Each test is 5 questions with varying difficulty.
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Designing an API Gateway is a cornerstone of modern system design interviews, reflecting the shift toward microservices and edge-centric architectures in 2026. An API Gateway acts as the single entry point for all client requests, abstracting the complexity of internal service topologies. It is essential for roles ranging from Backend and Infrastructure Engineers to AI Platform Engineers, who must manage traffic to model-serving endpoints. Interviewers test your ability to balance performance, security, and maintainability at the edge. Junior candidates are expected to understand basic request routing and simple rate limiting. Senior candidates must demonstrate expertise in high-concurrency request handling, complex auth flows (like mTLS/JWT), observability integration, and handling failure modes like cascading latency or circuit breaking. Mastering this topic unlocks the ability to design robust, scalable systems that serve as the backbone for distributed applications.
In 2026, the API Gateway is the primary control plane for traffic management. With the proliferation of LLM-based services and complex microservice meshes, the gateway is no longer just a proxy; it is a critical security and performance layer. For instance, an API Gateway managing traffic to a model inference cluster must handle streaming responses (SSE/WebSockets) and enforce token-bucket rate limits per user to prevent GPU resource exhaustion. A strong interview answer reveals a candidate's grasp of the 'separation of concerns' principle—offloading cross-cutting concerns like authentication, logging, and rate limiting from individual microservices to the edge. Weak answers often fail to address the performance overhead of these operations or the reliability implications of a single point of failure. Understanding this topic is high-signal because it forces you to reason about the trade-offs between latency, consistency, and availability in a distributed environment.
The API Gateway acts as a reverse proxy that intercepts client requests, applies policy filters, and routes traffic to the appropriate backend. The execution pipeline involves a series of filters (auth, rate limit, logging) followed by a load balancer that selects the target instance.
[Client Request]
↓
[Gateway Entry]
↓
[Auth Filter] → [Rate Limit Filter]
↓
[Service Discovery]
↓
[Load Balancer]
↓
[Backend Services]The gateway calls multiple backend services in parallel and aggregates the results into a single response.
Trade-offs: Reduces client-side round trips but increases gateway complexity and latency.
Deploying a proxy alongside each service instance to handle gateway-like tasks locally.
Trade-offs: Increases resource consumption but improves isolation and simplifies service-to-service communication.
Using a dynamic plugin architecture (e.g., Lua/Wasm in Envoy) to inject custom logic into the request lifecycle.
Trade-offs: Allows high flexibility but introduces potential performance bottlenecks if not optimized.
| Reliability | Use redundant gateway instances across availability zones and implement aggressive circuit breaking. |
| Scalability | Scale horizontally using a load balancer; use stateless gateway nodes to simplify scaling. |
| Performance | Minimize filter chain depth; use connection pooling and keep-alives to reduce TCP overhead. |
| Cost | Optimize resource usage by offloading compute-heavy tasks to specialized microservices. |
| Security | Enforce mTLS, rotate API keys, and implement rate limits to prevent DDoS attacks. |
| Monitoring | Track p99 latency, request throughput, error rates, and circuit breaker status. |
A Load Balancer operates primarily at Layer 4 (TCP/UDP) or Layer 7 (HTTP) to distribute traffic across instances. An API Gateway is a specialized Layer 7 proxy that adds business-level functionality like authentication, rate limiting, and request transformation.
Direct communication leads to tight coupling, security vulnerabilities, and redundant implementation of cross-cutting concerns like auth and logging in every service. A gateway centralizes these, simplifying the architecture.
Use a centralized, high-performance store like Redis to maintain counters. Gateways check the current count against the limit before forwarding the request, ensuring consistency across all instances.
An API Gateway handles north-south traffic (external to internal), while a service mesh handles east-west traffic (internal service-to-service). They complement each other in complex microservice environments.
Keep logic at the gateway lightweight, use connection pooling, offload heavy processing to backends, and scale the gateway horizontally.
Yes, modern gateways like Envoy or Kong support WebSockets by upgrading the connection and maintaining long-lived state, though it requires careful resource management.
Validate tokens (like JWT) at the gateway using a local public key or a JWKS endpoint. This avoids a round-trip to the identity provider for every request.
Monitor error rates and latency for specific backend services. If thresholds are exceeded, the gateway trips the circuit, returning an error immediately without calling the backend.
Managed gateways (AWS/GCP) offer lower operational overhead and high availability but can be more expensive and offer less customization. Self-hosted provides full control but requires significant engineering effort.
The gateway receives a single client request, makes multiple parallel calls to backend services, aggregates the results, and returns a single response to the client.
AI Prep covers AI Agents, Generative AI, ML Fundamentals, NLP & LLMs and a lot more, with adaptive tests and daily challenges. Fully offline on Android. Free to try, one-time unlock for lifetime access.