LLM Gateway vs. AI Gateway vs. MCP Gateway: What's the Difference?
TL;DR:
- An LLM gateway (a large language model gateway, also called an inference gateway) is a control point between your applications and model providers. It centralizes authentication, routing, failover, rate limiting, and token/cost tracking for model calls.
- An MCP gateway does the same job for tool calls. It governs which Model Context Protocol servers and tools your agents can reach. Authentication, access control, and auditing apply to every tool call.
- An AI gateway is the umbrella term, and increasingly means both: a single control point governing all AI traffic. Model calls and tool calls fall under it alike.
- An API gateway is a different, older layer. It governs generic HTTP traffic between applications, and it sits alongside all three gateways. See “Where does an API gateway fit?” below.
What’s the difference between an LLM gateway, an AI gateway, and an MCP gateway?
Vendors still define these terms to match whatever they sell, which is why the same two-way comparison (AI gateway vs. MCP gateway) now gets synthesized into a Google AI Overview citing half a dozen different vendor glossaries, each with a slightly different line between the terms. The confusion is not surprising: Gartner forecasts AI gateway spending will grow 70.9% in 2027, from $251 million to $429 million (Gartner, August 2026), and a market growing that fast attracts a vendor on every side of the definition.
This post gives you working definitions, a mental model for how the pieces fit, and a checklist for what an enterprise actually needs. For canonical definitions, see the AI Gateway Glossary. For a full vendor comparison, see our 2026 enterprise AI gateway guide. For how enterprises control AI spend at the gateway, see Your AI bill is an AI gateway problem.
What is an LLM gateway (inference gateway)?
An LLM gateway sits between every application or agent and every model provider. Instead of each team wiring its code directly to OpenAI, Anthropic, Bedrock, or Vertex with its own keys, retries, and logging, all model traffic flows through one governed path.
Core functions:
- Unified API. One OpenAI-compatible endpoint for every provider and model, so swapping models is a configuration change, not a code change. Stable model names let you repoint a model without a redeploy.
- Routing and failover. Policy-driven selection across providers and models. Agent Router Enterprise does this with smart routing, where policy runs first and removes disallowed models. This includes automatic multi-provider failover when a provider has an outage or silently updates a model.
- Rate limiting and budgets. Token budgets and request limits per team, project, or agent, enforced inline.
- Cost attribution. Token and cost telemetry by team, project, and agent: the raw material for showback and chargeback.
- Credential management. Provider keys live in the gateway, not scattered across repos and laptops.
If you’ve heard “inference gateway,” it’s the same concept with emphasis on the model-call path. For the full architecture breakdown and when organizations adopt one on its own, see What Is an LLM Gateway? and What Is an Inference Gateway?.
What is an MCP gateway?
In one sentence: an MCP gateway is the control point that governs which tools and data sources your AI agents are allowed to call, the same way an LLM gateway governs which models they can call.
The Model Context Protocol (MCP) standardized how agents call tools: databases, internal APIs, SaaS systems, file stores. That solved integration and created a governance problem, because an agent with tool access is an authenticated actor touching real systems.
An MCP gateway is the control point for that tool traffic:
- Curated tool catalogs. Centrally manage which MCP servers and tools each team, agent, or user group may access.
- Authentication and identity. Tool calls carry authenticated user and team context via your identity provider, rather than shared credentials baked into agents.
- Policy and auditing. Allow, deny, and log tool invocations, so when an agent misbehaves you can see exactly which tools it touched and cut access in one action.
Without an MCP gateway, every team connects agents to tools its own way, and nobody can answer “which agents can touch the customer database?” For the full breakdown of MCP gateway architecture, tool catalogs, and use cases, see the MCP overview and the MCP Gateway product page.
So what is an AI gateway?
In one sentence: an AI gateway governs all of an enterprise’s AI traffic. It covers model calls and tool calls together. Identity, policy, and audit apply to both under one model.
“AI gateway” started as a synonym for LLM gateway. In 2026 the useful definition is broader: one governing layer for all AI traffic in the enterprise. It covers model calls and tool calls together. Identity, policy, observability, and cost governance apply across both. Tetrate’s AI gateway is built to this definition.
