Enterprise AI Gateway: Choosing the Best Option for 2026

Enterprise AI gateways are presented as centralized layers for routing, securing, governing, and auditing AI models, agents, tools, and data access, but the text argues that conventional model-focused gateways often do not solve the separate challenge of reaching live enterprise data in CRM, ERP, and warehouse systems. It positions CData Connect AI Gateway as a combined model and data-access platform, using managed Model Context Protocol (MCP) connections to provide scoped access to hundreds of sources, identity-based permissions, audit logging, SIEM export, and support for multiple AI clients such as ChatGPT, Claude, Copilot, and Gemini. The discussion distinguishes data-layer AI gateways from traditional API gateways, LLM routers, iPaaS products, and native AI-platform connectors by emphasizing source-level permissions, cross-system federation, and semantic context. Citing reports from MIT, Anthropic, and Gartner, it contends that weak integration, inaccessible data, cost concerns, and inadequate controls contribute to failed AI initiatives, while noting that data access addresses only part of these risks. It recommends evaluating gateways on MCP governance, OAuth or SAML identity passthrough, exportable audit trails, vendor neutrality, and secure cross-system data access, while describing CData’s model-routing capabilities as early access as of October 2026.

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