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NeuralTrust TrustGate is an open-source AI gateway, built by a security company, that runs wherever you need it and secures LLM and MCP traffic across providers. Google Agent Gateway is the networking component of the Gemini Enterprise Agent Platform: a managed, proprietary service that governs agent connectivity inside Google Cloud. Both sit in front of agents and their tools, but they start from opposite places, one open and portable, the other bound to a single cloud.
That starting point shapes everything that follows: where the gateway can run, whether you can read its source, how many MCP tools connect without manual work, and whether its security understands a full conversation or just one request at a time.
TL;DR
- NeuralTrust is open source and runs anywhere. Google Agent Gateway is a proprietary managed service that only operates inside Google Cloud, with no self-hosting or on-premises option.
- NeuralTrust ships an integrated catalog of more than 200 MCP servers, connectable in one click. Google's Agent Registry governs access to servers you register yourself, with no ready catalog.
- NeuralTrust secures whole sessions and defends against MCP tool poisoning. Google's Model Armor inspects one request at a time and does not analyze a tool's own definition for planted instructions.
- NeuralTrust exports natively to SIEMs (Sentinel, Splunk, QRadar, Datadog, Elastic). Google keeps its telemetry inside Cloud Logging and Cloud Trace.
Comparison at a Glance
| Capability | NeuralTrust | Google Agent Gateway |
|---|---|---|
| Open-source license | ✅ | ❌ |
| Flexible deployment (private, cloud) | ✅ | ❌ |
| MCP catalog integrated | ✅ more than 200 | ❌ |
| Multi-turn session security | ✅ | ❌ |
| MCP tool poisoning defense | ✅ | ❌ |
| Native SIEM integration | ✅ | ❌ |
| Enterprise readiness | ✅ | ❌ |
Platform Overview
What is NeuralTrust TrustGate?
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TrustGate is NeuralTrust's AI gateway, built by a security company. It sits between agents and the services they call (LLM providers and MCP servers) and becomes the single place where routing, policy, security, and observability attach. 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.
It is open source and runs wherever you put it, in your own private infrastructure or in the cloud, with no dependency on a single provider. Security is the organizing principle: a Security Engine attaches to every route and inspects each request inline, enforcing an allow, block, or transform decision before the request reaches its target, and it maintains session memory so it reasons about a whole conversation rather than one message at a time. TrustGate also ships an integrated catalog of more than 200 third-party MCP servers and exports its events natively to the SIEMs a security team already runs.
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What is Google Agent Gateway?
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Google Agent Gateway is the networking layer of the Gemini Enterprise Agent Platform, the platform formerly known as Vertex AI Agent Builder. It acts as the entry and exit point for agent traffic running on Google Cloud, mediating protocols, terminating mTLS, and delegating authorization to Google Cloud services. It is strong on identity: agent identities are secured with mTLS and DPoP, and access decisions run through Identity-Aware Proxy and IAM.
It is also a proprietary, fully managed service that lives entirely inside Google Cloud. There is no open-source core to read or extend, and no way to run it in your own infrastructure or another cloud; each gateway is regional and scoped to a Google Cloud project. Its security is delivered by Model Armor, a separate Google Cloud service integrated as an optional layer, and its telemetry flows into Cloud Logging and Cloud Trace. The result is a capable gateway for organizations that are all-in on Google Cloud, and a closed one for everyone else.
Open and Portable vs Locked to One Cloud
Where a gateway can run, and whether you can see how it works, decides how much control you keep over your own AI infrastructure.
TrustGate is open source and deploys wherever you need it: in private infrastructure, in the cloud, or across both. You can read the source, run it in your own environment, and avoid being tied to any single provider. The gateway that governs your AI traffic is infrastructure you control, not a service you rent from one vendor.
Google Agent Gateway is proprietary and managed, and it only runs inside Google Cloud. There is no self-hosted or on-premises option and no multi-cloud deployment: if your agents need to run outside Google Cloud, this gateway does not go with them. Its source is closed, so its behavior is what Google documents and nothing more, and every gateway is regional and bound to a Google Cloud project. For an organization that wants portability or its own infrastructure, that is a hard boundary drawn before any feature comparison begins.
MCP Catalog Integrated vs a Registry You Fill Yourself
Agents reach their tools over the Model Context Protocol, so how quickly and cleanly those tools connect is a real measure of an AI gateway.
TrustGate ships an integrated catalog of more than 200 third-party MCP servers, the tools teams use every day, connectable in one click and governed through the gateway from the moment they are enabled. Teams do not spend engineering time wiring servers up individually, and every connected tool is under policy and observation by default because it runs through the gateway.
Google provides an Agent Registry: a central library where your agents, tools, and MCP servers must be registered before the gateway will allow traffic to them, and by default access to any unregistered server is blocked. That is sound governance, but it is not a catalog. The registry organizes what you have already connected; it does not give you a ready set of third-party servers to turn on. Every server you want is one you register and configure yourself, and each gateway instance is capped at governing 5,000 registered resources. The machinery to govern MCP traffic is there; the work of populating it stays with you.
Multi-Turn Session Security
The most dangerous attacks on an AI system rarely fit in a single message. They unfold across a conversation, one innocuous-looking turn at a time, until the full intent becomes clear only in aggregate.
TrustGate is built for this. Its Security Engine maintains session memory, so it reasons about an interaction as a whole rather than as a series of unrelated requests. An attack or abuse pattern that is deliberately spread across several messages, each harmless on its own, is exactly what session-level analysis is designed to catch, and enforcement happens inline before the request reaches the model.
