Kong vs LiteLLM for enterprise AI gateway governance in production

At a glance

What this is: This is Kong’s comparison of LiteLLM and Kong AI Gateway, focused on what changes when AI traffic moves from baseline routing into production governance.

Why it matters: It matters because AI gateways are becoming part of the identity and access control plane, especially where service accounts, non-human identities, and agentic workflows need policy enforcement, auditability, and compliance.

By the numbers:

• In a public head-to-head performance benchmark, Kong measured 859% higher throughput and 86% lower latency than LiteLLM in the tested environment.
• Kong’s AI PII Sanitizer enforces DLP across 20+ PII categories on both prompts and responses under one audit trail.
• Kong backs Konnect with a 99.9% uptime SLA, while Severity 1 incidents receive a 30-minute, 1-hour, or 2-hour initial response depending on support tier.

👉 Read Kong’s comparison of LiteLLM and Kong AI Gateway for enterprise production

Context

AI gateways are no longer just routing layers for model calls. They are becoming the control point where organisations decide how prompts, responses, tool access, and agent traffic are authorised, observed, and constrained.

Kong’s comparison with LiteLLM sits in that shift. The core question for practitioners is whether AI traffic can be governed with the same policy, identity, and audit expectations that already apply to APIs, service accounts, and non-human identities.

Key questions

Q: How should security teams govern AI gateway access in production?

A: They should treat the gateway as an enforcement point for identity, policy, and audit, not as a convenience layer for routing. That means binding access to enterprise identities, enforcing route and model-level policy consistently, and keeping prompts, responses, and tool calls inside one auditable control path.

Q: Why do AI gateways create new identity governance concerns?

A: AI gateways sit between users, service accounts, agents, and models, so they become the place where identity, authorisation, and data controls either stay coherent or fragment. If governance is split across code, plugins, and side integrations, compliance drift and policy gaps appear quickly.

Q: What breaks when MCP tool access is not default-deny?

A: Tool discovery and invocation become open-ended privilege expansion paths. Without default-deny controls, teams cannot reliably prove which tools an agent can reach, which scopes were granted, or whether those calls were logged in a way that supports audit and incident review.

Q: Should organisations evaluate uptime and patch SLAs for AI gateways?

A: Yes, because the gateway is now part of the security boundary and the operational blast radius. If patch timing, incident response, or release integrity are unclear, security teams inherit uncertainty at the exact layer that protects prompts, data, and agent access.

Technical breakdown

Multi-LLM routing becomes a control-plane problem

Multi-LLM routing is only the starting point. Once an AI gateway carries shared production traffic, throughput, latency, and policy evaluation overhead all affect how many models, teams, and workloads the platform can support without creating operational drift. At that stage, the gateway is no longer a convenience layer. It is part of the enforcement path, where routing choices, budget limits, and observability must remain consistent under load. That consistency is what separates a dev-friendly gateway from one that can anchor enterprise AI governance.

Practical implication: evaluate whether the gateway can sustain policy enforcement at production scale before you commit core AI traffic to it.

Identity and access control for AI traffic

AI traffic inherits the same identity questions that govern APIs and workloads: who or what is allowed to call a model, which credentials are trusted, and whether access can be scoped to the route, model, or consumer. When those controls live outside the gateway, teams end up reimplementing them in application code, which weakens consistency and auditability. In enterprise settings, OIDC, mTLS, ACLs, and IAM-native identities matter because they determine whether AI systems fit into the existing identity model or become a separate trust domain.

Practical implication: map AI gateway authentication and authorisation to your existing IAM model before allowing service accounts or agents into production paths.

MCP and agent-to-agent governance need default-deny thinking

MCP expands the problem from model access to tool access. Once agents can discover tools, invoke functions, or message other agents, the gateway has to enforce scope, default-deny posture, and auditable authorisation at every hop. That is a governance problem, not just an integration problem. The same applies to A2A traffic: if agent messages are treated as ordinary internal calls, privilege boundaries blur quickly and accountability weakens. The practical standard is to govern agent and tool traffic as privileged identity activity, not as generic application chatter.

Practical implication: require default-deny tool discovery and invocation controls before exposing MCP servers or agent-to-agent channels.

NHI Mgmt Group analysis

AI gateways are becoming identity enforcement points, not just traffic routers. Once AI systems are used in production, the control question shifts from whether a model can be reached to whether the call is authorised, attributable, and auditable. That makes gateway choice an identity architecture decision as much as a platform decision. Practitioners should treat the gateway as part of the access-control boundary, not a separate AI feature layer.

