LLM Gateway vs. AI Gateway vs. MCP Gateway: What's the Difference?
What’s the difference between an LLM gateway, an AI gateway, and an MCP gateway?
The short answer:
- An LLM 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, with authentication, access control, and auditing on tool use.
- An AI gateway is the umbrella term, and increasingly means both: a single control plane governing all AI traffic, model calls and tool calls alike.
The terms get used loosely, and vendors define them to match whatever they sell. This post gives you working definitions, a mental model for how the pieces fit, and a checklist for what an enterprise actually needs. For a full vendor comparison, see our 2026 enterprise AI gateway guide.
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.
- Routing and failover. Policy-driven selection across providers and models, including 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.
What is an MCP gateway?
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 IdP, 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?”
So what is an AI gateway?
“AI gateway” started as a synonym for LLM gateway. In 2026 the useful definition is broader: the control plane for all AI traffic in the enterprise, covering both model calls and tool calls, with shared identity, policy, observability, and cost governance across both.
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? | ✅ | ❌ | ✅ |
| Which agents can touch which internal systems? | ❌ | ✅ | ✅ |
| Reconstruct a misbehaving multi-agent chain end to end | Partial | Partial | ✅ |
| One identity and policy model across all AI activity | ❌ | ❌ | ✅ |
| Kill switch for a compromised agent (models and tools) | Partial | Partial | ✅ |
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 (control plane) is what coordinates multiple gateways. Real enterprises don’t run one gateway. They run gateways per region, per cloud, per environment. The control plane is where catalogs, budgets, access policies, and telemetry are managed once and enforced everywhere. As a concrete example of the layering: Envoy AI Gateway is the open-source, CNCF-backed gateway (the data plane), and Tetrate Agent Router 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 VPC or on-prem.
What does an enterprise actually need?
A practical checklist. You need a unified AI gateway (LLM + MCP, one control plane) if several 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, not just 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
If only the first one or two are true, an LLM gateway may be enough to start. Choose one that can grow into the unified model rather than forcing a second migration later. For the onboarding pattern that eliminates key sprawl, see identity-based developer access.
Tetrate Agent Router Enterprise provides continuous runtime governance for GenAI systems. Enforce policies, control costs, and maintain compliance at the infrastructure layer — without touching application code.
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.
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.
Tetrate Agent Router Enterprise unifies the LLM gateway, MCP gateway, and guardrails under one control plane, built on the CNCF-backed Envoy AI Gateway that Tetrate co-created with Bloomberg. Book a demo to see the full model.
Tetrate Agent Router Enterprise provides continuous runtime governance for GenAI systems. Enforce policies, control costs, and maintain compliance at the infrastructure layer.
