AI gateways, designed specifically for AI workloads, offer significant advantages over traditional API gateways, which excel in routing and authentication but struggle with AI tasks such as token tracking and semantic caching. AI gateways are tailored to manage AI-specific needs, including intelligent model routing, token-level cost management, semantic caching, and streaming-native architectures, making them crucial for handling the demands of large language models (LLMs). The market for AI gateways is rapidly growing, with projections indicating a rise from USD 3.9 billion in 2024 to USD 9.8 billion by 2031. This growth reflects the increasing need for specialized infrastructure capable of addressing security vulnerabilities, cost overruns, and governance challenges associated with AI deployments. While API gateways remain essential for traditional traffic management, AI gateways offer an optimized solution for AI inference, promising reduced costs, improved performance, and centralized governance. As organizations navigate these evolving demands, a layered approach combining both gateway types can provide a comprehensive strategy to harness AI's full potential while maintaining robust security and cost controls.