Topic starter
24/06/2026 7:14 pm
TL;DR: Enterprise AI gateway decisions now hinge on policy granularity, identity integration, MCP governance, and operational commitments, while LiteLLM is positioned as a baseline option for initial AI connectivity, according to Kong. The practical issue is not feature count alone, but whether AI traffic can be governed as part of existing IAM and security controls rather than a parallel stack.
Editorial analysis by NHI Mgmt Group, based on content published by Kong: “Kong AI Gateway vs LiteLLM: Which AI Gateway Scales for Production?”.
Key questions
Q: What breaks when AI gateway policy is split across multiple config surfaces?
A: Policy drift starts when teams have to reason about precedence across keys, users, teams, models, and routes in separate places.
Q: Why do service accounts make AI gateway governance harder?
A: Because the traffic is no longer just model-to-model routing.
Q: What are the signs that MCP governance is failing?
A: Common signs include agents reaching systems outside their intended workflow, incomplete audit trails for tool use, and data retrieval that cannot be tied back to a clear business purpose.
Practitioner guidance
- Validate central policy enforcement Test whether the gateway can apply layered controls for consumer, group, model, provider, header, and path without duplicating logic across routes or application code.
- Map AI access to enterprise identity Require service accounts, non-human identities, and delegated workflows to use the same identity and authentication model as the rest of the environment.
- Put MCP tool access behind default deny Treat tool discovery and invocation as governed access, with scope-based authorization and audit logging on every call.
Bottom line: AI gateway selection is becoming an identity governance decision because production traffic now includes service accounts, agents, and tools, not just model calls.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
25/06/2026 4:26 am
AI gateway governance is now an identity control problem. The article shows that the decisive production question is whether AI traffic can be governed as part of the enterprise identity model, not whether it can be routed at all. Once service accounts, non-human identities, and delegated tool access enter the path, gateway policy becomes part of IAM architecture, not an isolated AI feature. Practitioners should treat the gateway as a control plane for identity-bound AI traffic.
A question worth separating out:
Q: Should organisations treat AI gateways like privileged identity controls?
A: Yes. AI gateways mediate access to powerful non-human capabilities, so they need the same discipline used for sensitive service accounts and other privileged access paths. That means authentication, authorisation, logging, rate limiting, and explicit separation between user input and system instructions. Without those controls, the gateway becomes an unmanaged privilege surface.
👉 Read our full editorial: Kong vs LiteLLM for enterprise AI gateway governance in production