Integrating LLMs into production is easy for a demo, but scaling them for thousands of concurrent users reveals a harsh reality: they are high-latency, expensive, and fundamentally non-deterministic. Traditional API Gateways weren't built for 60-second request times or "hallucinating" status codes.
In this session, we move beyond basic API calls to explore the AI Gateway Pattern. We will dive into how to build a resilient orchestration layer that treats AI models as unreliable downstream dependencies rather than standard microservices. You will see how to implement "semantic caching" to save costs, handle token-based rate limiting, and use circuit breakers to failover between disparate models (e.g., from GPT-4 to a local Llama instance).
We will walk through the transition from synchronous "Request/Response" to Event-Driven AI orchestration, ensuring your architecture remains responsive even when the model is slow. This is a session about the "plumbing" of AI—security, observability, and stability—designed for engineers who need to move AI from a playground to a production-grade system.