Build APIs That Hold Up Under Pressure
Rate limiting and scalability are not optional features — they are what separates a proof-of-concept from a production system. This track covers the technical foundations of keeping APIs fast, fair, and available under real-world load: from token buckets and sliding windows to distributed caching, message queues, and resilience patterns across microservices architectures.
What You Will Learn
You will start with the core concepts of rate limiting — why it exists, what it protects, and how algorithms like sliding window and token bucket actually work. From there you will apply those ideas in code with a practical rate limiter implementation, then broaden into API scalability: caching strategies, asynchronous processing with message queues, and the architectural patterns that let services scale horizontally. Advanced courses cover rate limiting policies at the gateway level, serverless and microservices deployment models, and observability — metrics, tracing, and monitoring — so you can diagnose bottlenecks in running systems. The track closes with advanced resilience patterns including circuit breakers, bulkheads, and backpressure.
The Learning Path
Twelve courses span A1 through C2. The opening course, Introduction to API Rate Limiting Fundamentals, requires no prior knowledge. A1 and A2 courses establish the vocabulary and algorithms. B1 and B2 move into Architectural Patterns for Scalable APIs and a full Practical Rate Limiter Implementation. The C1 tier introduces Microservices & Serverless for Scalability and Observability and Monitoring for Scalable APIs. The track finishes at C2 with Rate Limiting in Microservices & Gateways and Advanced Scalability & Resilience Patterns — the most demanding material in the curriculum.
How It Works
Each course is split into short, hands-on lessons you complete in the built-in code editor with real-time feedback. An AI tutor is available whenever you get stuck, so you can work through tricky concepts — like distributed rate limit synchronization or queue back-pressure — without losing momentum.