The reason the definition expanded is that agents expanded. A production agent’s risk and cost surface is no longer just its prompts. It’s the chain of model calls and tool calls together. Governing one without the other leaves half the problem unsolved:
| Concern | LLM gateway alone | MCP gateway alone | Unified AI gateway |
|---|---|---|---|
| Who is using which models, and what does it cost? | Yes | No | Yes |
| Which agents can touch which internal systems? | No | Yes | Yes |
| Reconstruct a misbehaving multi-agent chain end to end | Partial | Partial | Yes |
| One identity and policy model across all AI activity | No | No | Yes |
| Kill switch for a compromised agent (models and tools) | Partial | Partial | Yes |
For the dedicated breakdown of what counts as an AI gateway and how to evaluate one, see What Is an AI Gateway? and the AI gateway product page.
Key differences at a glance
The table below puts all three definitions side by side. Most vendor content compares only two of the three terms at a time (AI gateway vs. MCP gateway, or AI gateway vs. API gateway). This extends the comparison to all three AI-specific gateway types, since the terms get used inconsistently across vendor content and a reader evaluating one typically needs to place all three.
| Dimension | LLM gateway | AI gateway | MCP gateway |
|---|---|---|---|
| What it governs | Model calls to LLM providers | Model calls and tool calls together, under one policy | Tool calls agents make through MCP servers |
| Primary unit tracked | Tokens, requests, latency, provider | Tokens, requests, tool invocations, cost, identity | Tool invocations, sessions, scopes, authenticated identity |
| Core risk it addresses | Provider outage, runaway token spend, credential sprawl | Fragmented policy between model traffic and tool traffic | Unaudited or unauthorized tool access by an agent |
| Typical deployment | Standalone proxy in front of model providers | Single control point in front of both models and tools | Standalone proxy, or a module inside a unified AI gateway |
| Example scope | One OpenAI-compatible endpoint for every model and provider | LLM gateway, MCP gateway, and guardrails under one management plane | A curated MCP server catalog with per-profile authentication |
Where does an API gateway fit?
An API gateway (Kong, Apigee, AWS API Gateway, and similar) predates AI traffic entirely. It governs generic HTTP APIs: authentication, rate limiting, routing, and traffic shaping for any backend service.
The distinction that matters: an API gateway has no concept of a token, a model, a prompt, or a tool call. It cannot enforce a per-team token budget, fail a request over to a different model provider, or distinguish a tool invocation from any other API call. Some API gateway vendors have added AI plugins on top of their existing product, but the underlying governance model stays API-centric (paths, methods, rate limits) rather than token-and-tool-centric (models, prompts, budgets, tool scopes).
An API gateway and an AI gateway each govern a different slice of enterprise traffic. Many enterprises run both: the API gateway in front of general application traffic, the AI gateway in front of AI-specific traffic, each enforcing the policy it is built for. For the full comparison, see AI Gateway vs. API Gateway: Key Differences Explained.
Where do guardrails and the management layer fit?
Two more terms complete the picture:
- Guardrails are policies enforced in the request path: PII detection and redaction, prompt-injection blocking, banned-topic filtering, transaction blocking. A gateway is where guardrails become enforceable rather than aspirational, because every request passes through it. Standalone guardrail products exist. A good gateway lets you bring your own and enforces them inline. In regulated industries, see how this maps to HIPAA-compliant AI deployments.
- The management layer is what coordinates multiple gateways. Real enterprises don’t run one gateway. They run gateways per region, per cloud, per environment. The management plane is where catalogs, budgets, access policies, and telemetry live. They are managed once and enforced everywhere. As a concrete example of the layering: the Agent Router project (formerly Envoy AI Gateway) at the Agentic AI Foundation (AAIF) is one example of the gateway itself. Tetrate Agent Router Enterprise is the management layer that governs one or many of them: traffic, budget, access, and guardrail policies for different user groups, with the data planes running in your own virtual private cloud (VPC) or on-prem.
What does an enterprise actually need?
A practical, four-step framework for deciding.
Step 1: Map what’s actually running today
Count the model providers your teams already call directly, and check whether any agent already reaches internal tools, databases, or SaaS systems through MCP, even informally. Most organizations underestimate both numbers. The count lives in several teams’ heads, so no single dashboard shows it.
Step 2: Score the signals
Count how many of these are true:
- More than two or three teams are building or buying agents
- Leadership asks what AI costs per team and nobody can answer from one place
- A provider outage would page multiple unrelated on-calls
- Agents call internal tools and systems beyond models
- You’re in a regulated industry and auditors will ask how AI traffic is controlled
- Developers wait on tickets or manage their own provider keys to get model access
Step 3: Read your score
If you only need to route and pay for model calls today, an LLM gateway can be enough to start. If agents already call internal tools, systems, or data through MCP, or will soon, choose a gateway that governs both from day one. Choose a gateway that can grow into the unified model. That avoids a second migration later.
