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This comparison is written for enterprise security and platform leaders (CISOs, CIOs, Heads of AI, and platform engineering teams) evaluating an AI gateway as the control layer for LLM, MCP (Model Context Protocol), and agent-to-agent (A2A) traffic. It compares two products in that category: NeuralTrust TrustGate and Kong AI Gateway.
An AI gateway sits between AI agents and the services they call (LLM providers, MCP tool servers, and other agents) and becomes the single place where routing, policy, security enforcement, and observability attach. Both TrustGate and Kong AI Gateway govern LLM, MCP, and A2A traffic through one gateway.
The two products approach the category from different starting points. TrustGate is built by a security company and treats runtime security enforcement as the gateway's organizing principle. Kong AI Gateway is a set of AI plugins layered on top of Kong Gateway, the widely deployed, Nginx/OpenResty-based API gateway, extending a mature API-management platform into AI traffic. This article compares them on architecture, runtime security, posture management, governance, MCP and tool controls, observability, and deployment. Only the gateway component of each vendor is in scope.
Related article: The 11 Best AI Gateways for Enterprise AI Security in 2026
Executive Summary (TL;DR)
- TrustGate is security-first, positioning as "the AI gateway built by a security company." Its differentiating design choice is a Security Engine that attaches to every Route, into which NeuralTrust's own runtime security product (TrustGuard) plugs.
- TrustGate emphasizes a single unified multi-protocol trace tree across LLM, MCP, and A2A traffic, with cost, latency, and security findings attributed inline at every level (Consumer, Route, Provider, span).
- NeuralTrust offers a companion posture product (TrustLens) that discovers and assesses AI running outside the gateway's path, a layer Kong does not include in the gateway.
- TrustGate ships a first-party runtime detection engine. Kong's AI security is delivered through plugins that primarily wrap external services (AWS Guardrails, Google Model Armor, Lakera Guard, Mistral moderation) or apply regex and semantic rules.
- Kong AI Gateway is a mature, enterprise-proven platform built on the widely deployed Kong Gateway, with a rich plugin ecosystem, multi-LLM routing, semantic caching, RAG injection, token-aware rate limiting, and strong API-management heritage. This maturity and breadth are genuine strengths.
- Kong governs LLM, MCP, and A2A traffic and adds MCP-specific capabilities (generating MCP servers from Kong-managed APIs, an MCP Registry, auth for MCP access).
- Many of Kong's AI security and governance plugins are Enterprise/Konnect features. The AI Proxy and base logging are open source, but Prompt Guard, PII Sanitizer, advanced guardrails, and audit logging require a commercial license.
- Both are self-hostable and cover LLM, MCP, and A2A; both reserve their enterprise governance and security depth for paid tiers.
Comparison at a Glance
| Feature | NeuralTrust TrustGate | Kong AI Gateway |
|---|---|---|
| AI Gateway (LLM / MCP / A2A) | ✅ | ✅ |
| Runtime AI Security | First-party Security Engine (TrustGuard) attaches to every Route | Plugins wrapping external guardrails + regex/semantic rules; most are Enterprise |
| AI Agent Discovery / Posture | Via TrustLens (separate NeuralTrust product) | Not a gateway feature |
| MCP Governance | Per-Consumer tool access, unified audit | MCP generation from APIs, MCP Registry, auth and access control |
| AI Observability | Unified multi-protocol trace tree; cost/latency/security attribution | L7 AI observability, token/cost tracking, metrics, tracing |
| Deployment | Self-hosted OSS core; managed & hybrid (VPC, air-gapped) at enterprise tier | Self-hosted Kong Gateway; Konnect (SaaS/hybrid); on-prem |
| License | Apache 2.0 | Kong Gateway OSS (Apache 2.0); AI security plugins are Enterprise/Konnect |
| Best For | Security-led buyers wanting a first-party enforcement substrate | Organizations standardizing AI traffic on an existing Kong API estate |
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 is designed to be the one place where routing, policy, security, 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; routing, failover, retries, and caching live in the gateway rather than in each application's code.
