necessitating changes to backend API endpoints or response structures. This agility accelerates frontend development cycles significantly and reduces dependencies on backend team timelines, fostering a more agile development process.
Microservices Aggregation and API Gateway Layer: GraphQL can serve as an extremely effective API gateway or aggregation layer, particularly in microservices architectures. Using patterns like Apollo Federation, a single GraphQL endpoint can orchestrate queries across multiple underlying microservices, consolidating their data and presenting a unified, consistent API to clients. This simplifies client interactions with a distributed backend significantly, abstracting away the complexity of aggregating data from disparate services and reducing the number of direct client-microservice interactions.
Emphasis on Strongly Typed Schemas and Discoverability: If building an API with a strong, server-defined type system is a priority for ensuring data integrity, providing self-documenting capabilities, and enabling powerful developer tooling (such as auto-completion, code generation, and robust validation), GraphQL's schema-first approach is highly beneficial. Schemas are central to GraphQL's structure, providing crucial context for data requirements and driving developer confidence and productivity through enhanced discoverability and early error detection.

Considering Hybrid Approaches:

It is also important to recognize that a hybrid model is often a viable and practical solution, especially in large or evolving systems. Many organizations leverage GraphQL as an API gateway or aggregation layer for their microservices, while retaining RESTful endpoints for backend microservices that expose simpler, resource-centric data or have specific deployment constraints that align better with REST. This hybrid approach allows teams to capitalize on the respective strengths of both architectural styles, optimizing for specific use cases. Adopting a hybrid model—using GraphQL for complex relationships and REST for simpler CRUD operations—can effectively balance architectural complexity and performance requirements, providing the best of both worlds. The optimal choice ultimately depends on the specific needs of your project, the nature of your data, your team's skillset, and your organization's long-term API strategy.

REST vs. GraphQL: A Deep Dive into Technical Considerations

The decision between adopting REST or GraphQL for your API project involves a thorough understanding of their technical nuances. These differences span data fetching mechanics, endpoint structures, performance implications, and strategies for API evolution and versioning. Recognizing these technical distinctions is crucial for making an informed architectural choice that aligns with your project's unique requirements and desired tradeoffs.

Data Fetching Mechanics

REST: REST operates on a resource-centric model where each distinct resource or collection of resources typically has its own dedicated endpoint (URI). To retrieve related data—for instance, an order resource and its associated order_items—a client might be required to make multiple sequential requests. A typical sequence could involve requesting an order from /orders/{orderId}, then requesting its items from /orders/{orderId}/items, and potentially making further requests for product details for each item retrieved. This pattern can easily lead to the "N+1 problem," where fetching a list of items results in N additional requests just for the details of each item. This can significantly increase network latency and place a considerable burden on server resources due to numerous individual requests.
GraphQL: GraphQL revolutionizes data fetching by allowing clients to request precisely the data they need in a single, structured query. A client can specify nested data requirements directly within the query itself. For example, a single GraphQL query can be formulated to fetch an order, its associated line items, and the related product details for each item simultaneously. This capability drastically reduces the number of network round trips required, minimizes latency, and ensures clients receive only the data they explicitly requested, thus eliminating both over-fetching (receiving more data than needed) and under-fetching (needing multiple requests to get all required data).GraphQL's flexibility in data retrieval is particularly beneficial for applications with complex data requirements and evolving needs, enabling more efficient data transfer.

Endpoint Structure and Operations

REST: Employs a resource-oriented architecture characterized by distinct endpoints for each resource and its associated operations. Examples include:
- GET /users for listing all users.
- GET /users/{userId} for retrieving a specific user.
- POST /users for creating a new user.
- PUT /users/{userId} for updating a specific user.
- DELETE /users/{userId} for removing a user. This structure is generally intuitive for client developers familiar with basic HTTP protocols and standard web resource patterns, making it relatively easy to discover and use.
GraphQL: Typically exposes a single endpoint (commonly /graphql) that handles all operations. Clients send queries (for reading data), mutations (for writing data), and subscriptions (for real-time data updates) to this single endpoint. The type of operation and the specific data being requested are defined within the GraphQL query language itself, rather than through separate URLs or distinct HTTP verbs. This consolidation can simplify API endpoint management and client configuration, presenting a unified interface to the API.

Over-fetching and Under-fetching

REST: Is inherently prone to both over-fetching and under-fetching.
- Over-fetching: An endpoint might return a comprehensive set of fields for a resource, even if the client application requires only a subset of that data (e.g., an API might return a user's full profile including address, phone number, and employment history when the client application only needs the user's name and email for display). This leads to wasted bandwidth and increased processing on both the client and server sides.
- Under-fetching: Conversely, an endpoint might not return enough related data, requiring the client to make additional requests to fetch necessary information, contributing to the previously mentioned "N+1 problem" and increasing latency.
GraphQL: Is specifically designed to solve these problems. Clients explicitly define the exact fields they need in their queries. The server is then obligated to respond with only that requested data, effectively eliminating over-fetching by giving clients the power to shape the response precisely. By allowing nested queries, GraphQL also preempts under-fetching, enabling clients to retrieve related data efficiently in a single round trip, significantly improving performance, especially in mobile or high-latency environments.

