An AI gateway built by a security company is a different product from an API gateway that added AI plugins.

TrustGate was designed for AI traffic from the start: session memory, 200+ pre-built MCP servers, and inline security built in. On the other hand, Kong AI Gateway routes AI traffic through API infrastructure. Both are open source. The difference is how the gateway is built to understand the differences.

An AI gateway built by a security company is architecturally different from an AI gateway built by a software company that added security plugins. That distinction is not marketing language. It determines where the security enforcement lives, who maintains it, and whether it operates inline before actions execute or after the fact through external integrations.

This comparison covers TrustGate and Kong AI Gateway on exactly those terms: for architecture leaders deciding which model fits their enterprise AI security mandate.

NeuralTrust TrustGate is an AI gateway designed from the ground up for LLM, MCP, and agent traffic. Kong took a different path: it added a set of AI plugins on top of an existing API gateway. When you are choosing the control layer for your AI traffic, that difference in origin shapes everything, from how the gateway understands a conversation to how quickly you can connect and govern the tools your agents use.

TL;DR

NeuralTrust understands sessions, not just isolated requests. It has session memory and analyzes traffic across a whole conversation. Kong inspects each request on its own, with no session-level view.
NeuralTrust ships an integrated catalog of 200+ third-party MCP servers (GitHub, Notion, Figma, and more) ready to connect and govern. Kong has no such catalog: you insert and configure each MCP server manually.
NeuralTrust is a purpose-built AI gateway. Its AI capabilities are native to the product, not add-ons layered on API infrastructure.
NeuralTrust has a native activity view. You inspect every individual request (status, endpoint, model, latency) inside the gateway itself. Kong emits per-request data to logs and OpenTelemetry, but the message-by-message view lives in external tools you export to and maintain.
Both are open source, so the real decision is not licensing, it is whether the gateway was designed for AI traffic or adapted to carry it.

NeuralTrust vs Kong: AI Gateway Comparison at a Glance

Capability	NeuralTrust	Kong
Open-source license	✅	✅
Flexible deployment (private, cloud)	✅	✅
Purpose-built for AI	✅	❌
First-party security engine	✅	❌
MCP catalog integrated (+200 MCP servers)	✅	❌
Native activity view	✅	❌
Enterprise readiness	✅	❌

NeuralTrust vs. Kong: Platform Overview

What is NeuralTrust TrustGate?

TrustGate is NeuralTrust's AI gateway. It sits between agents and the services they call (LLM providers, MCP servers, and other agents) and becomes the single place where routing, policy, and observability attach across all three kinds of AI traffic.

Its core abstractions are Consumers, Providers, Routes, and Policies: provider connections are configured once and reused, while routing, failover, retries, and caching live in the gateway rather than in each application's code.

What makes TrustGate a native AI gateway is that it works at the level AI traffic actually operates. It maintains session memory, so it understands a conversation as a whole rather than as a series of unrelated requests, and it treats LLM calls and MCP tool calls as first-class citizens in one model.

On top of that, it ships an integrated catalog of more than 200 third-party MCP servers, so the tools your agents use are connected and governed through the gateway from the moment they are enabled. It is a control plane built for how agents behave, not adapted to it.

What is Kong AI Gateway?

Kong AI Gateway is not a standalone product. It is a set of AI plugins layered on Kong Gateway, an API gateway built on Nginx and OpenResty. Kong approaches AI as more traffic to route through an API gateway: LLM routing, prompt handling, and MCP support are delivered as plugins added onto an API-management core.

That framing has consequences for how the gateway behaves. Those plugins operate on individual requests, with no session-level view of a conversation, and MCP servers are something you wire up and configure yourself, one by one. It is API infrastructure extended toward AI, rather than a gateway built for AI from the start.

The Main Difference: Purpose-Built for AI vs an Adapted API Gateway

TrustGate was designed from the ground up as an AI gateway. Its core model (Consumers, Providers, Routes, Policies) and its defining behaviors (session memory, an integrated MCP catalog, a native activity view, LLM and MCP traffic handled as first-class citizens) exist because they were the point of the product from day one. Nothing here is retrofitted. The gateway understands AI traffic natively because that is the only thing it was ever meant to carry.