Step 4: Pick a starting point for today
A unified gateway and a standalone LLM gateway are not mutually exclusive choices made once. They are a starting point you pick based on today’s score, with a documented path to the other if the score changes. For the onboarding pattern that eliminates key sprawl, see identity-based developer access.
Which gateway do you need first?
LLM gateway, AI gateway, MCP gateway, and API gateway name four different control points. An LLM gateway governs model calls. An MCP gateway governs tool calls. An AI gateway governs both under one policy. An API gateway handles generic application traffic. The four-step framework above turns that distinction into a decision: map what is actually running, score the real signals against your organization, and pick a starting architecture that can grow into a unified model instead of forcing a second migration later.
Agent Router Enterprise
Tetrate Agent Router Enterprise routes AI agent traffic across providers and your own models, with smart routing, policy, cost controls, and audit on every request, in cloud, on-prem, or edge.
Frequently asked questions
Is an AI gateway the same as an API gateway? No. API gateways (Kong, Apigee, and similar) govern generic HTTP APIs. AI gateways are token-aware and model-aware: they understand prompts, streaming, token budgets, model versions, and provider failover semantics. Some API gateway vendors have added AI plugins, but the governance model remains API-centric rather than token-centric. See “Where does an API gateway fit?” above for the full distinction.
Is a model router like OpenRouter an AI gateway? It’s an LLM gateway in the narrow sense (unified API, routing) delivered as a hosted aggregator. It lacks the enterprise dimensions: self-hosted deployment, identity-aware policy, MCP governance, and inline guardrails.
Do I need an MCP gateway if my agents don’t use MCP yet? Soon, probably. Framework support for MCP is now broad, and tool use is where agent value concentrates. Choosing an AI gateway that already includes MCP governance avoids bolting on a second system in a year.
Where does observability fit? A real gateway produces unified metrics, traces, and logs for every AI call across providers, frameworks, and teams as a byproduct of being in the request path. Observability-only products see traffic but cannot enforce anything on it.
Is an MCP gateway the same as an MCP registry? No. An MCP registry is a passive directory that lists available MCP servers and tools. An MCP gateway is the active enforcement layer: it sits in the request path and authenticates the caller. It then allows, denies, or logs the actual tool invocation. A registry tells you what exists. A gateway controls what happens.
Is an MCP gateway the same as an MCP server? No, and this is one of the most common mix-ups in the space. An MCP server exposes a specific set of tools, such as a database connector or an internal API wrapper. An MCP gateway sits in front of many MCP servers at once. It governs which agents or users can reach which servers, with one authentication and audit layer instead of one per server.
Can one platform cover both the LLM gateway and MCP gateway, or do I need two separate tools? One platform can cover both. A unified AI gateway runs the LLM gateway and MCP gateway functions under one management plane. Model calls and tool calls then share the same identity, policy, and audit trail.
Tetrate Agent Router Enterprise unifies the LLM gateway, MCP gateway, and guardrails under one management plane. It is built on the Agent Router project (formerly Envoy AI Gateway), the open source data plane at the Agentic AI Foundation (AAIF) that runs on the Envoy proxy and that Tetrate co-created with Bloomberg. Start a fast-track evaluation: first routed request under five minutes, nothing to install. Book a demo to see the full model.
Key terms glossary
LLM gateway (inference gateway): a control point between applications and model providers. It governs authentication, routing, failover, rate limiting, and cost tracking for model calls.
AI gateway: the control point for all of an enterprise’s AI traffic, covering model calls, tool calls, identity, and policy under one shared model.
MCP gateway: the control point that governs which Model Context Protocol servers and tools an agent is allowed to call. Authentication, access control, and auditing apply to every tool call.
API gateway: a general-purpose control point for HTTP APIs (authentication, rate limiting, routing) with no concept of a token, model, prompt, or tool call.
Guardrails: policies enforced in the request path, such as PII redaction, prompt-injection blocking, and banned-topic filtering, that a gateway makes enforceable because every request passes through the gateway.
Management plane: the layer that coordinates multiple gateways across regions, clouds, and environments. Catalogs, budgets, access policies, and telemetry are managed once, then enforced everywhere.
Learn more about Tetrate Agent Router Enterprise — enterprise AI agent routing with smart routing, policy, cost controls, and audit across every gateway.