Schema and Type System

REST: While standards like OpenAPI (formerly Swagger) are widely used to define REST API schemas and provide rich documentation, these schemas are often considered supplemental to the API's operational contract. The fundamental contract is primarily defined by the endpoints, HTTP methods, and the documented structure of request/response payloads, which can sometimes be less rigidly enforced without external validation tools or custom server-side checks.
GraphQL: Features a strongly typed schema that is fundamental to its operation and serves as the core contract between the client and server. This schema defines all possible queries, mutations, subscriptions, and the precise types of data that can be returned. This built-in, server-defined schema acts as both comprehensive documentation and a rigorous validation mechanism, providing a robust contract that drives client development, enables powerful developer tooling (such as auto-completion and validation), and ensures data integrity. Schemas are central to GraphQL's structure, providing crucial context for data requirements and fostering developer confidence and productivity through enhanced discoverability and early error detection.

Client Control and API Evolution

REST: The server traditionally dictates the structure and amount of data returned by each endpoint. Changes to an endpoint's response (e.g., adding a new field, modifying an existing one) can potentially break existing clients if not managed through strict versioning strategies (e.g., using URI versioning like /v1/users versus /v2/users). Deprecating or removing fields/endpoints requires careful planning, extensive communication, and often a dual-versioning support period to avoid disrupting clients.
GraphQL: Empowers the client with greater control over the data they fetch, leading to more flexible API evolution. The server's schema can be updated more gracefully. New fields can be added to types without impacting existing clients that don't explicitly request them. Furthermore, fields can be marked as deprecated within the schema, providing a clear signal to clients that they should plan to migrate away from using those fields, facilitating a smoother evolution path compared to traditional REST versioning strategies which can be more disruptive.

Caching

REST: Benefits significantly from HTTP's well-established and widely supported caching mechanisms. Responses to GET requests can be effectively cached by browsers, intermediate proxies, and Content Delivery Networks (CDNs) based on standard HTTP caching headers (e.g., Cache-Control, ETag, Last-Modified). This can provide substantial performance improvements for read-heavy APIs without requiring custom implementation beyond standard HTTP practices.
GraphQL: Caching is generally more complex. Since most operations are typically routed through a single endpoint via POST requests, standard HTTP caching mechanisms are less effective. Caching strategies are usually implemented at the client library level (e.g., using normalized caches in Apollo Client or Relay) or through specialized server-side solutions that can parse GraphQL queries to identify cacheable data. This requires a different approach to caching compared to the straightforward implementation often seen with REST, and might require more sophisticated client-side or gateway-level solutions to achieve similar benefits.

A thorough understanding of these technical distinctions is crucial for making an informed decision, as they directly impact development workflows, performance characteristics, the ability to evolve the API over time, and the overall maintainability of your API project.

Evaluating Your Needs: When to Choose GraphQL or REST

When REST Might Be a Better Fit

Simple Resource Exposure and CRUD Operations: For APIs that primarily expose straightforward Create, Read, Update, and Delete (CRUD) operations on well-defined, distinct resources with limited and simple relationships between them, REST's resource-centric model and extensive tooling support can be highly effective and easier to implement initially. For example, an API for managing simple user profiles or product catalogs that do not involve complex relational fetching might be perfectly served by REST. Its inherent simplicity is often an advantage when the domain model is straightforward and the interactions are largely independent of each other.
Leveraging Existing Infrastructure and Expertise: If your organization already possesses a mature RESTful API ecosystem, has invested heavily in REST-specific tooling (e.g., monitoring solutions, security scanning tools, client libraries), established best practices, and if your development team has deep and broad expertise in RESTful principles, continuing with REST for new, complementary services might be more pragmatic and cost-effective. This approach avoids the overhead of introducing a new paradigm, potentially requiring extensive retraining or specialized hiring, and maximizes the return on existing investments.
Leveraging HTTP Caching Effectively: For APIs that are predominantly read-heavy and where responses are static or change infrequently, REST's natural alignment with HTTP's well-established caching mechanisms can offer significant performance advantages. Browsers, intermediate proxies, and Content Delivery Networks (CDNs) can effectively cache RESTful GET responses based on standard HTTP caching headers (e.g., Cache-Control, ETag, Last-Modified), reducing server load and improving client-side latency without requiring custom implementation beyond standard HTTP practices.
Public APIs with Broadest Consumer Reach: REST's familiarity and ubiquitous adoption mean that a vast number of developers across the industry are already comfortable consuming REST APIs. For public-facing APIs intended for broad adoption by external developers who may not be deeply specialized in specific API technologies, REST's widespread understanding can be a significant advantage, lowering the barrier to entry and accelerating integration by a wider audience due to its established conventions.
Strict Adherence to HTTP Semantics: If your design philosophy mandates strict adherence to HTTP semantics—such as fully leveraging the spectrum of HTTP status code for distinct operational outcomes, or ensuring specific HTTP verbs accurately represent the intended action on a resource—REST aligns naturally with these principles, making the API's behavior more predictable based on standard web conventions.