Kong is an API gateway, and its AI capabilities are plugins added on top of it. Kong's own documentation describes the AI Gateway as a set of AI features built on top of Kong Gateway. That heritage is exactly why the gaps in this comparison exist: an API gateway processes requests, so session-level awareness is not part of its model; it was built to route to your services, so MCP servers are something you configure by hand rather than a ready catalog; and its visibility model is API analytics, so per-request AI detail lives in exported telemetry. None of these are oversights in a bad API gateway. They are the natural shape of an API gateway asked to do a job it was not designed for.

The distinction matters because AI traffic is not just more API traffic. It is conversational, agentic, and tool-driven, and it carries risks and patterns that only make sense at the level of a session and a workflow. A gateway built for that operates the way AI actually works. A gateway adapted to it inherits the shape of what it used to be. That is the difference between NeuralTrust and Kong, and every row in the table above is a consequence of it.

Built-In Security: Session Memory & Inline Protection

AI traffic is conversational. An agent's behavior unfolds across many turns, and a gateway that only sees one request at a time is missing most of the picture, whether the goal is understanding intent, applying policy, or protecting the traffic.

TrustGate has security built into the gateway itself, and its foundation is session memory. It understands that what an agent is doing rarely lives in a single prompt, so it analyzes behavior across the whole conversation and enforces policy inline before a request reaches its target.

That conversation-level awareness is what lets it apply protection intelligently and catch patterns, including coordinated abuse and multi-turn attacks, that only become visible when you look at the session rather than an isolated message. Protection is native to the gateway, not something you add on afterward.

Kong has no equivalent built into the gateway. Its AI protection is delivered through plugins that operate on each request in isolation, with no concept of session memory and no conversation-level view. Anything that depends on understanding how a sequence of turns fits together, from nuanced policy to catching a pattern that spreads across several messages, is outside what a request-level model can see.

This is not a tuning problem you fix with a better rule: an API gateway is built to process requests, and the connective tissue of an AI conversation lives between them. NeuralTrust models that connective tissue by design. Kong does not.

MCP Catalog: Integrated vs Manual Setup

As enterprises adopt agents, the Model Context Protocol becomes the backbone of how agents reach tools, and how quickly and cleanly you can connect those tools becomes a core question for any AI gateway.

TrustGate ships with an integrated catalog of more than 200 third-party MCP servers, the tools teams actually use, such as Notion, Github, Figma, and many others, ready to connect and govern through the gateway. The value is twofold. Operationally, your teams do not spend weeks wiring up and maintaining MCP server connections one by one; the connectors are already there.

And because the connection runs through the gateway, that tool traffic is under routing, policy, and observation from the moment it is enabled, rather than as an afterthought. It is the kind of out-of-the-box capability you expect from a gateway designed for agentic workloads.

Kong offers nothing comparable. In Kong, MCP is something you build and configure yourself, server by server, defining the setup for each. There is no ready catalog of 200+ third-party MCP servers to connect. That means more engineering time, a slower path to production for agentic use cases, and more surface for configuration mistakes as the number of tools grows. What TrustGate delivers out of the box, Kong leaves as manual work.

The gap widens with scale. Connecting a handful of tools by hand is manageable; standing up and governing hundreds of tool connections across many teams is a program of work in Kong. In TrustGate it is a configuration step, because the catalog and the gateway governance come together.

Native Activity View vs. Exported Telemetry

When something goes wrong with an agent, or when you simply need to understand what happened, the question is not "what are my aggregate token numbers" but "show me this exact request, right now." How quickly you can answer that depends on whether request-level visibility is built into the gateway or something you have to assemble.

TrustGate has a native Activity view built into the gateway. Every single request is there to inspect on its own: timestamp, traffic type, status code, the endpoint that was called, the model that served it, and latency, request by request, in the gateway's own interface.

You do not stand up a logging pipeline or a dashboarding tool to see it; you open the Activity view and you see exactly what happened, message by message. That is the difference between understanding a specific interaction in seconds and reconstructing it after the fact. Because the detail lives in the product, the moment a request returns a 401 or a 429, you can see that individual event and trace precisely what occurred.

Kong takes a different approach. It emits per-request data through logs and OpenTelemetry span attributes, and its ready-made visual experience is aggregate: Konnect Advanced Analytics dashboards for token usage, cost, and latency by model or provider.