Google secures traffic through Model Armor, which inspects prompts and responses one request at a time for prompt injection, jailbreaks, sensitive data, and malicious URLs. Those are real protections, but they are per-request by design: Model Armor evaluates each message in isolation and does not carry a session-level view across turns. An adversarial sequence that only becomes recognizable when the turns are read together falls outside what a single-request filter can see. Adding more per-request checks does not add the layer that is missing.
MCP Tool Poisoning Defense
An MCP tool's definition, its name, description, and parameter schema, is what an agent trusts implicitly before it ever calls the tool. Security researchers (OWASP's MCP Top 10, CyberArk, Invariant Labs) have documented that this trust is exactly what attackers exploit: hidden instructions embedded in a tool's own metadata can manipulate an agent without a single malicious request ever being sent.
TrustGate addresses this at the gateway. It can detect a tool definition that has been planted with a hidden attack, then patch or override the compromised name, description, or schema fields before that definition ever reaches the model, without touching the third-party server's own code or waiting on its release cycle. A newly onboarded MCP server with a poisoned description gets sanitized at the gateway the moment it is found, and the tool stays usable while the vendor is notified.
Google's Model Armor does not cover this surface. It scans the content of prompts and responses, but it does not inspect a tool's own definition for planted instructions, and the Agent Gateway governs which servers and tools an agent may reach rather than analyzing the trustworthiness of a tool's schema. Defending against a poisoned definition on Google's stack means bringing in a separate scanner outside the gateway, with no built-in way to patch a compromised definition in transit. Given that this class of attack is now tracked as its own category in the OWASP MCP Top 10, that gap matters.
Native SIEM Integration
An AI gateway produces exactly the events a security operations team wants in its SIEM: who called which model or tool, what was blocked, which policies fired, where costs spiked. Whether the gateway delivers those events cleanly into that tooling is what decides if it fits into how an enterprise already runs security.
TrustGate treats this as a first-class capability. It exports and streams its events natively to the SIEMs security teams already run, with confirmed support for Microsoft Sentinel, Splunk, IBM QRadar, Datadog, and Elastic, plus OpenTelemetry as the export standard and a generic webhook for anything else. The events flow into the customer's own pipeline, in standard formats, so NeuralTrust becomes one more high-quality source feeding the SOC.
Google keeps its telemetry inside its own stack: Agent Gateway observability flows into Cloud Logging and Cloud Trace. For a team whose SOC already runs on Google Cloud, that is convenient, but there is no native selection of third-party SIEM destinations. Routing Google's gateway events into Sentinel, Splunk, or QRadar means building and maintaining an export pipeline out of Cloud Logging yourself, rather than choosing the destination from the gateway. The data exists; the clean, native path into the SIEM your team already uses does not.
Final Verdict
Google Agent Gateway is a reasonable fit for organizations fully committed to Google Cloud, with strong agent identity and tight integration into the Gemini Enterprise Agent Platform. But it is a closed service on a single cloud: proprietary, not self-hostable, filling its own registry by hand, securing traffic one request at a time, blind to tool-definition poisoning, and keeping its telemetry inside Google's stack.
NeuralTrust is open, portable, and built to secure AI traffic. It runs in your own infrastructure or any cloud; it connects more than 200 MCP servers in one click; it reasons across whole sessions and detects and patches poisoned tool definitions at the gateway; and it exports natively to the SIEMs a SOC already runs. The decision is not about which cloud you happen to use. It is whether you want an AI gateway you own and can run anywhere, built to secure agentic traffic, or a managed one that only works where Google says it does.
Frequently Asked Questions
1. What is the main difference between NeuralTrust and Google Agent Gateway?
NeuralTrust TrustGate is an open-source AI gateway, built by a security company, that runs in your own infrastructure or any cloud and secures LLM and MCP traffic with session-aware enforcement. Google Agent Gateway is a proprietary, managed networking service inside the Gemini Enterprise Agent Platform that only runs on Google Cloud and secures traffic through the separate Model Armor service on a per-request basis.
2. Can Google Agent Gateway run outside Google Cloud?
No. Google Agent Gateway is a managed service that operates only within Google Cloud, with no self-hosting, on-premises, or multi-cloud option, and each gateway is regional and scoped to a Google Cloud project. NeuralTrust is open source and deploys in private infrastructure or any cloud.
3. How does NeuralTrust's MCP catalog differ from Google's Agent Registry?
NeuralTrust ships an integrated catalog of more than 200 third-party MCP servers that connect in one click and are governed immediately through the gateway. Google's Agent Registry requires you to register and configure each server yourself before the gateway allows access to it, and caps each gateway at 5,000 registered resources; it organizes what you connect but provides no ready catalog.
4. Does Google's Model Armor protect against multi-turn attacks and MCP tool poisoning?
Model Armor inspects prompts and responses one request at a time for prompt injection, jailbreaks, sensitive data, and malicious URLs. It does not maintain a session-level view across turns, so attacks spread across a conversation fall outside it, and it does not inspect a tool's own definition for planted instructions, so tool poisoning is not covered. NeuralTrust reasons across whole sessions and detects and patches poisoned tool definitions at the gateway.
5. Which should I choose for enterprise agentic AI?
If your organization runs entirely on Google Cloud and wants tight platform integration and strong agent identity, Google Agent Gateway fits that setup. If you want an open, portable gateway you can run anywhere, with a one-click MCP catalog, session-aware security, defense against poisoned tool definitions, and native SIEM export, NeuralTrust TrustGate is built for that.
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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, contributing to the development of secure and trustworthy AI.
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.
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