APIs are the foundation of today’s applications. Every single day we are using software that is using APIs to retrieve data. In the year 2025, developers have a simple question, REST or GraphQL. These two styles are significantly different. In this blog, we will compare graphql vs rest in order for you to find out which is a proper API style.
Is GraphQL really better than REST? What are the differences in terms of speed, scalability, and ease of use? We will consider each side in common terms. By the end, you will understand the differences between REST and GraphQL and when you would want to choose one over the other.
Understanding GraphQL Vs REST
What is a REST API?
REST (Representational State Transfer) is the classic way to build web APIs. It has been around for years and is very popular. A REST API treats data as resources. Each resource (like a user or an article) has its own URL endpoint. You use standard HTTP methods (GET, POST, PUT, DELETE) to read or change data.
Think of a REST API like a restaurant menu. Each menu item (endpoint) serves a fixed dish (data). For example, one endpoint might give you user profiles, another posts, and a third comments. You get a preset structure of data from each endpoint. This makes REST easy to understand and simple to start with.
REST APIs are stateless, meaning each request stands alone. This helps with scaling. Since each request is independent, you can spread them across many servers. REST also has great support for caching (using HTTP headers). Proper caching can speed up a REST API a lot. These features help REST handle heavy loads and large apps well.
What is GraphQL?
GraphQL is a newer API, created by Facebook in 2012. It is a query language rather than an architecture. In GraphQL, there is usually a single endpoint (often /graphql). Clients send a query asking exactly for the data they need, and the server replies with precisely that data.
Picture ordering food from a custom menu. Instead of fixed meal combos, you just ask for the exact ingredients you want. With GraphQL, you request specific fields from possibly many resources at once. For example, you could ask for a user’s name, email, and the title of their latest post in one shot. GraphQL will return only those fields and nothing extra.
This declarative data fetching is GraphQL’s strength. It fixes two problems common in REST: over-fetching and under-fetching. With REST, you might get more data than you need (over-fetching), or have to call multiple endpoints to get all the pieces (under-fetching). GraphQL solves both by letting you ask for exactly what you need in one query.
Data Fetching and Performance
There is one clear distinction between the two paradigms when it comes to their data fetching abilities. In GraphQL, we can send one query and get all of the data we mentioned. In REST, we typically send many requests to different URLs. Thus, the efficiency advantage of GraphQL is larger for complex data needs. In fact, one of the benefits of GraphQL is the reduction in round-trip requests for the client:
Imagine a mobile app that requires permission to access user information and the user’s list of posts. In a REST query, you will explicitly do two calls (GET/user and GET/posts), while you may perform one GraphQL query and get the data in one call (I’ll show an API with a list of resources in class). The faster the requests, the better.
Obviously, the fewer calls you have to make, the quicker responses you will receive on a slow network. As one source notes, “the ability of GraphQL to retrieve all the data required in a single request will reduce the round trips.” This often translates to better performance, especially when the data is distributed across resources:
However, REST can also be very fast; one study showed that REST had a quicker response time on average: ~922ms vs GraphQL ~1865ms. Why? Because REST can heavy use HTTP caching. The standard REST endpoint can be cached either at the browser or CDN level. This makes repeated REST queries can completely skip the server calls entirely.
For GQL, conversely, there is one endpoint and each query can be different; thus caching becomes inherently harder by default. In practice, GraphQL most often wins out when your data needs vary considerably.
Data Volume and Overhead
A key point is data size. REST endpoints return a fixed data shape. Sometimes that includes more fields than the client needs. GraphQL returns precise and optimized data by design. For example, if you need only a name and email, GraphQL won’t send the whole profile picture or address. This can save bandwidth and speed up responses.
On the server side, GraphQL might work a bit harder. All the queries and schema validations add CPU work. In contrast, a simple REST call can often be served from a cached response or static file. So in one analysis, REST’s average response time was much lower (thanks to caching), even though its servers used more resources.
Is GraphQL Faster Than REST?
Is GraphQL faster than REST? The answer is: It depends on your use case. In scenarios where the client needs complex, nested data, GraphQL often feels faster because you make fewer network requests. But in very simple or heavily cached cases, REST might edge out GraphQL in raw response time.
The bottom line: GraphQL can speed things up by avoiding wasted data transfer, while REST can speed things up via caching and simplicity. Both can be fast if tuned, but GraphQL shines with flexible queries, and REST shines with predictable, cache-friendly calls.
Scalability and Architecture
REST APIs are inherently scalable. Since they are stateless, each request doesn’t rely on past requests. You can throw more servers at a REST API to handle more users. Also, REST is very mature: nearly every language and platform has built-in support or frameworks for REST. This makes it easy to find tools, documentation, and developers familiar with REST.
REST also promotes a clear separation of concerns. Each endpoint deals with a specific resource. This can make designing and understanding the API straightforward. The OpenAPI Specification (Swagger) further standardizes REST designs, helping teams to define endpoints and data models for large systems.
GraphQL: One Endpoint, Many Sources
GraphQL uses a single endpoint. Internally, that endpoint can gather data from many places. This makes GraphQL great at gluing together multiple data sources or microservices. For example, your user data might be in one database and your product data in another. GraphQL lets a client fetch both in one query, acting as a unified layer. It fits well with modern microservices and serverless architectures.
Because of this, GraphQL APIs often come with a schema that ties many types together. GraphQL can aggregate data, reducing the need for custom endpoints for each combined dataset. If your project involves complex or interrelated data, GraphQL can scale by simply extending the schema, rather than adding new endpoints. It can also simplify versioning: GraphQL uses schema changes instead of a versioned URL.
