Kong AI Gateway vs. Tetrate Agent Router Enterprise: Which Fits AI Traffic?

TL;DR

Kong AI Gateway extends Kong’s nginx/Lua-based API platform into AI, MCP, and agent-to-agent traffic through plugins (MCP in 3.12, A2A in 3.14). Kong remains a proven choice for REST and gRPC API management. Tetrate Agent Router Enterprise is an enterprise AI gateway where model routing, MCP tool governance, cost attribution, and agent guardrails are part of the data plane itself. If Kong already runs your APIs, keep it there. Put AI and agent traffic on Agent Router Enterprise. The architectural question is API gateway + AI plugins vs. purpose-built AI gateway.

What Kong AI Gateway is

Kong AI Gateway is Kong’s AI product line on top of Kong Gateway, the widely deployed open-source API gateway built on nginx and OpenResty. Per Kong’s own product documentation, it adds token-based rate limiting (by total, prompt, or completion tokens), semantic caching backed by Redis vector search, prompt guardrails, and PII sanitization to LLM traffic. MCP support shipped in Gateway 3.12, and Agent Gateway capabilities for agent-to-agent traffic arrived in AI Gateway 3.14. The open-source core carries basic AI plugins, and the full AI Gateway runs through Kong Konnect or Kong Gateway Enterprise.

Those are real, shipped capabilities. The comparison below covers architecture and operating model: what it means to run AI traffic through an API platform extended with plugins versus a data plane built for AI traffic from the start.

What each product is for

Kong AI Gateway extends Kong’s nginx/Lua API management platform into AI, MCP, and agent-to-agent (A2A) traffic via plugins. It shipped MCP support in 3.12 and Agent Gateway capabilities in 3.14. Kong remains a strong API ingress choice.

Tetrate Agent Router Enterprise starts from a purpose-built AI gateway on Envoy. Tetrate co-created the Agent Router project (formerly Envoy AI Gateway) at the Agentic AI Foundation (AAIF) with Bloomberg and continues to maintain it. Agent Router Enterprise productizes the project with managed operations, authenticated identity, per-team cost attribution, MCP tool governance, and immutable audit logs.

For organizations already standardized on Kong for APIs, the key question is layering: stretch an nginx-based API stack into AI via plugins, or keep Kong on API ingress and put agent traffic on an AI-native plane.

When Kong is already your API gateway

Problem. Agent traffic is not the same as REST/gRPC API management. Production agents need token economics, approved model catalogs, MCP tool governance, per-team showback and chargeback, runtime guardrails, and often region-specific residency policies. An API gateway was not designed for those controls. Forcing them into a plugin model can slow AI rollout or leave finance and compliance teams without attribution or audit records.

Solution. Leave Kong on existing API ingress, where a proven API gateway still fits. Route LLM, MCP, and agent traffic through Agent Router Enterprise, which runs the Agent Router project data plane wherever agents run, under a Tetrate-run management plane. See also AI gateway vs. API gateway.

Outcome. No API migration. Kong stays on existing API workloads. Teams get safe, governed AI on an AI-native plane: per-person, team, agent, and project cost attribution with immutable audit logs, without waiting for every AI control to land as a Kong plugin.

Head-to-head comparison

How the pricing models differ

The two products price in structurally different ways, and the difference compounds with multi-model agent traffic. Kong’s published Konnect pricing for AI gateways combines per-control-plane monthly fees, usage charges above included volumes, and a monthly charge for each unique LLM model proxied, with a cap of five models on the Plus tier. SSO and audit logging are Enterprise-only. The structure is predictable at small scale. It grows line by line as control planes, models, and request volume increase.

Agent Router Enterprise is licensed flat, priced by feature set, deployment footprint, and support tier, with no per-token or per-model fee. Procurement can read the cost before the traffic exists. Neither vendor publishes enterprise dollar figures, so the durable comparison is the shape: per-unit metering on one side, flat licensing on the other. Check Kong’s live pricing page for current figures before any evaluation.

One management plane, distributed data planes

Kong’s deployment model is self-host (plus Konnect for managed) on its nginx-based gateway stack. Tetrate Agent Router Enterprise runs a different, AI-native topology: one Tetrate-run management plane that governs distributed data planes deployed wherever your agents run: Tetrate-hosted, inside your own AWS/Azure/GCP VPC, on-premises, at the edge, or per-region with localized model catalogs and guardrails. Each data plane can enforce region-specific compliance policies, model catalogs, and guardrails, all from a single governed control point without duplicating logic in each application.

That model works whether or not Kong is already your API gateway. Kong shops often keep Konnect for existing API traffic and use Agent Router Enterprise as the dedicated AI plane: different layers, with AI-native governance for agents. For enterprises already running Envoy Gateway and Istio for ingress and service mesh, Agent Router Enterprise extends the same distributed architecture to AI traffic: same data plane lineage, same operational model, same team behind it. Kong’s control plane (Konnect) governs Kong Gateway instances, and it does not extend to Envoy-native AI workloads.

