Building robust and efficient APIs is a critical aspect of software development, and choosing the right API technology is paramount to delivering fast, reliable, and secure API services.
When choosing an API technology, you’ll want to consider factors such as the complexity of your project, the number of endpoints you’ll need, the expected traffic, and the client requirements. It’s also important to consider the skills and experience of your development team, as some technologies may require a steeper learning curve than others.
In this article, we’ll compare tRPC and GraphQL and highlight their pros and cons to help you decide which API technology best suits your project.
Jump ahead:
tRPC is an open source remote procedure call (RPC) framework that is fast, lightweight, and enables efficient communication between client and server applications. It simplifies building high-performance and scalable APIs by automating many everyday tasks required for API development over HTTP/2.
tRPC uses Protocol Buffers as its default serialization format for encoding and decoding the data that is transferred between the client and server across multiple languages and technology stacks.
tRPC automates data serialization and deserialization between client and server, allowing you to define API methods and request/response data in a simple, readable syntax. This way, when clients make requests, you don’t have to worry about the low-level details. tRPC also provides automatic code generation for clients and servers.
tRPC is language agnostic, and you can build clients and servers that support Protocol Buffers, including Go, Java, Python, and Node.js.
Several features of tRPC make the API technology attractive for building scalable and performant APIs. Here are some of the features:
tRPC is an excellent option for building robust and efficient web apps. The primary reasons why you’ll want to use tRPC as the API technology for your app include its ease of use and its performance and scalability.
You can get up and running with tRPC in minutes because of its simple and intuitive API, and its efficient binary protocol for communication boosts its performance and scalability. This makes tRPC ideal for applications that need to handle high-traffic requests.
While there are many benefits of using tRPC, you must be aware of some cons of using tRPC.
tRPC has a complex configuration process that can be challenging for beginners. While experienced developers may find the configuration process easy, it may discourage some developers from getting started using tRPC.
Another significant limitation of tRPC is its limited language support. tRPC doesn’t support other popular languages like Java, Ruby, and Python, which is a challenge to widespread adoption. tRPC also has limited framework support. It works well with a few web frameworks like Next.js and Express.js, but it may differ too much to be useful with other frameworks like Django, Spring, or Laravel.
Integrating tRPC into existing projects that use unsupported frameworks can also add extra development time and complexity, which reduces its performance benefits. Despite these limitations, tRPC is still an excellent option for specific projects, such as large-scale web applications that require complex APIs.
tRPC’s performance and scalability make it an ideal choice for high-traffic projects that require efficient client-server communication. TypeScript projects can also easily be built with tRPC because it itself it built using TypeScript, so it integrates seamlessly into TypeScript projects.
Finally, tRPC is an excellent option for projects requiring many API calls. The framework’s performance and efficiency make it an ideal choice for projects that rely heavily on APIs.
GraphQL is a query language and runtime for APIs that revolutionizes how we build APIs on HTTP. GraphQL provides a more efficient, powerful, and flexible way of building APIs than traditional REST APIs.
GraphQL enables clients to precisely specify and receive only the data they need, reducing the amount of data transfer over the network and improving performance. Unlike REST APIs, where complex applications have many endpoints for data consumption, GraphQL APIs have one entry point for everything.
GraphQL solves the issues of over-fetching and under-fetching, which is an issue with traditional APIs. By fetching only requested data, clients can reduce resources, leading to better performance for their use case.
GraphQL APIs have a schema written in its own schema definition language (SDL) that consists of types and fields. You can fetch all the data in a GraphQL API by describing what you want with a single query. Types define the shape of the data and its fields, and the fields represent the data you can query.
When clients query a GraphQL API, the server validates the query against the schema. If the query is valid, the server processes the query and returns the requested data as a JSON object. The response will match the structure of the query, making it easy for clients to understand and operate on the data.
GraphQL owes its growing adoption to its wide array of features. Here are some notable features of GraphQL:
GraphQL stands out from other API technologies for three reasons:
GraphQL’s efficient data transfer method reduces what gets transferred over a network. In traditional RESTful APIs, the client has to make multiple requests to complex APIs to fetch all the data it needs.
Additionally, GraphQL is flexible; with the right query, you can fetch the exact data you need without over or under-fetching data. GraphQL‘s ’robust typing system also ensures the client and server agree on the data structure, reducing errors and increasing code reliability.
GraphQL’s excellent developer experience also makes it easy to learn and use. The GraphQL SDL is easy to understand, and many resources are available online to help you get started. It provides functionality for automatic API documentation, so that consumers can easily understand your API without consulting external documentation.
Automatic documentation generation eases the process of developing and maintaining APIs.
