This session introduces the AI Gateway pattern—a critical architectural component that serves as the 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. We'll dive deep into the technical architecture, covering separation of control and data planes, asynchronous logging patterns, and horizontal scaling strategies. The session will also look ahead to emerging patterns like Model Context Protocol (MCP) integration, where gateways will manage not just model access but entire tool ecosystems, enabling natural language automation across enterprise software. Whether you're an architect planning your AI infrastructure, a platform engineer managing multi-model deployments, or a technical leader navigating AI governance challenges, this session provides actionable insights for building resilient, scalable AI systems. You'll leave with concrete patterns, architectural blueprints, and a roadmap for implementing centralized AI control planes that grow with your organization's AI maturity. Key takeaways include gateway design principles, performance optimization strategies, multi-provider management patterns, and a practical framework for evaluating AI infrastructure needs in your organization.

This session introduces the AI Gateway pattern—a central control plane for enterprise AI ecosystems. 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, with a look ahead to Model Context Protocol (MCP) integration for managing not just model access but entire tool ecosystems, enabling natural language automation across enterprise software.

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.

Overview of the AI Gateway pattern as a central control plane for enterprise AI ecosystems, including API abstraction, failover, semantic caching, centralized guardrails, and cost controls. Discuss practical architectural patterns for high-availability gateways that handle thousands of concurrent requests with sub-millisecond in-memory decision-making, separation of control and data planes, asynchronous logging, horizontal scaling, and future MCP integration for tool ecosystems and natural language automation across enterprise software.