TrustGate's defining design choice is that a Security Engine attaches to every Route: when one is attached, every request is inspected and an allow/block/transform decision is executed before the request reaches its target. Security findings render as first-class spans in the same trace tree as operational telemetry. NeuralTrust positions this as the architectural consequence of being "the only AI gateway built by a security company."
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Per NeuralTrust's own documentation, TrustGate is explicitly not itself the content-detection product (that is TrustGuard, the engine that attaches) and not a posture-management product (that is TrustLens, a separate product). The gateway core is Apache 2.0 and open source; the governance layer, retention, and security-finding depth are commercial.
What is Kong AI Gateway?
Kong AI Gateway is a set of AI plugins that run on Kong Gateway, Kong's widely deployed API gateway built on Nginx and OpenResty. Rather than being a separate product, it extends Kong's mature, plugin-based platform to broker and govern AI traffic. Its documented plugins include AI Proxy and AI Proxy Advanced (multi-provider routing, load balancing, retries, fallback), AI Semantic Cache, AI RAG Injector, AI Prompt Decorator, AI Prompt Compressor, AI Rate Limiting Advanced, and AI Sanitizer, among others.
Kong positions the AI Gateway to govern LLM, MCP, and A2A traffic from one gateway. It provides a unified OpenAI-compatible API across many providers (OpenAI, Anthropic, Azure OpenAI, Bedrock, Cohere, Mistral, Vertex, Ollama, self-hosted), PII sanitization across many categories and languages, token-aware quotas with showback/chargeback, and L7 observability on AI traffic. For MCP, Kong can generate MCP tools and servers on top of Kong-managed APIs, enforce auth for MCP access, and offers an MCP Registry (announced in Konnect in February 2026) to register, discover, and govern MCP servers.
A material nuance for enterprise evaluation: Kong Gateway itself is open source (Apache 2.0), and the AI Proxy plus base logging are open source, but the AI security plugins (Prompt Guard, Semantic Prompt Guard, PII Sanitizer, third-party guardrail integrations) and audit logging are Kong Enterprise or Konnect features. The AI security and governance surface is largely a commercial tier.
Architecture Comparison
Both are software gateways that centralize AI traffic and apply policy, and both build on a control-plane / data-plane model.
TrustGate organizes around Consumers, Providers, Routes, and Policies, and is designed so that a Security Engine attaches at the Route level as a foundational concern. It covers LLM, MCP, and A2A traffic in one model and renders security findings inline in the trace tree. NeuralTrust's stated deployment model is an open-source self-hosted core, with managed and hybrid (VPC, air-gapped) deployment at the enterprise tier.
Kong AI Gateway inherits Kong Gateway's proven architecture: a high-performance Nginx/OpenResty data plane with a plugin system, managed either self-hosted or through Kong Konnect (control plane as a service, with local or hybrid data planes). AI capabilities are delivered as plugins attached to routes and services, configured declaratively (for example via decK). The strengths are maturity, performance, and a very large plugin ecosystem that unifies API and AI traffic on one platform. The trade-off, noted in third-party reviews, is that governance depends on plugin configuration, ordering, testing, and lifecycle management, which rewards teams with existing Kong expertise and adds ramp-up for teams new to the model.
Runtime AI Security
This is the sharpest difference, and it is a difference of model and packaging.
TrustGate places a Security Engine attachment at the center of its design: NeuralTrust's own runtime product, TrustGuard, plugs in and inspects every request inline, with the decision executed before the request reaches its target. NeuralTrust positions TrustGuard as delivering conversation-level and multi-turn analysis rather than isolated per-request filtering. Two honest caveats: the content-detection capability is TrustGuard (a separate, attachable product), not the bare gateway, and TrustGuard's specific detections should be verified against its own documentation.
