Building Across Multiple AI Models: Four Production Lessons

Building tools that query multiple AI models simultaneously exposes fundamental infrastructure challenges that aren't edge cases but core requirements. Rate limiting, smart caching, and transparent model versioning directly impact user experience and system reliability. These lessons are increasingly critical as the LLM landscape evolves rapidly and developers must support multiple competing models without service degradation.