This session introduces the AI Gateway pattern—a central control plane for enterprise AI ecosystems. We'll explore how AI gateways solve real-world challenges through unified API abstraction, intelligent failover mechanisms, semantic caching, centralized guardrails, and granular cost controls. You'll learn practical architectural patterns for building high-availability gateways that handle thousands of concurrent requests while maintaining sub-millisecond decision-making through in-memory operations. The session covers separation of control and data planes, asynchronous logging patterns, and horizontal scaling strategies. It also discusses Model Context Protocol (MCP) integration for managing model access and tool ecosystems to enable natural language automation across enterprise software. Key takeaways include gateway design principles, performance optimization strategies, multi-provider management patterns, and a framework for evaluating AI infrastructure needs.