Kong AI Gateway implements runtime security through plugins. It offers AI Prompt Guard (allow/deny prompts via regex rules), AI Semantic Prompt Guard and AI Semantic Response Guard (topic-level filtering by meaning), AI PII Sanitizer (detecting and redacting sensitive data across many categories and languages via an external anonymizer service), and integrations with third-party guardrail services such as AWS Guardrails, Google Cloud Model Armor, Lakera Guard, and Mistral moderation. This is a capable, layered guardrails model. Two points a security buyer should weigh: the deepest checks are delivered by wrapping external services rather than a first-party detection engine, and, per Kong's documentation and third-party analysis, the AI security plugins (Prompt Guard, PII Sanitizer, guardrails) and audit logging are Enterprise/Konnect features rather than part of the open-source gateway.
Bottom line: TrustGate offers a first-party engine from a security company, attached inline as the gateway's organizing principle, with multi-turn analysis positioned as core. Kong offers a broad, layered guardrails toolkit built from plugins and external integrations, with the security-relevant plugins gated behind its commercial tier. Which fits depends on whether the buyer wants a single security-company stack or a plugin-based toolkit on a mature API platform.
AI Security Posture Management (AI-SPM)
Posture management means discovering and assessing AI agents, tools, and models across the estate, including those that do not route through the gateway.
Neither gateway does this on its own. For TrustGate, NeuralTrust's documentation is explicit that discovering and assessing AI outside the gateway's path is the job of TrustLens, a separate NeuralTrust product that consumes gateway signals. For Kong, estate-wide AI discovery and posture assessment is not a documented capability of the AI Gateway itself; Kong governs and observes the traffic that flows through it (and notes that, because all model traffic can route through Kong, the gateway becomes a single choke point for visibility, including unsanctioned or direct-to-provider attempts that still pass through it).
On a strict gateway-only basis this is a tie: neither gateway is a posture-management product. The practical difference is that NeuralTrust offers a dedicated companion posture product (TrustLens) that a customer can evaluate alongside the gateway, whereas Kong's answer to estate-wide posture is not a distinct posture layer but the choke-point visibility of traffic that already routes through the gateway.
AI Governance
| Governance capability | TrustGate | Kong AI Gateway |
|---|---|---|
| Policy enforcement | Operational policy (rate limits, model allowlists, token/cost caps, routing) per request; security via attached engine | AI rate limiting, token quotas, prompt guards, semantic guards; enforced via plugins |
| RBAC | Consumer-based access; enterprise tier adds SSO/SAML, RBAC | Kong Consumers, RBAC and workspaces (Enterprise/Konnect) |
| Identity | Consumer identity model; SSO/SAML at enterprise tier | Auth plugins (key, JWT, OAuth, OIDC); auth for MCP access |
| Audit | Every request logged/traced; findings as spans; long-term retention at enterprise tier | Admin/config audit and content audit channels (Enterprise/Konnect) |
Kong's governance surface is deep and mature, inherited from its API-management heritage: Consumers, a large catalog of auth plugins, RBAC and workspaces, token-aware quotas, and showback/chargeback for LLM, agent, and MCP usage. The nuance for a security review is licensing and configuration: the security and audit plugins are Enterprise/Konnect, and governance quality depends on disciplined plugin configuration and lifecycle management. TrustGate similarly gates SSO/SAML, RBAC, and long-term retention behind its enterprise tier.
MCP & Tool Governance
Both products have a genuine MCP governance story, and Kong's is broad.
TrustGate governs MCP traffic that flows through it: per-Consumer tool access, tracing of every tool invocation (which tool, which arguments, which Consumer, which result, at what cost), and a unified audit trail. NeuralTrust's documentation is explicit that TrustGate is not an MCP connectivity platform and does not ship a library of pre-built MCP integrations; it governs the servers the customer registers.
