Most teams build MCP (Model Context Protocol) servers as proof-of-concepts. That’s fine - early on, the goal is simply to “make it work.” But problems begin when traffic grows: these systems collapse under load, become unstable, and turn into bottlenecks. Let’s break down why - and what actually works in production. 🚨 Why MCP Servers Fail 1. In-process state PoC servers often store: sessions context cache inside the process memory. Problem: no horizontal scaling restarts wipe state load balancing becomes hard 2. Blocking synchronous flows Typical anti-pattern: direct LLM calls blocking DB queries chained dependencies Result: high latency, low throughput. 3. No rate limiting or backpressure Traffic spikes lead to: unbounded queues resource exhaustion cascading failures 4. Tight coupling to dependencies Direct dependency on: LLM APIs storage external services Any failure propagates system-wide. 🏗 Architecture Patterns That Scale 1. Stateless MCP + External State Keep MCP servers stateless.…