MCP Gateway vs. LLM Gateway: The Two Control Points Every Enterprise AI Deployment Needs

An MCP gateway controls what AI agents can access in your systems (tools, data, APIs). An LLM gateway controls what leaves your agents for the model (prompts, context, and the data inside them). They are the two bookends of any safe enterprise AI deployment, they solve different problems, and policy has to span both. You need both, and most organizations have only started on the first.

As enterprises connect agents to real systems, two different things get called “the gateway.” The confusion matters because buying or building one does not cover the other. This post defines each, compares them side by side, and explains why the interesting policies are the ones that cross the boundary between them.

What is an MCP gateway?

An MCP gateway sits between agents and your data. It is the control point for everything an agent reaches into: databases, SaaS APIs, internal services, file stores, and the tools that wrap them.

The Model Context Protocol (MCP) is a simple protocol. An MCP server describes, in natural language, a set of tools and resources a model can use, and gives the model a way to call them. That makes MCP a selectively permeable membrane between agentic systems that operate in natural language and existing systems that operate procedurally. Data crosses it in both directions under control.

An MCP server by itself, though, is just an endpoint. The gateway is what makes a fleet of them governable:

• Authentication and authorization. Your data lives in dozens of systems with different auth subsystems. The gateway integrates agent identity with those systems so an agent can reach a resource only with the authority of the user or service it acts for.
• Tool scoping. Each agent sees only the tools and resources it is chartered to use, not the full catalog.
• Isolation. MCP servers run in their own containers with scoped permissions and a managed network boundary, so a compromised or buggy server cannot reach beyond what it was given.
• Observability and audit. Every tool call is attributed to an agent instance and a principal, and logged.
• Data filtering in both directions. Sensitive data that should never reach an agent is kept out; agents are kept from touching resources outside their charter.

The MCP gateway answers the question: what did this agent touch, and was it allowed to?

What it is not: it does not see or control what the agent sends to the model. Once data has crossed the membrane into the agent’s context, the MCP gateway’s job is done. That is the second gateway’s problem.

What is an LLM gateway?

An LLM gateway sits between the agent and the model. It is the control point for everything that leaves the agent as a prompt and comes back as a completion.

It helps to separate the model from the agent around it. The model is the inference stack: GPUs, weights, text in, text out. The agent is everything left of the LLM: the loop that assembles a system prompt, loads skills and memory, pulls in tool results, and packages all of it into context. By the time a request reaches the model, it may contain customer records, source code, internal documents, and the output of a dozen tool calls. The LLM gateway is where you decide whether that payload is allowed to go where it is headed.

An LLM gateway gives you:

• Attribution. Every prompt and completion is tied to an agent instance, a session, a user, and ideally a feature or task. This is the data you need to answer what left your network and what it cost.
• Routing and data-residency policy. Rules about which models a request may reach, based on what is in it. A session that has touched regulated data may be restricted to a model running inside your VPC, or on an inference endpoint you operate, and barred from any public API.
• Prompt-injection defense. Tool results are untrusted input. The gateway is the place to inspect and flag content that tries to redirect the agent before it reaches the model.
• Cost control and token economics. Spend per team, per model, per workflow; cache-hit rates; the pricing thresholds that double your bill when a context crosses them. Token spend is now an operational metric alongside latency and error rate, and the gateway is where it is measured.
• Provider independence. Which model answers, from which provider, is configuration at the gateway rather than code in every agent. Swapping a model for a cheaper or more capable one does not require touching the agents.

The LLM gateway answers the question: what did this agent send out, to which model, and was that allowed?

MCP gateway vs. LLM gateway at a glance

	MCP gateway	LLM gateway
Sits between	Agent and your data, tools, and APIs	Agent and the model
Direction of concern	What comes in to the agent	What goes out of the agent
Core question	What did this agent touch, and was it allowed to?	What did this agent send out, to which model, and was that allowed?
Protocol	Model Context Protocol (MCP)	Model provider APIs (OpenAI-compatible, Anthropic, Bedrock, self-hosted inference)
Identity role	Authorizes tool and resource access against the agent’s delegated identity	Attributes prompts and completions to the agent instance and user
Primary risks addressed	Over-reach into systems, unauthorized data access, compromised MCP servers	Data exfiltration to external models, prompt injection, runaway spend
Policy examples	Agent X may call tools A and B but not C; MCP server runs with read-only scope	Sessions that touched tool C may only route to in-VPC models; cap this team at N tokens/day
Observability output	Tool-call audit log per agent instance	Token and prompt attribution per session, model, and feature
Stacklok project	ToolHive	Part of the Stacklok platform; the Mecatl harness routes through it

Why you need both, and why policy has to span them

Each gateway on its own closes one door. The interesting policies are the ones that need both doors to share a view of the session.