Kong AI Gateway approaches MCP from its API-management strength. It can generate MCP tools and servers on top of Kong-managed APIs, proxy existing MCP servers, aggregate multiple MCP tools for AI clients, enforce auth for MCP server access, and optimize token spend through MCP context optimization. Kong also offers an MCP Registry in Konnect (announced February 2026) to register, discover, and govern MCP servers and AI-native tools. This ability to turn an existing API estate into governed MCP tools is a distinctive Kong capability for organizations that already run many APIs on Kong.
Neither product ships a large managed catalog of third-party MCP integrations as the gateway's core value. Kong's angle is generating MCP tools from your own APIs; TrustGate governs the servers you register. If the requirement is a big catalog of external pre-built MCP servers, qualify it separately for both.
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Observability
Both provide strong observability, with different emphases.
TrustGate resolves a full agent workflow into a single trace tree (the model call, the MCP tool calls it triggers, and A2A delegations) with cost, latency, and security findings attributed at every level (Consumer, Route, Provider, span). NeuralTrust frames this unified multi-protocol trace as a differentiator, with security findings inline in the same view. Live observability is in the open-source core; long-term retention and historical analytics are commercial.
Kong AI Gateway provides L7 observability on AI traffic: tracking AI consumption, tool usage, and token spend, predictive consumption models for cost optimization, and debugging via logging and tracing (including OpenTelemetry, with A2A-specific metrics such as payloads, latency, token usage, and errors). Kong emits logs, metrics, and audit channels through its standard telemetry paths, which integrate well into existing SOC and observability stacks. Content-level detail (prompt/response text and raw PII) appears only when the operator explicitly enables payload logging, and the deeper security-verdict telemetry depends on the licensed security plugins.
The practical difference is emphasis: TrustGate centers a unified multi-protocol trace with inline security findings; Kong offers mature, telemetry-rich AI observability that plugs into the broader Kong and SOC ecosystem.
Deployment
| Deployment option | TrustGate | Kong AI Gateway |
|---|---|---|
| SaaS / Managed | Managed control plane (enterprise/consumption tier) | Kong Konnect (control plane as a service) |
| Self-hosted | Yes, Apache 2.0 core | Yes, self-hosted Kong Gateway (data plane) |
| Hybrid | Enterprise tier | Konnect hybrid (SaaS control plane, local/hybrid data planes) |
| On-prem / Air-gapped | Enterprise tier (VPC, air-gapped) | Self-managed Kong Gateway; verify air-gapped specifics with Kong |
| A2A traffic | ✅ | ✅ |
Kong's deployment flexibility is a strength: self-hosted Kong Gateway data planes, a managed Konnect control plane, and hybrid topologies that many enterprises already run for API management. TrustGate positions air-gapped and hybrid at its enterprise tier. For organizations that already operate Kong, extending the same platform to AI traffic is a low-friction path; for others, it means adopting Kong's operational model.
Detailed Feature Comparison
| Capability | TrustGate | Kong AI Gateway |
|---|---|---|
| LLM routing (multi-provider, failover) | ✅ | ✅ (AI Proxy Advanced) |
| Semantic caching / routing | ✅ | ✅ |
| RAG injection | Not specified | ✅ (AI RAG Injector) |
| MCP governance | Per-Consumer tool access, unified audit | ✅ Generate MCP from APIs, MCP Registry, auth |
| A2A traffic | ✅ | ✅ (A2A metrics, centralized authN/Z, audit) |
| First-party runtime detection engine | ✅ Security Engine (TrustGuard attaches) | ✗ Plugins wrapping external guardrails; most are Enterprise |
| Companion posture product | ✅ TrustLens (separate NeuralTrust product) | ✗ |
| Unified multi-protocol trace tree | ✅ Positioned as differentiator | L7 AI observability across LLM/MCP/A2A |
| Security plugins in open source | Core OSS; security depth commercial | ✗ Prompt Guard, PII Sanitizer, guardrails are Enterprise |
| Built on existing API-management platform | No (purpose-built AI gateway) | ✅ (Kong Gateway) |
| License | Apache 2.0 | Kong Gateway OSS (Apache 2.0); AI security plugins Enterprise |
Which Platform Should You Choose?
