GraphQL vs. REST: Top 4 Advantages & Disadvantages
Picking between GraphQL and REST isn’t about which one is “better,” it’s about which fits your specific project. Both handle API requests differently, and understanding these differences helps you avoid performance bottlenecks and unnecessary complexity.
We evaluate the advantages and disadvantages of GraphQL to determine when it may be better to use GraphQL over REST, and vice versa.
GraphQL vs. REST
What are the key differences between GraphQL and REST?
GraphQL is a query language and integration protocol for APIs that allows clients to specify the exact data they need. In contrast, REST APIs rely on multiple endpoints to return fixed data structures. REST uses standard HTTP methods, such as GET, POST, PUT, and DELETE, whereas GraphQL operates through a single endpoint, enabling more flexible and efficient data fetching.
Feature | GraphQL ✅🚫🟡 | REST ✅🚫🟡 |
|---|---|---|
Data Fetching | ✅ Fetches only requested fields (efficient) | 🚫 Returns entire resource (over-fetching possible) |
Endpoint | ✅ Single endpoint (/graphql) | 🟡 Multiple endpoints (/users, /orders, etc.) |
Performance | ✅ Reduces network requests & payload size | 🟡 Can require multiple requests for related data |
Flexibility | ✅ Clients define response structure | 🚫 Server dictates response format |
Versioning | ✅ No need for versioning (schema evolves) | 🚫 Requires API versioning (v1, v2, etc.) |
Real-time Support | ✅ Built-in via subscriptions | 🚫 Requires WebSockets or polling |
Complexity | 🚫 Requires schema & resolver setup | ✅ Simpler, follows RESTful principles |
Caching | 🚫 More complex (no native HTTP caching) | ✅ Easier with HTTP methods (GET caching) |
Use Case | ✅ Best for dynamic frontends & complex queries | ✅ Best for simple, resource-based APIs |
Figure 1: Rest API query vs. GraphQL query
Source: Medium.1
Where GraphQL Wins
You Get Exactly What You Ask For
Mobile apps often struggle with REST because each screen might need data from 4-5 different endpoints. GraphQL solves this by letting you bundle everything into one request. A study by Seabra, Nazário, and Pinto found that apps using GraphQL saw 66% better performance after migrating from REST, mainly because they eliminated redundant network calls.
Faster Development Iteration
Frontend developers don’t need to wait for backend teams to create new endpoints. The schema is already defined, so they can request new field combinations immediately. One company reported cutting their query implementation time from 9 steps down to 6 when switching from REST.
Works With Existing Systems
GraphQL sits on top of your current infrastructure. You don’t rebuild your databases or microservices, you add a GraphQL layer that pulls from existing APIs and data sources.
Where REST Still Makes Sense
Better Performance Under Heavy Load
GraphQL’s single endpoint becomes a problem when you’re handling thousands of requests per second. The same research that praised GraphQL for mobile apps found it underperformed REST once traffic exceeded 3,000 requests. That single endpoint can’t distribute load the way multiple REST endpoints can.2 .
Caching Just Works
Every REST endpoint has its own URL, which means browsers, CDNs, and proxies automatically cache responses. With GraphQL, you’re sending POST requests to one URL with different query bodies, standard HTTP caching doesn’t help. You’ll need to add libraries like Apollo Client or implement custom caching logic.
Security Is Straightforward
REST has been around since 2000. Authentication patterns are well-established, and most developers know how to secure endpoints. GraphQL requires you to build rate limiting and query cost analysis from scratch. Without proper controls, someone could write a query that requests deeply nested data, overloading your server.
Predictable Costs
With REST, you know the computational cost of each endpoint. GraphQL queries can vary wildly a simple user lookup versus a query that fetches users, their posts, comments on those posts, and likes on those comments. According to research by Mavroudeas et al., calculating query costs beforehand requires machine learning approaches, which adds complexity.
Making the Choice
Use REST when:
- You’re building a public API with unknown consumers
- Caching is critical for performance
- Your team is small or inexperienced with GraphQL
- You have simple, predictable data requirements
- You need to ship quickly
Common REST scenarios:
- E-commerce product listings
- Blog content APIs
- Mobile apps with simple data needs
- Third-party integrations
- Systems where uptime and reliability matter most
Use GraphQL when:
- Your frontend needs flexibility in data fetching
- You’re building for multiple clients (web, mobile, desktop)
- You have complex, interconnected data
- Your team can invest time learning the schema system
- You want real-time features (via subscriptions)
Common GraphQL scenarios:
- Social media feeds with nested comments and likes
- Analytics dashboards pulling from multiple sources
- Mobile apps on slow connections
- Internal APIs where you control both ends
- Applications that need real-time updates
What is GrahpQL?
Figure 2: GraphQL
GraphQL is an open-source query and manipulation language for APIs developed by Facebook in 2012 and released publicly in 2015. Contrary to REST architecture, GraphQL is not an API specification; it’s a runtime for fulfilling queries with existing data. Backbend GraphQL provides a type system that describes a schema for data; in return, this gives front-end API consumers the ability to request the exact data they need.
GraphQL is schema-driven, meaning the server defines a strict schema outlining data types and relationships. Clients query or modify data (create, update, delete) based on this schema, receiving predictable JSON responses. It also supports real-time updates via subscriptions. Its flexibility and efficiency make it ideal for complex, interconnected data scenarios, such as mobile apps requiring minimal network requests.
Web developers utilize REST APIs where data entities live on several URLs on a server. Contrary to multiple URLs and entry points of REST APIs (see Figure 3), GraphQL has a single entry point (see Figure 2). Data is fetched by describing it with a syntax that mirrors its return shape in JSON.3
What is REST?
REST (Representational State Transfer) is an architectural style and integration protocol for building web services that operate over HTTP. It centers around the concept of resources, which are identified by URLs (Uniform Resource Locators), and these resources can be manipulated using standard HTTP methods such as:
- GET (retrieve data),
- POST (create data),
- PUT (update data),
- DELETE (remove data)
RESTful services are stateless, meaning that each request from the client to the server must contain all the necessary information, and the server does not maintain any session state between requests. This simplicity and adherence to the widely understood principles of HTTP have made REST a popular choice for designing APIs.
One of the core benefits of REST is its use of standard HTTP features, including status codes and headers, which allow for simpler error handling and caching. REST services are flexible, enabling data to be exchanged in various formats like JSON, XML, or plain text, depending on the client’s needs. Its clear structure and well-established conventions make it easy to implement and understand, making REST a go-to approach for many web-based applications, particularly when simplicity, scalability, and cacheability are key concerns.
Figure 3: Representation of REST API
Frequently Asked Questions: GraphQL vs. REST
FAQ
Further Reading
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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