To get the message-by-message, inspect-this-exact-request experience, you typically export those logs and traces into an external stack (Kibana, Grafana, Datadog, Dynatrace) or rely on the premium Konnect Analytics tier, and then build the views yourself. The data exists, but the out-of-the-box gateway experience leans on aggregate analytics, and the granular per-request detail lives outside the gateway in tooling you have to wire up and maintain.

The practical consequence is speed and clarity. With TrustGate you open the gateway and read exactly what happened on any given request. With Kong you first decide where that detail will live, connect it, and maintain it, before you can answer the same question. For teams that want to understand precisely what their agents are doing, a native activity view is a clear advantage.

NeuralTrust vs. Kong: Final Verdict

An AI gateway is not an API gateway with a few extra plugins. AI traffic is conversational, agentic, and tool-driven, and a gateway built for it behaves differently from one adapted to carry it.

NeuralTrust built TrustGate as an AI gateway from the start: session memory that understands a conversation as a whole, LLM and MCP traffic handled natively, an integrated catalog of 200+ MCP servers governed the moment they connect, and a native activity view where you inspect exactly what happened on any individual request.

Kong takes AI traffic and routes it through an API gateway, and its behavior reflects that: request-level plugins with no session view, MCP servers you assemble by hand, and per-request detail that lives in external telemetry tools you export to and maintain.

Both are open source, so the choice is not about licensing. It is about whether you want a gateway that carries AI traffic or one that was designed for it.

If you are building on agents and want a gateway that operates the way AI actually works, NeuralTrust is built for exactly that.

Frequently Asked Questions about NeuralTrust vs. Kong's AI Gateway

1. What is the main difference between NeuralTrust and Kong AI Gateway?

NeuralTrust TrustGate is a purpose-built AI gateway: session memory, and LLM and MCP traffic handled as first-class capabilities in one model. Kong AI Gateway is a set of AI plugins layered on top of an API gateway, so its AI capabilities are add-ons on an API-management core rather than native to the product.

2. What is session memory and why does it matter for a gateway?

Session memory means the gateway understands a conversation as a whole rather than judging each request in isolation. That matters because AI traffic is conversational: intent, policy, and abuse patterns often span many turns. NeuralTrust analyzes behavior across the whole session, while Kong's AI plugins operate on each request on its own, with no conversation-level view.

3. What is the integrated MCP catalog and why does it matter?

NeuralTrust ships an integrated catalog of more than 200 third-party MCP servers (such as Notion and Github) that are ready to connect and are governed through the gateway from the moment they are enabled. This saves significant engineering time and puts tool traffic under routing, policy, and observation by default. Kong has no equivalent catalog: MCP servers must be configured manually, one by one.

4. Are both NeuralTrust and Kong open source?

Yes, both offer an open-source gateway, which is why licensing is not the deciding factor between them. The meaningful difference is architectural: NeuralTrust was designed for AI traffic, while Kong routes it as an extension of an API gateway.

5. What is the native activity view and how is it different from Kong's analytics?

NeuralTrust's activity view is built into the gateway and lets you inspect every individual request on its own: timestamp, traffic type, status code, endpoint, model, and latency, message by message, without setting up any external tooling.

Kong emits per-request data through logs and OpenTelemetry span attributes, and its ready-made visual experience is aggregate analytics (token usage, cost, and latency by model or provider) in Konnect.

To get the same message-by-message inspection, you typically export Kong's logs and traces into an external stack such as Kibana, Grafana, or Datadog, or use the premium Konnect Analytics tier, and build the views yourself.

6. Which should I choose for enterprise agentic AI?

If you are building on agents and want a gateway that operates the way AI traffic actually works, with session-level awareness and a fast, governed path to connecting MCP tools, NeuralTrust TrustGate is purpose-built for that. It is the AI gateway built for AI, not adapted to it.

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About the Author

Alessandro Pignati is Lead AI Security Researcher at NeuralTrust, where he leads research on AI and agentic security, advancing techniques to evaluate and secure large language models and autonomous AI systems. He specializes in adversarial machine learning, AI red teaming, LLM security, and AI safety. Connect on LinkedIn

NeuralTrust is an AI agent security platform, recognized in the Gartner 2025 Market Guide for AI Gateways and Guardian Agents, and the KuppingerCole 2025 Leadership Compass for Generative AI Defense. Headquartered in Barcelona with ISO 27001 certification.

NeuralTrust vs. Kong: AI Gateway Comparison 2026