System Design for AI Systems

Building at scale requires thinking beyond the model.

Core Components

Request routing. Where does the request go? Do you need multiple models for different tasks? Load balancing? Fallbacks?

Caching strategy. Cache embeddings, retrieve results, LLM responses. The right cache hits reduce latency 10x and cost 5x.

Rate limiting and quotas. Per user, per feature, per API. Prevents runaway costs and protects against abuse.

Error handling and fallbacks. LLM APIs fail. Models time out. Gracefully degrade: return cached results, use fallback model, return "I don't know."

Monitoring and observability. Log prompts, latency per step, model outputs, user satisfaction. Without logging you're blind.

Data pipeline. How does data flow from users → storage → retrieval? Is it real-time or batch? Can you update it without redeploying?

Architectural Patterns

Async processing. Embedding generation and vector search shouldn't block user responses. Queue them.

Streaming responses. Return the first token in 500ms instead of waiting 3 seconds. Dramatically improves perceived latency.

Circuit breakers. If an API is down, fail fast instead of waiting for timeout.

Batching. If you don't need real-time response, batch requests and process them together.

Scaling Considerations

Most scaling issues aren't about the LLM — they're about retrieval, caching, and orchestration. Scale retrieval first. Then optimize LLM calls.

The bottleneck usually isn't the model.