GraphQL sacrifices a few features for the benefits it provides. One of these is caching: since GraphQL allows clients to request exact data, it isn’t easy to cache responses relative to traditionally REST APIs. You can cache data at the endpoint level with REST; for GraphQL, you’ll have to cache at the field level, which may be challenging to implement.
Another drawback is GraphQL’s complexity. GraphQL increases the complexity of your application’s backend because it is more flexible than traditional REST APIs. On top of this, exact data fetching makes query optimization difficult, which can be taxing while developing complex applications.
GraphQL is an excellent choice for developing mobile and web applications that require flexibility and efficiency. GraphQL APIs are consistent and predictable, so they’re easy to maintain over time.
GraphQL is handy for building microservices, where several services interact and interoperate. With GraphQL, you can compose single APIs from multiple APIs into a unified interface that’s easy to update.
GraphQL is a great option for ecommerce, IoT, and real-time apps because it’s easy to scale and can interact with the large amounts of data from different sources these apps tend to process.
As we’ve discussed, tRPC and GraphQL are both modern API technologies that excel at the operations they’re designed for.
tRPC is mainly used for building performant microservices, while GraphQL is used for building flexible APIs for efficient data transfer between web and mobile applications.
Here’s a table that compares the API technologies:
| Category | tRPC | GraphQL |
|---|---|---|
| Protocol type | Remote procedure call (RPC) | Query language |
| Server implementation | Node.js, Go, etc. | Node.js, Java, Python, Ruby, etc. |
| Client implementation | JavaScript, TypeScript, Dart, Swift, Kotlin, etc. | JavaScript, TypeScript, Dart, Swift, Kotlin, etc. |
| Transport protocols | HTTP/2, gRPC, WebSocket | HTTP/1.1, HTTP/2, WebSocket |
| Request/response format | Protocol Buffers, JSON, MessagePack | JSON |
| Type system | Strongly typed, supports both scalar and custom types | Strongly typed, supports both scalar and custom types |
| Schema language | TypeScript | GraphQL SDL (Schema Definition Language) |
| Querying | Single request/response per method call | Batched queries/mutations, allowing multiple queries/mutations. Executable in a single request/response |
| Subscriptions | Built-in support for real-time data updates | Built-in support for real-time data updates |
| Performance | Faster than GraphQL, due to the use of binary serialization and multiplexing | Slower than tRPC, due to the use of text-based serialization and batching |
| Ecosystem | Still growing, but has good community support | Has a large and mature ecosystem, with many tools and libraries available |
| Learning curve | Steeper than GraphQL, due to the use of TypeScript and fewer available learning resources | Less steep than tRPC, due to its popularity and abundance of available learning resources |
Overall, tRPC and GraphQL are both powerful API technologies, but tRPC is faster and better suited for low-latency, high-throughput applications, while GraphQL is more flexible and better suited for building APIs that need to support a wide range of clients and use cases.
Your choice between tRPC and GraphQL ultimately depends on the specific needs of your project and the trade-offs you’re willing to take.
Hurray! You’ve learned about tRPC and GraphQL, their features, their pros and cons, and the projects for which they’re best suited.
Both tRPC and GraphQL have their strengths and weaknesses. Ultimately, the choice between tRPC and GraphQL depends on your project’s specific requirements and your development team’s skills and experience.
If you have any questions, please contact me via social media. I’ll be glad to assist and answer any questions. Til I write to you again, happy coding.
While GraphQL has some features for debugging requests and responses, making sure GraphQL reliably serves resources to your production app is where things get tougher. If you’re interested in ensuring network requests to the backend or third party services are successful, try LogRocket.
LogRocket lets you replay user sessions, eliminating guesswork around why bugs happen by showing exactly what users experienced. It captures console logs, errors, network requests, and pixel-perfect DOM recordings — compatible with all frameworks.
LogRocket's Galileo AI watches sessions for you, instantly aggregating and reporting on problematic GraphQL requests to quickly understand the root cause. In addition, you can track Apollo client state and inspect GraphQL queries' key-value pairs.
Hey there, want to help make our blog better?
Join LogRocket’s Content Advisory Board. You’ll help inform the type of content we create and get access to exclusive meetups, social accreditation, and swag.
Sign up now
Discover what’s new in The Replay, LogRocket’s newsletter for dev and engineering leaders, in the October 15th issue.
Jemima Abu examines where AI falls short on accessibility and how we can best harness AI while still building products that everyone can use.
Cloud AI made scaling easy, but local AI brings control, cost stability, and data privacy. Explore the hardware realities, tradeoffs, and strategies shaping this shift.
Writing REST APIs by hand is a thing of the past. Frameworks like tRPC, Fastify, and Hono eliminate boilerplate with schema-driven design, improving speed and safety.
Learn more →
