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EnglishTrueFoundry AI Gateway vs MCP Gateway: What's the Difference?
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TrueFoundry's AI Gateway and MCP Gateway govern two different layers of an AI stack. The AI Gateway sits in front of LLM and model calls, handling routing, rate limiting, and observability across providers. The MCP Gateway sits in front of Model Context Protocol connections, governing which tools, data sources, and external services an agent can reach. If your concern is controlling and monitoring model traffic, you want the AI Gateway. If your concern is controlling what an agent can connect to and act on, you want the MCP Gateway. Most enterprise agentic deployments end up using both together.
What the AI Gateway covers
The AI Gateway is the control plane for LLM traffic. Based on TrueFoundry's product description, it is positioned as a single gateway for an entire AI stack, covering LLMs, MCPs, and agents, with rate limiting, observability, and compliance built in.
In practice, the AI Gateway is where you would expect to handle:
- Routing across models and providers — directing requests to the right LLM endpoint.
- Rate limiting — capping request volume so one workload or team cannot exhaust shared capacity.
- Observability — seeing every AI interaction that passes through, which is the basis for cost tracking, latency analysis, and debugging.
- Compliance controls — applying policy at the point where model calls enter and leave your environment.
The gateway is described as deployable in VPC, on-prem, air-gapped, or across multiple clouds. That matters if your model traffic cannot leave a controlled network boundary.
What the MCP Gateway covers
The MCP Gateway governs Model Context Protocol connections. MCP is the interface through which agents reach tools, data, and external services. Where the AI Gateway governs model calls, the MCP Gateway governs the connections that give an agent its capabilities.
The practical questions the MCP Gateway answers are:
- Which MCP servers or tools is an agent allowed to connect to?
- What does the agent actually do when it reaches them?
- How is that access secured and observed?
This is the layer that becomes critical once agents move beyond answering questions and start taking actions — querying internal databases, calling internal APIs, or triggering workflows.
How they work together in agentic AI
TrueFoundry describes orchestrating agentic AI with the AI Gateway, and lists both gateways as separate products alongside an Agent Gateway, Prompt Management, and an Agent Skills Registry. That product structure reflects the split: model traffic and tool/context traffic are governed separately but composed in the same stack.
A concrete example makes the division clear. Suppose an agent handles customer support tickets. It calls an LLM to draft a reply and calls an MCP server to look up the customer's order history.
- The LLM call — routing to the chosen model, rate limiting under peak ticket volume, logging token usage and latency — is the AI Gateway's job.
- The order-history lookup — deciding whether this agent may reach that data source at all, and recording what it retrieved — is the MCP Gateway's job.
If you only deploy the AI Gateway, you see and control the model calls but not the tool access. If you only deploy the MCP Gateway, you control tool access but not model routing, rate limits, or model-level observability.
Choosing between them
| Dimension | AI Gateway | MCP Gateway |
|---|---|---|
| Governs | LLM and model calls | MCP connections to tools, data, and services |
| Primary controls | Routing, rate limiting, observability, compliance | Access governance and security for agent connections |
| Typical owner | Platform and ML teams managing model traffic | Security and platform teams managing agent permissions |
| Choose it when | You need to control and observe model usage across providers | You need to control what agents can connect to and act on |
| Deploy together when | You run agents that both call models and reach external tools | Same scenario, viewed from the tool-access side |
The decision rule is simple: look at what you need to control. Model traffic points to the AI Gateway. Agent-to-tool access points to the MCP Gateway. Agentic workloads that do both need both, which is why TrueFoundry presents them as parts of one gateway layer rather than alternatives.
What to verify before deciding
TrueFoundry's site lists pricing as a separate page but the provided material does not include plan details, so check current pricing and deployment terms directly. Two things are worth confirming for your own environment:
- Deployment fit — the gateway is described as supporting VPC, on-prem, air-gapped, and multi-cloud, so confirm which applies to your setup.
- Scope of governance — confirm which MCP servers and tools the MCP Gateway can govern in your architecture, and which model providers the AI Gateway routes across.
TrueFoundry also publishes direct comparisons against Portkey, Kong, LiteLLM, Solo.io, and SageMaker, which are useful if you are evaluating the AI Gateway against alternatives rather than deciding between the two gateways themselves.