MCP Gateway vs. Inference Hooks: Two Deployment Options

Two deployment options exist today for inspecting AI data in motion before it leaves your business: an MCP Gateway, and Inference Hooks. Both are inline, in the sense that a request waits for a verdict before it proceeds. Neither is a substitute for the other, because they sit in different places and see different traffic. This piece breaks down how each one actually works, the real pros and cons of both, and why most enterprises running AI agents end up deploying both rather than picking one.

The short version

• An MCP Gateway sits directly in the agent-to-tool request path at the protocol level. It is model-agnostic: any agent, any MCP server, any LLM, and it can allow, redact, hold for approval, or block a request.
• Inference Hooks is a callback built into one model vendor’s own inference pipeline, currently Claude Enterprise. It inspects prompts and tool results before they reach that specific model and returns an allow or deny verdict.
• Both are inline and blocking. The difference is where each one sits and what it can see, not which one is “really” inline.
• An MCP Gateway’s biggest gap is traffic that never goes over MCP. Inference Hooks’ biggest gap is that it only exists for the vendors who have built it, and it cannot redact, only allow or deny.
• Most enterprises need both: Inference Hooks to gate what reaches a specific model directly, and an MCP Gateway to govern what any agent, on any model, does with a tool afterward.

What is an MCP Gateway, and where does it sit?

An MCP Gateway is the control point that sits between your AI agents and every tool, database, or model they call over the Model Context Protocol (MCP). It reads the content of each request as it happens, not just the metadata, and decides in real time whether to allow it, redact the sensitive part, hold it for a person to review, or block it. Because it operates at the protocol level, it does the same job regardless of which agent is calling or which model is behind that agent. Claude, Cursor, ChatGPT, or a custom agent framework all look the same to the gateway: a request carrying content that either should or shouldn’t be allowed through. See What Is an MCP Gateway? for the full definition, and Metomic’s MCP Gateway for how Metomic implements it.

What are Inference Hooks, and where do they sit?

Inference Hooks is a feature Anthropic built for Claude Enterprise. It routes every governed prompt across Claude chat, Claude Code, and Claude Cowork to an organization’s own AI security server, which verifies a signed webhook call and returns an allow or deny verdict before inference runs, inside a configured timeout (five seconds by default). Metomic operates that server, so a customer’s security team writes policy rather than standing up infrastructure. Because the hook is built into Anthropic’s own inference pipeline, it covers traffic that never touches a tool call at all, including a prompt typed straight into Claude chat. See Metomic for Claude Inference Hooks and Anthropic’s own documentation on Inference Hooks for the underlying spec.

Are Inference Hooks actually “not inline”?

It’s worth being precise here, because the two get described loosely. Inference Hooks is blocking: Anthropic’s model waits on a verdict from the security server before inference proceeds, the same way an MCP Gateway waits before letting a tool call through. Neither one is inline in the sense of physically sitting in a network path the way a proxy does. The real difference is architectural: an MCP Gateway is a protocol-level control point that works the same way no matter which model or agent is involved. Inference Hooks is a callback wired into one vendor’s own product, so it only exists, and only sees traffic, where that vendor chose to build it.

MCP Gateway: pros and limits

Where it wins:

• Inline in the protocol-level request path, so it inspects traffic from multiple agents and multiple LLMs independently of any one model vendor.
• Sees the full tool call, arguments and results, not just prompt text, which is where sensitive data most often actually moves.
• Discovers and governs Shadow AI: the MCP servers and agents nobody approved, not only the ones on an official list.
• Decision options go beyond allow or deny: redact the sensitive part, hold the request for a person, or block it, so most low-risk traffic goes through untouched. That’s “govern, not block” in practice.

Where it stops:

• It only covers traffic that actually goes over MCP. A direct LLM API call or an embedded copilot that never makes an MCP tool call is outside what a gateway alone can see, that traffic needs an inference hook or a similar interception point wired into the model call itself.

Inference Hooks: pros and limits

Where it wins:

• Native to the model vendor’s own enterprise product, so it covers prompts and completions before they ever reach the model, including a prompt typed straight into chat, not only a tool call.
• Nothing to stand up on the customer’s side beyond pointing a webhook at Metomic and generating a signing secret.
• Can run in shadow mode before anything is enforced: Metomic always answers Claude with allow, so nothing is blocked, while it still classifies each transcript and logs every sensitive-data detection internally. That gives you real visibility into what’s flowing before you turn on enforcement.
• Deny reasons are written in plain language and shown to the user, so a blocked prompt becomes a teachable moment instead of just friction.

Where it stops:

• It only exists for vendors who have built the capability. Today that’s Claude Enterprise. A model without an equivalent hook has no equivalent control.
• Anthropic’s verdict schema is allow or deny only, once you do move to enforcing. There is no redaction option on this path today, so a prompt carrying one sensitive field alongside a legitimate request gets the same binary treatment as one that’s entirely sensitive.
• It doesn’t see what happens after a model decides to call a tool over MCP. That handoff is exactly what an MCP Gateway is built to govern.

Side by side

	MCP Gateway	Inference Hooks
What it governs	Every call any agent makes to any tool, on any model	Prompts and tool results reaching Claude Enterprise
Model coverage	Any model, any MCP client, any agent	Claude Enterprise only: chat, Code, Cowork
Decision	Allow, redact, hold for approval, or block	Allow or deny
Visibility before enforcing	See live traffic and findings before any policy is turned on	Shadow mode: always answer allow, log detections internally
Where the check runs	Metomic’s hosted gateway, in the agent-to-tool path	Your AI security server, called by Anthropic’s webhook
Shadow AI outside the covered model	Discovered and governed	Not visible
Infrastructure to stand up	None, hosted and preconfigured	None, Metomic operates the webhook endpoint for you

Do you need both?

If AI agents in your business call tools over MCP, and your organization also uses Claude Enterprise directly for chat or coding, yes. The two aren’t competing for the same job. Inference Hooks gates what reaches Claude directly, including prompts that never touch a tool. An MCP Gateway governs what any agent, on any model, sanctioned or not, does with a tool once it’s connected. Metomic runs both from one platform, so the two controls share one policy engine and one audit trail instead of two consoles that never talk to each other. See Metomic’s platform for how the two deployment options fit together.

Key takeaways

• An MCP Gateway is a protocol-level, model-agnostic control point in the agent-to-tool path. Inference Hooks is a callback built into one model vendor’s own inference pipeline.
• Both are inline and blocking. The real difference is where each sits and what traffic it can see, not which one is “more” inline.
• An MCP Gateway’s gap is traffic that bypasses MCP entirely. Inference Hooks’ gap is vendor coverage (Claude Enterprise only today) and a binary allow-or-deny decision with no redaction.
• Most enterprises running AI agents need both, covering different traffic, sharing one policy and one audit trail.

If you want to see what either control point looks like on your own traffic, book a demo of Metomic.