The 5 LLM Architecture Patterns That Scale (And 2 That Do Not)

After building LLM features for 18 months, here are the architecture patterns I have seen work at scale. And the two that consistently fail. Patterns That Scale 1. Prompt-as-a-Service User Input → Prompt Template → LLM API → Response → User Simple. Reliable. Easy to debug. Most LLM features should start here. 2. Retrieval-Augmented Generation (RAG) Query → Vector Search → Context → Prompt → LLM → Response Good for question answering, knowledge bases, anything requiring specific information. 3. Agentic Workflows Task → LLM Planning → Tool Calls → Review → Output For complex tasks requiring multiple steps. More powerful but harder to debug. 4. Caching Layer Input → Cache Check → [HIT] → Response → [MISS] → LLM → Cache → Response Reduces cost and latency for repeated queries. Essential at scale. 5. Human-in-the-Loop LLM Output → Human Review → [APPROVE] → Output → [REJECT] → Retry For high-stakes decisions. Expensive but necessary for compliance. Patterns That Do Not Scale 1. Direct Datab