Consider the rule every regulated organization eventually wants: if an agent has accessed this class of data, that data must never leave our network. Enforcing it requires two facts. The MCP gateway knows which tools and data the session touched. The LLM gateway knows which model the session is about to call and where that model runs. Only when the two share session context can the platform say: this session read from the customer database, so it may only route to the model running on Bedrock inside our VPC, never to a public endpoint.

The same logic applies in the other direction. A prompt-injection attempt arrives through a tool result at the MCP boundary; the LLM gateway is where it would otherwise reach the model. Catching it needs both sides to agree on what counts as untrusted content.

And it applies to cost. Token attribution at the LLM gateway is only actionable when it can be joined to what the agent was doing, which is what the MCP gateway’s tool-call log tells you. That join is how you get from “this team spent a lot” to “this workflow uses an expensive model for a step a cheaper one handles fine.”

This is why we describe the two gateways as bookends of one system rather than two products. Buying an MCP gateway from one vendor and an LLM gateway from another, with no shared identity or session model, gets you two audit logs and no cross-boundary policy. The value is in the seam.

Where the gateways should run: inside your perimeter

Both gateways handle your most sensitive traffic: the data agents pull in and the prompts agents send out. Where they run is not a deployment detail. It is the security decision.

Self-hosted, not SaaS. The endpoints agents reach may be a SQL database in your data center, a SaaS service, or something in your cloud. The gateways themselves need to live inside your network perimeter, whether that is on-premises or your own VPC. No bank we have spoken with over the past few years is willing to hairpin critical data access through a SaaS endpoint owned by a startup. Serious organizations want the control plane inside their safety domain, integrated with their identity systems, operated on their terms. Stacklok does not build SaaS control planes for exactly this reason.

Kubernetes-native, not Kubernetes-compatible. Gateways that run as ordinary Kubernetes workloads inherit the cluster’s isolation, network policy, workload identity, scaling, and lifecycle management. ToolHive runs MCP servers as containers with scoped permissions and a managed boundary, and ships a Kubernetes operator so the MCP gateway is managed like everything else your platform team operates. The Mecatl harness runs the agent loop as a Kubernetes workload and routes model calls through the LLM gateway, so session identity flows across both bookends.

Open source, so you can inspect the membrane. A gateway is a trust boundary. You should be able to read the code that enforces it, patch it on your own schedule, and benefit from every other organization hardening the same critical path.

One honest caveat: this is built for large, regulated organizations with complex environments. A smaller company without those constraints may be well served by a SaaS gateway. We focus on the hard end of the problem because that is where we have spent our careers.

Frequently asked questions

What is the difference between an MCP gateway and an LLM gateway? An MCP gateway controls what an AI agent can access in your systems: tools, data, and APIs reached over the Model Context Protocol. An LLM gateway controls what the agent sends to the model: prompts, context, and the data inside them, plus which model is allowed to receive them. One governs inbound access, the other governs outbound exposure.

Do I need both an MCP gateway and an LLM gateway? Yes, for any production deployment that touches sensitive data. The MCP gateway cannot see what leaves for the model; the LLM gateway cannot see what the agent touched. Policies like “data from this system must never reach an external model” require both gateways to share session context.

Is an LLM gateway the same as an AI gateway or an API gateway? An LLM gateway is a specialized AI gateway focused on the agent-to-model boundary: attribution, routing, data-residency policy, prompt-injection inspection, and token cost control. A generic API gateway handles HTTP routing and rate limiting but has no understanding of prompts, context, or model semantics.

What does an MCP gateway do that an MCP server doesn’t? An MCP server exposes tools. An MCP gateway governs a fleet of them: identity integration and authorization, per-agent tool scoping, container isolation with a managed network boundary, audit logging of every call, and policy enforcement. ToolHive is an open source MCP gateway and platform.

Can an LLM gateway prevent data leakage to public AI models? Yes, if it has session context. By knowing which data a session accessed (from the MCP gateway) and which model it is about to call, the LLM gateway can enforce rules that confine sensitive sessions to models running inside your VPC or on your own inference infrastructure.

Should an enterprise run its AI gateways as SaaS? Most regulated organizations will not route agent data access or prompts through a third-party SaaS endpoint. Both gateways should run inside your network perimeter or VPC, integrated with your identity systems. Smaller organizations without compliance constraints may find SaaS adequate.

How do AI gateways help control LLM costs? The LLM gateway attributes every token to a session, user, model, and workflow, measures cache-hit rates, and can route requests to cheaper models of equal capability. Joined with the MCP gateway’s tool-call log, that attribution shows which workflows are overspending and why.