The management plane is not in the data path. Each data plane keeps a local copy of the rules. If the management plane is unreachable, AI traffic and speed do not change. Only rule changes wait. Tetrate runs the management plane under SOC 2 Type II controls, backed by a 24/7 support SLA. Each data plane connects out to Tetrate, and nothing connects in. The customer chooses what reaches the management plane, so prompts can stay in the customer’s network.

How the two gateways are built

Source: Kong’s hybrid mode documentation.

Kong’s Konnect and Tetrate both keep the control part away from live traffic, so Kong data planes and Tetrate data planes keep running if the central part is down. The main difference is the data plane. Kong is an nginx and Lua API gateway with AI plugins. Tetrate’s data plane is built for AI traffic on Envoy.

A note on performance benchmarks

Kong publishes an open-source benchmark methodology with a public, reproducible test suite. The published figures compare Kong against Portkey and LiteLLM using mock LLM backends under default gateway configurations, which Kong itself acknowledges: their own AI Gateway docs state to “benchmark with your real workload and avoid relying on synthetic or idealized figures.”

In the independent Broadcom/VMware test of the Agent Router project (then named Envoy AI Gateway), the data plane added about 2 ms of overhead, 0.01% of total latency. Overhead stayed flat up to the point where the GPUs saturated, and the test included a three-hour endurance run.

Kong’s headline numbers are therefore a useful baseline for proxy overhead on an nginx-based stack, and a production comparison needs governance load on top. The same caveat applies to Tetrate’s benchmarks. What matters is performance with your policies, plugins, and auth enabled. Both vendors publish open methodologies, so run both under your actual policy profile before deciding.

Choose Kong AI Gateway when

• You want one control plane for API, LLM, MCP, and A2A traffic, even if that means extending an nginx-based API gateway with AI plugins.
• Your AI and agent traffic is a thin extension of existing API workloads already on Kong.
• You are deeply invested in Kong plugins and operations and prefer not to introduce a second gateway tier.

Choose Tetrate Agent Router Enterprise when

• Kong already runs your APIs and you want an AI-native agent gateway alongside it, without replacing Kong for API traffic.
• You need MCP, guardrails, and cost attribution built into the data plane.
• You want your gateway operated by the team that co-created the Agent Router project and maintains it.
• You need immutable audit logs, data-residency control, and cross-team governance for regulated AI deployment.

Frequently asked questions

What is Kong AI Gateway? Kong AI Gateway is Kong’s AI product line on top of its open-source API gateway, adding token-based rate limiting, semantic caching, prompt guardrails, PII sanitization, MCP support (3.12), and agent-to-agent capabilities (3.14) through plugins, delivered via Kong Konnect or Kong Gateway Enterprise.

What is the best Kong AI Gateway alternative? It depends on which layer you are replacing. For AI traffic specifically, Agent Router Enterprise is the purpose-built alternative, running the Agent Router project data plane with native MCP governance, cost attribution, and guardrails. For general API gateway alternatives, vendors such as Apigee and KrakenD compete with Kong on the API layer, which Tetrate does not.

Can I run Tetrate Agent Router Enterprise alongside Kong? Yes, and for Kong-incumbent API teams, that is often the preferred architecture. Keep Kong on existing API ingress for REST/gRPC and related services. Put LLM, MCP, and agent traffic through Tetrate Agent Router Enterprise as the AI-native plane. The two address different layers: Kong stays the API gateway. Agent Router Enterprise governs model access, tool catalogs, cost attribution, and agent guardrails.

Should I replace Kong with Tetrate Agent Router Enterprise? For API traffic, no. Use Kong (or your existing API gateway) for APIs. Use Agent Router Enterprise for AI and agent traffic. Kong’s nginx heritage fits API ingress, and Agent Router Enterprise’s AI-native design fits agents.

Is Kong’s performance advantage real in production? Kong’s published benchmark uses mock LLM backends under default configurations: a controlled proxy-overhead measurement. A production decision needs the same test with governance policies loaded. Kong’s own docs explicitly recommend against relying on synthetic figures. Run both under your actual policy and plugin load before drawing conclusions.

Is Kong AI-native? Kong’s architecture is an nginx/Lua API management platform, and its AI capabilities arrive through plugins (MCP Proxy in 3.12, A2A in 3.14). That is a credible path for teams that want one Kong plane for APIs and AI. Tetrate Agent Router Enterprise takes the other architecture: MCP, guardrails, and attribution are part of the Agent Router project data plane. They run wherever your data plane runs, including on-premises under a Tetrate-run management plane.

Compare other gateways: vs. OpenRouter · vs. Portkey · vs. Bifrost · vs. Cloudflare AI Gateway · vs. the Agent Router project (OSS) · vs. LiteLLM

See the full 2026 enterprise AI gateway comparison.