Choose NeuralTrust TrustGate if...
- Your driver is a security mandate and you want the runtime-security engine to be a first-party product from a security company, not a set of plugins that wrap external, separately licensed guardrail services.
- You want multi-turn and conversation-level analysis positioned as core, and you will validate TrustGuard's detections against its documentation.
- You want security findings inline in the same trace tree the platform team already reads.
- You expect to also adopt a companion posture product (TrustLens) to cover AI outside the gateway's path.
- You want the security capabilities available without gating the core security functions behind a commercial API-platform license.
- You do not already run Kong and prefer a purpose-built AI gateway over extending an API-management platform.
Choose Kong AI Gateway if...
- You already run Kong Gateway or Konnect for API management and want to extend a familiar, mature platform to AI traffic with minimal new tooling.
- You value a very large plugin ecosystem and want API and AI traffic governed on one unified platform.
- You want to turn an existing API estate into governed MCP tools and use Kong's MCP Registry.
- You need mature token-aware quotas, showback/chargeback, semantic caching, and RAG injection at the gateway.
- Your team has Kong operational expertise to configure, order, and lifecycle-manage plugins with discipline.
- You are comfortable licensing the Enterprise/Konnect tier for AI security plugins and audit.
Final Verdict
Both are credible, enterprise-capable AI gateways, and on multi-LLM routing, MCP and A2A governance, and observability they are genuinely comparable. Kong's advantages are the maturity and breadth of its platform, a large plugin ecosystem, deep API-management heritage, and the ability to unify API and AI traffic (and turn existing APIs into MCP tools) on infrastructure many enterprises already run. TrustGate's advantages are first-party inline security enforcement, unified multi-protocol tracing with security findings in the same view, and a companion posture product.
The decision usually turns on two axes. The first is the security model and where it lives: TrustGate treats a first-party Security Engine attachment as the gateway's reason for existing and delivers security from a single security company, while Kong delivers a plugin-based guardrails toolkit that wraps external services, with the security-relevant plugins gated behind Enterprise/Konnect. The second is platform starting point: Kong is the natural choice for organizations already standardized on Kong, whereas TrustGate is a purpose-built AI gateway that does not assume an existing API-management investment.
A security-led buyer who wants a first-party enforcement substrate with multi-turn analysis and a dedicated posture companion will likely prefer TrustGate. An organization already invested in Kong that wants to extend one mature platform across API and AI traffic will likely prefer Kong AI Gateway.
Key Takeaways
- TrustGate's biggest differentiators are a first-party Security Engine (TrustGuard), unified multi-protocol tracing with inline security findings, and a companion posture product (TrustLens).
- Kong AI Gateway is a set of AI plugins on the mature, widely deployed Kong Gateway, extending an API-management platform to LLM, MCP, and A2A traffic.
- Kong's runtime security is plugin-based and largely wraps external services (AWS Guardrails, Google Model Armor, Lakera, Mistral moderation) plus regex and semantic rules.
- Kong Gateway and the AI Proxy are open source, but the AI security plugins (Prompt Guard, PII Sanitizer, guardrails) and audit logging are Enterprise/Konnect features.
- Kong can generate governed MCP tools from existing Kong-managed APIs and offers an MCP Registry, a distinctive capability for existing Kong estates.
- Neither gateway ships a large catalog of third-party pre-built MCP servers; both govern the servers you register (Kong also generates them from your APIs).
- Both are self-hostable with control-plane / data-plane models and reserve enterprise governance and security depth for paid tiers.
- The choice usually reduces to a first-party security-company stack (TrustGate) versus extending a mature API-management platform to AI (Kong).