However, one risk is that one GraphQL endpoint can become a bottleneck. That single endpoint might struggle if thousands of clients hit it with heavy queries at once. Caching at the HTTP level is harder, too. You have to implement custom caching (e.g., via persisted queries or client-side cache libraries). In short, GraphQL can handle scale, but it requires careful management of queries and resources.
Ease of Use and Learning Curve
For many developers, REST is the safe choice. REST is simple to learn and has been around for years. Most developers have built or used a REST API before. This makes it easy to adopt REST quickly.
Setting up a REST API often involves defining routes/URLs and handling HTTP methods – concepts most devs know. Error handling in REST uses standard HTTP status codes (404, 500, etc.), which most web frameworks support out of the box. And because REST is widely used, there are tons of tutorials and best practices.
GraphQL: Flexible but New
GraphQL is more flexible than a REST interface, but flexibility often comes with complexity. Many people who use GraphQL are met not only by a new API but also by a new schema system and query syntax. You will first need to create a schema that includes the type and fields you will use. You will then write a query using this schema type. This learning curve can be steep.
On the bright side, after you have learned GraphQL, it can benefit your development speed. Part of the time saved, if any, partially comes from strong typing (every type and field is represented and defined in the schema), which can help catch bugs earlier in the development cycle. Many tools will also allow you to auto-generate documentation or client code from the schema. However, getting to this point can take time and effort. If your project is small or simple, you might want to think twice before adopting this overhead. It has even been suggested that “it can be time-consuming to figure out and implement GraphQL queries”, even simple queries.
There is also a practical limit: file upload. You can post a file in REST to upload it. In GraphQL, you cannot upload files due to its specifications. You would need to find a workaround for file uploads. One more thing to learn.
Tooling and Ecosystem
REST has a mature ecosystem. You have API design tools like Swagger/OpenAPI, plenty of client libraries, and services for documentation (Swagger UI, Postman, etc.). Testing and mocking tools for REST are common. This makes integration easier if your team already has experience with REST.
GraphQL’s ecosystem is younger but growing strong. Key advantages include:
- Strongly Typed Schema: The schema serves as a built-in contract. It auto-documents the API. Tools like GraphiQL and Apollo Explorer let you query your API interactively.
- Self-Documentation: Since the types and fields are defined in code, the schema can be introspected. This means developers can easily see available fields and their types without separate docs.
- Code Generation: Some tools can generate client-side code or TypeScript interfaces from a GraphQL schema automatically. This tight coupling keeps client and server in sync.
- Playground/Test Tools: GraphQL comes with playground UIs (GraphiQL, Playground) where you can build and test queries on the fly.
The trade-off is that GraphQL tooling is still catching up to REST. Many teams have already invested in REST frameworks and processes (CI/CD, security scanners, etc.). Migrating or adding GraphQL requires a new setup. But if your project values cutting-edge flexibility, GraphQL tools can pay off.
When to Use Each: Use Cases
In the real world, the choice often depends on your app’s needs:
- Use GraphQL if: You need flexible, precise data fetching. For example, if clients vary widely in what data they want, or if you have multiple data sources to combine. Mobile apps with slow networks benefit from GraphQL’s single query approach. If you have nested data (e.g., posts with comments with authors) and want to fetch it all together, GraphQL is ideal. It also shines if you expect to iterate on your API a lot: adding fields to the schema won’t break old queries.
- Use REST if: Your data and operations are fairly fixed and uniform. If your app is simple or you already have a well-defined backend structure, REST is easier. Small projects often don’t need GraphQL’s flexibility. REST is also better if you need simple file uploads or want to leverage standard HTTP caching heavily. And if your team is already well-versed in REST, sticking with it may be faster.
The AWS team summarizes it well: “GraphQL is likely better if you have limited bandwidth, multiple data sources, or varied client requests. REST is likely better for smaller apps with less complex data”. You might even use both: GraphQL for data aggregation where needed, and REST for simpler parts of your system.
Conclusion
There is no one-size-fits-all. REST works great for straightforward APIs: it is simple and well-known and scales with caching and stateless design. GraphQL works great for complex APIs: it lets clients ask for exactly what they need and can unify multiple data sources behind one flexible endpoint.
Choosing between GraphQL and REST API depends on your project. Ask: Do you need a few simple endpoints, or do you need a dynamic schema that can handle many query patterns? If you want minimal data chatter and have varying client needs, lean on GraphQL. If you need reliability, caching, and your data needs are fixed, lean REST.
In the end, the difference between GraphQL and REST API is in how and what you fetch. GraphQL gives clients freedom and precision; REST gives servers structure and stability. You might even use a mix: keep REST for simple parts and add GraphQL for complex queries. The important thing is to pick the style that suits your team and use case.
API choice shapes your whole system, so consider these points carefully. Whether it’s GraphQL vs REST, remember: use the right tool for the job.
FAQs
Q-1) When should I use GraphQL over REST?
Use GraphQL when clients need varied or nested data, and you want to reduce network requests. It’s good for apps with changing data needs or limited bandwidth. For simple, uniform data needs, REST is usually fine.
Q-2) Is GraphQL faster than REST?
It depends. GraphQL can be faster when you fetch complex or related data with one query. REST can be faster when you rely on HTTP caching or make simple requests. Proper caching often makes REST very fast.
Q-3) Which is easier to learn: GraphQL or REST?
REST is generally easier to start with. Most developers already know RESTful calls and endpoints. GraphQL has a steeper learning curve because you must define a schema and learn its query language. However, once learned, GraphQL can make data fetching more efficient.