I once thought that an API was just a set of endpoints. Until I encountered a project where we first did everything with REST, then added GraphQL for flexibility, and six months later realized that half of the requests would have been better sent via gRPC. The rewrite took months. That's when I understood: choosing a protocol is not hype, but an engineering decision. And if you want to avoid the pain of refactoring, it's worth understanding API design consciously.
The API Design (REST, GraphQL, gRPC) course on asibiont.com is not just an overview of three technologies. It's a practical guide to decision-making: when REST is justified, where GraphQL gives an advantage, and in which scenarios gRPC saves milliseconds and nerves. I took this course to systematize my knowledge, and now I'm sharing my impressions and useful insights.
Why API design is more than syntax
Many developers know how to write GET /users, but far from all think about versioning, pagination, HATEOAS, or error handling. Yet these details determine how convenient the API will be for clients and maintainable for the team. According to a SmartBear study (State of API Report 2023), more than 70% of organizations face problems with API integration due to insufficient documentation and poorly thought-out design. The course helps fill these gaps.
Here are the key topics covered in the course:
- REST vs GraphQL vs gRPC: comparison by criteria — latency, query flexibility, streaming support, caching complexity.
- OpenAPI and specifications: how to describe an API so that clients generate code automatically.
- Versioning: URI, header, and media type strategies.
- HATEOAS: why hypermedia controls are needed and how to implement them.
- Pagination: offset, cursor, keyset — pros and cons.
- Security: OAuth 2.0, API keys, JWT, rate limiting.
- Error handling: unified format, status codes, problem details (RFC 7807).
What I learned from the course
Before the training, I thought GraphQL was always better than REST. But the course covered cases where GraphQL loses: for example, when working with files or in systems with strict caching requirements. On the other hand, gRPC proved itself in microservice architecture: the binary Protobuf protocol and HTTP/2 provide speed gains and enable bidirectional streaming. Now I can make a reasoned choice of protocol for the task.
I'd like to highlight the practical assignments. We designed an API for an online store: started with REST, then moved part to GraphQL for flexible client queries, and moved internal services to gRPC. This gave an understanding of how to combine approaches in a single product.
How learning works on asibiont.com
The asibiont.com platform uses AI to generate personalized lessons. You don't watch videos or read static texts — the neural network creates a program tailored to your level and goals. If you're a beginner, it will start with the basics of HTTP and API types. If you have experience, you'll go straight to advanced topics: HATEOAS, gRPC streaming, GraphQL schema optimization.
All lessons are text-based and available 24/7. You can ask the AI assistant questions (it doesn't respond in chat, but generates additional explanations and examples upon request). Learning proceeds at a comfortable pace: the neural network adapts to your answers and offers more practice where you make mistakes.
Who will benefit from the course
- Backend developers who want to design APIs consciously, not by template.
- Fullstack engineers who need to understand how the frontend interacts with different protocols.
- Technical leaders making decisions about the stack for new services.
- Product teams where it's important to avoid costly refactoring.
A real-world example: how choosing a protocol saved time
In one project, we were building a mobile delivery app. Initially, the API was REST. When we added real-time courier tracking, REST couldn't cope: constant polling requests created load. We moved this functionality to gRPC streaming, and latency dropped from 2–3 seconds to milliseconds. If we had known about this approach from the start, we would have saved weeks of development. The course teaches exactly how to choose the right tool for the task.
Why AI learning is effective
Traditional courses often suffer from an excess of theory or, conversely, superficiality. The AI on asibiont.com generates lessons based on your gaps. For example, if you don't understand the difference between idempotency and safety of HTTP methods, the neural network will offer additional examples and exercises. It's like a personal mentor who is always available.
Moreover, the text format allows you to quickly return to the necessary sections, copy code examples, and immediately apply them in your project. No hours of video — just the essence and practice.
Conclusion: an investment in a skill that pays off
API design is a skill that affects development speed, product stability, and customer satisfaction. The API Design (REST, GraphQL, gRPC) course provides a systematic approach: you'll learn to choose a protocol for the task, not based on hype, and avoid months of refactoring. If you want to improve in this area, I recommend starting your learning on asibiont.com. Personalized lessons, 24/7 access, and a practical focus make the process fast and effective.
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