We're Entering the Age of AI Connectivity

An enterprise AI gateway should provide a centralized point of policy enforcement for routing, governing, securing, and observing artificial intelligence traffic at scale. LiteLLM is one of many AI gateways that can cover the foundational AI connectivity needs teams often start with. For organizations standing up an initial AI gateway, it can be a natural place to begin.

LiteLLM is an open-source AI gateway that provides baseline capabilities like multi-LLM routing, LLM traffic governance, cost control, and observability. However, the more meaningful comparison begins when organizations need the gateway to scale beyond basic AI connectivity use cases and support real production requirements. For teams exploring LiteLLM alternatives, understanding these differences is essential.

This blog evaluates the major differences between LiteLLM and Kong AI Gateway across the areas that matter most in production: core AI gateway functionality, full AI data path governance, and overall enterprise readiness.

Comparing core AI gateway functionality in production

For many buyers, this is where the evaluation begins: the part of the stack responsible for controlling, shaping, and observing AI traffic as it moves between applications and AI models. Once the baseline requirements are met, the question then shifts from simple feature coverage to how well the gateway holds up as usage grows, policies get more granular, and when multiple teams begin to rely on it as the central control layer.

Multi-LLM routing and performance

Why it matters: Multi-LLM routing is now table stakes for nearly all AI gateways, so the real question is what happens once that routing layer becomes shared infrastructure carrying real production traffic. At that point, throughput and latency translate directly into compute cost. A gateway that handles less traffic per node forces you to run more nodes to absorb the same load.

In a public head-to-head performance benchmark, Kong measured in with 859% higher throughput and 86% lower latency than LiteLLM in the tested environment.

Traffic control and policy granularity

Why it matters: Once a gateway serves multiple teams, policies become a layered system of per-user, per-group, per-model, and per-route controls. When those can't be expressed cleanly in one place, teams could easily duplicate rules, leave coverage gaps, or ship orphan rules that no longer match the paths they're meant to protect.

LiteLLM supports rate limits and budgets, but those dimensions are configured as separate fields on separate entities rather than composed in a single policy.

Kong’s AI rate-limiting plugin can evaluate an ordered list of policies against attributes like consumer, consumer group, model, provider, header, and path. This allows teams to combine per-user, per-group, and per-model controls on the same route instead of spreading them across more complex route and plugin combinations.

Security and compliance

Security and compliance for an AI gateway shows up in two primary places: how data is protected as it moves through the platform, and how access to the platform fits into the broader enterprise identity model. Both Kong and LiteLLM provide coverage in each area, but the consistency of that coverage at production scale is where they diverge.

PII Sanitization and DLP

Why it matters: When PII protection is split across multiple guardrail vendors, reconciling inconsistent DLP across models and consumers can be a challenge.

Kong's AI PII Sanitizer enforces DLP at the gateway across 20+ PII categories on both prompts and responses.

Identity and Access Control

Why it matters: Identity and access controls are where AI traffic either fits into the existing enterprise IAM model or becomes a parallel system that security teams have to govern separately.

LiteLLM supports SSO, SAML, JWT-based authentication, and OAuth 2.0 flows, while Kong supports a broader gateway-layer auth surface, including OIDC, mTLS, ACL enforcement, and multi-cloud IAM integrations.

Full AI data path governance

Full data path governance means securing, governing, and observing more than just LLM traffic between applications and models. Kong brings these traffic patterns together in one platform.

Agent-to-agent governance

Why it matters: Treating agents like users means treating their traffic like authenticated calls. Governance is critical to maintaining transparency in permissions.

MCP governance

Why it matters: Tool access becomes a governance issue very quickly once MCP is part of the stack. Kong provides a broader governance model than LiteLLM.

Enterprise AI gateway readiness

Enterprise readiness comes down to whether a gateway can operate effectively inside the broader platform and operating model of the business.

Self-service access

Why it matters: Self-service capabilities prevent platform teams from becoming a bottleneck for each new app or service account.

Kong combines several tools to support self-service access in an enterprise-oriented fashion.

Cost control and monetization

Why it matters: Real cost governance acts at the gateway layer, preventing runaway spending before it occurs. LiteLLM supports real-time budget enforcement, but Kong provides token-aware rate limiting and product catalog features that enable monetization and accurate billing.

Conclusion: Which AI gateway is built for production?

LiteLLM is a reasonable starting point for teams with baseline AI gateway needs. However, for organizations that need to ensure enterprise-level functions, Kong AI Gateway stands apart.