Frequently Asked Questions
1. What is the difference between NeuralTrust TrustGate and Kong AI Gateway?
TrustGate is a security-first, purpose-built AI gateway whose Security Engine (TrustGuard) attaches to every Route and which covers LLM, MCP, and A2A traffic with inline security findings. Kong AI Gateway is a set of AI plugins on Kong's mature API gateway, extending an established API-management platform to AI traffic, with security delivered through plugins that largely wrap external services.
2. Does TrustGate support on-prem or air-gapped deployment?
NeuralTrust positions managed and hybrid deployment, including VPC and air-gapped, at its enterprise tier, with an open-source self-hosted core.
3. Does TrustGate ship pre-built MCP servers?
No. NeuralTrust's documentation states TrustGate is not an MCP connectivity platform and does not ship a library of pre-built MCP integrations; it governs the servers the customer registers.
4. Which AI gateway is better for enterprise AI?
Neither is universally better. Security-led buyers wanting a first-party enforcement engine and multi-turn analysis tend toward TrustGate; organizations already standardized on Kong that want one platform for API and AI traffic tend toward Kong AI Gateway.
5. Does either gateway do AI posture management (AI-SPM)?
Not as a gateway feature. NeuralTrust offers a separate posture product (TrustLens) that covers AI outside the gateway's path; Kong provides choke-point visibility of traffic that routes through it rather than a distinct posture layer.
6. Is Kong AI Gateway open source?
Kong Gateway itself is open source (Apache 2.0), and the AI Proxy plus base logging are open source. However, the AI security plugins (Prompt Guard, Semantic Prompt Guard, PII Sanitizer, third-party guardrail integrations) and audit logging are Kong Enterprise or Konnect features.
7. How does Kong AI Gateway handle runtime security?
Through plugins: AI Prompt Guard (regex allow/deny), AI Semantic Prompt Guard and Response Guard (topic-level filtering by meaning), AI PII Sanitizer (via an external anonymizer service), and integrations with AWS Guardrails, Google Model Armor, Lakera Guard, and Mistral moderation. Most of these are Enterprise features, and the deepest checks wrap external services rather than a first-party detection engine.
8. Does Kong AI Gateway support MCP?
Yes. Kong can generate MCP tools and servers from Kong-managed APIs, proxy existing MCP servers, aggregate MCP tools, enforce auth for MCP access, and offers an MCP Registry in Konnect (announced February 2026) to register, discover, and govern MCP servers.
9. Does Kong AI Gateway govern agent-to-agent (A2A) traffic?
Yes. Kong observes A2A traffic with A2A-specific metrics (payloads, latency, token usage, errors), enforces centralized authentication and authorization, and audits every A2A call with caller identity and capabilities invoked. TrustGate also covers A2A alongside LLM and MCP.
10. Which has better observability?
Both are strong. Kong provides L7 AI observability with token/cost tracking, metrics, tracing, and audit channels that integrate into existing SOC stacks. TrustGate emphasizes a single unified multi-protocol trace tree with cost, latency, and security findings attributed inline.
11. Do I need existing Kong experience to use Kong AI Gateway?
It helps significantly. Kong AI Gateway is built on Kong Gateway's plugin model, and governance quality depends on configuring, ordering, testing, and lifecycle-managing plugins. Teams already running Kong adopt it with low friction; teams new to Kong should plan for ramp-up.
12. Which is more cost-effective?
It depends on your starting point and requirements. Kong Gateway and the AI Proxy are free open source, but the AI security plugins and audit require Enterprise/Konnect. TrustGate offers an open-source core with a commercial enterprise tier. In both cases, budget for the operational and infrastructure cost of running the gateway, not just licenses.
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, contributing to the development of secure and trustworthy AI.
NeuralTrust is an AI agent security platform, recognized in the Gartner 2025 Market Guide for Guardian Agents. Headquartered in Barcelona with ISO 27001 certification.
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