When GraphQL Might Be a Better Fit

Complex Data Relationships and Nested Data Requirements: When your data model involves deeply nested structures or interconnected entities (e.g., fetching a customer, their historical orders, and the details of items within those orders), GraphQL excels. It allows clients to efficiently fetch all required data in a single, precisely defined query, thereby circumventing the "N+1 problem" commonly encountered in RESTful architectures and dramatically reducing network latency. For example, a social media feed might require fetching user profiles, their posts, and likes for each post, all in one GraphQL query, showcasing its power in aggregating related data efficiently.
Mobile Applications and Frontend-Heavy Workloads: For mobile applications or Single Page Applications (SPAs) that need to fetch specific data efficiently to render complex user interfaces with minimal network requests, GraphQL's ability to request exactly the data needed is a significant advantage. It empowers frontend teams to optimize data loading, improve app performance, and enhance the overall user experience without constant backend intervention for new data requirements or changes to response structures.
Rapid Frontend Iteration and Flexibility: GraphQL's schema-first approach and flexible querying capabilities empower frontend teams to iterate rapidly on UI features and data requirements. They can fetch new data fields or explore different data combinations as needed without necessitating changes to backend API endpoints or response structures. This agility accelerates frontend development cycles significantly and reduces dependencies on backend team timelines, fostering a more agile development process.
Microservices Aggregation and API Gateway Layer: GraphQL can serve as an extremely effective API gateway or aggregation layer, particularly in microservices architectures. Using patterns like Apollo Federation, a single GraphQL endpoint can orchestrate queries across multiple underlying microservices, consolidating their data and presenting a unified, consistent API to clients. This simplifies client interactions with a distributed backend significantly, abstracting away the complexity of aggregating data from disparate services and reducing the number of direct client-microservice interactions.
Emphasis on Strongly Typed Schemas and Discoverability: If building an API with a strong, server-defined type system is a priority for ensuring data integrity, providing self-documenting capabilities, and enabling powerful developer tooling (such as auto-completion, code generation, and robust validation), GraphQL's schema-first approach is highly beneficial. Schemas are central to GraphQL's structure, providing crucial context for data requirements and driving developer confidence and productivity through enhanced discoverability and early error detection.

Considering Hybrid Approaches

Conclusion: Choosing Wisely for API Success

The decision between GraphQL and REST is a pivotal one in API development, with each approach offering distinct advantages and technical trade-offs that significantly impact project outcomes. REST, with its resource-centric model, widespread adoption, and strong alignment with HTTP semantics and caching mechanisms, remains an excellent choice for simpler APIs, APIs exposing well-defined resources, and scenarios where broad consumer familiarity and existing infrastructure are key considerations. Its straightforward structure and clear use of HTTP methods make it highly discoverable and accessible to a wide range of developers.

Conversely, GraphQL excels in scenarios demanding flexibility, efficiency in data fetching, and rapid iteration, particularly for applications dealing with complex data models, mobile clients with diverse data needs, and microservices architectures requiring aggregation. Its ability to eliminate over-fetching and under-fetching by allowing clients to specify exact data requirements, its powerful, built-in strongly typed schema, and its more adaptable evolution path offer significant benefits for modern, data-intensive applications. GraphQL's schema-centric design is a powerful advantage that enhances productivity and data integrity.

Ultimately, understanding your project's specific needs—whether it's the simplicity of resource management, the complexity of data relationships, the need for frontend flexibility, or the strategic advantage of leveraging existing infrastructure—is crucial for making the right choice. Hybrid approaches, which combine GraphQL for aggregation and REST for specific backend services, are also a practical and increasingly common strategy, allowing flexibility and optimization across different parts of a system. By carefully evaluating these technical considerations and aligning them with your project goals, you can select the architectural style that best positions your API for success, ensuring optimal performance, long-term maintainability, and a positive developer experience.

Next Steps

Stay tuned for our upcoming column on the API 101, where you'll find the latest updates and insights!

Eager to deepen your knowledge about API gateways? Follow our Linkedin for valuable insights delivered straight to your inbox!

If you have any questions or need further assistance, feel free to contact API7 Experts.