TL;DR: Enterprise AI gateway selection now hinges on a split between general API management extended for AI and purpose-built AI agent governance, with platforms differing on MCP control, per-agent identity, and visibility beyond gateway traffic, according to MintMCP. The central question is whether governance must treat agents as first-class principals rather than just another traffic source.
Editorial analysis by NHI Mgmt Group, based on content published by MintMCP: “Comparing Enterprise AI Gateway Options (and How Mintive, Kong, Portkey, LiteLLM and TrueFoundry Stack Up) (2026)”.
Key questions
Q: What should teams evaluate before adopting a unified AI gateway?
A: Teams should evaluate where identity authority, policy enforcement, and audit logging actually sit, and whether those functions are preserved outside the application layer.
Q: Why do AI agents need more than standard API gateway controls?
A: API gateways govern request traffic, but agents also need principal-level identity, scoped tool authority, and lifecycle controls.
Q: Where do AI gateway programmes fail in practice?
A: They fail when tool access is easier to grant than to revoke, when connector sprawl outruns ownership, and when off-gateway activity is invisible.
Practitioner guidance
- Map agent governance requirements before selecting a gateway Separate requirements for API routing, MCP governance, per-agent identity, and off-gateway monitoring before comparing platforms.
- Inventory every agent principal and connector Create a register of agents, MCP servers, hosted connectors, and delegated credentials so each access path can be owned, reviewed, and retired on a defined schedule.
- Require independent revocation for each agent Design the identity model so compromise or decommissioning of one agent does not affect other agents or human accounts, and so credential revocation is immediate and isolated.
Bottom line: AI gateway selection now depends on whether the organisation needs request routing or true agent governance.
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