Policy granularity is the real production test for AI governance. A gateway that can express per-user, per-group, per-model, and per-route controls in one place reduces the risk of duplicate rules and orphan policy paths. Fragmented configuration surfaces create blind spots that show up only after policy drift has already spread across teams. The implication is that governance quality depends on how cleanly policy can be composed and enforced, not on whether policy exists at all.

MCP governance makes default-deny the baseline for agentic access. Tool discovery and tool invocation are privileged actions, because each one can expand what an agent can do in the environment. When those actions are governed outside the platform layer, teams lose consistency in audit and scope enforcement. Practitioners should assume that every exposed tool increases identity risk unless the gateway can prove otherwise.

Operational commitments matter because AI gateways now carry security obligations, not just workload load. Patch SLAs, uptime guarantees, and release integrity have direct implications for identity and data protection when the gateway sits on the production path. A missing operational commitment becomes an uncertainty multiplier for security, resilience, and compliance teams. The practical conclusion is that procurement and architecture review must consider lifecycle assurance, not only feature parity.

Identity blast radius: the effective scope of damage when an AI gateway or agent is over-trusted is defined by how far its credentials, policies, and tool reach extend across models and services. This post shows why blast radius is now a governance metric for AI traffic, not just a breach metric for secrets. Teams that cannot bound identity reach will struggle to bound cost, compliance exposure, or agent misuse. The implication is to measure AI control by containment potential, not by routing coverage alone.

From our research:

• Kong’s AI PII Sanitizer enforces DLP across 20+ PII categories on both prompts and responses under one audit trail, according to DeepSeek breach.
• Kong’s benchmark comparison also reports 859% higher throughput and 86% lower latency in the tested environment.
• For the production governance angle, see OWASP Agentic AI Top 10 for the control risks that appear when agents and tools share the same path.

What this signals

Identity blast radius is now a practical design constraint for AI gateways. If policy, identity, and tool access are split across multiple layers, the organisation will struggle to prove which calls were authorised, which were merely routed, and which expanded privilege without review.

Kong’s comparison reinforces a pattern we are seeing across AI programmes: the most useful gateway is the one that can absorb AI traffic into existing IAM, audit, and compliance controls without creating a second trust stack. That is especially relevant where service accounts and non-human identities already underpin the workload model.

The governance signal is simple. As AI usage moves from experimentation into production, teams need control surfaces that can keep pace with model churn, agent growth, and tool exposure. OWASP Agentic AI Top 10 remains a useful reference point for the failure modes that appear when those surfaces are too thin.

For practitioners

• Consolidate AI gateway policy into one enforcement layer Avoid splitting routing, budgets, DLP, and access checks across separate tools unless you can prove the combined policy path is still auditable end to end.
• Bind AI traffic to enterprise identity controls Require OIDC, mTLS, ACLs, or equivalent IAM-native controls for model calls, service accounts, and agent traffic so access does not become a parallel trust model.
• Treat MCP tool access as privileged access Apply default-deny rules to tool discovery and invocation, and review each exposed tool as an increment in identity blast radius.
• Assess operational assurance before production rollout Check patching commitments, uptime SLAs, and release integrity evidence before you allow the gateway to carry regulated or high-volume AI traffic.

Key takeaways

• AI gateway selection is now an identity governance decision because routing, access, and audit controls are converging in the same layer.
• Production AI traffic exposes the weakness of fragmented policy models, especially when model access, tool access, and identity controls live in different systems.
• Teams should evaluate default-deny MCP controls, enterprise identity integration, and operational assurance before promoting AI gateways into the production security path.

Key terms

• AI Gateway: An AI gateway is a control layer that sits between applications, users, and model providers to enforce routing, policy, observability, and security. In practice, it becomes part of the identity boundary when it decides which calls are allowed, how they are logged, and what data can pass through.
• MCP Tool Governance: MCP tool governance is the control of which tools an AI agent can discover, invoke, and chain during runtime. It matters because each allowed tool expands the agent’s reach, so the governance model must define scope, auditability, and denial behaviour before production use.
• Identity Blast Radius: Identity blast radius is the amount of damage that can occur when credentials, policy scope, or trust boundaries are too broad. For AI systems, it includes the model, tool, and data paths a gateway or agent can reach, which makes containment a primary governance concern.
• Policy Composition: Policy composition is the ability to combine access rules, budgets, model controls, and route constraints in one coherent enforcement model. When composition is fragmented across separate configuration surfaces, teams often create overlapping rules, hidden exceptions, and gaps that weaken auditability.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an identity security programme, it is worth exploring.

This post draws on content published by Kong: LiteLLM vs Kong: Choosing the Right Enterprise AI Gateway for Production. Read the original.