Mia-Platform vs AI & MCP Gateways
A gateway decides if a call goes through. In agentic engineering, Mia-Platform also decides what an agent knows, what it can access and how far it may act.
Before you start. A transparent comparison, to help you choose freely. We compare categories, never products.
A note from our leadership
A fast-moving market, and a way to find your bearings.
This comparison is here to help anyone who has to choose in a market that moves fast. It's a transparent comparison, designed to help you understand which category of solutions best fits your needs. We compare categories, never individual products, and we also say where Mia-Platform fits, so you can weigh it with a critical eye.
We share our experience and the public documentation of each category, so that your choice is considered and informed, built on your own use cases.
Francesco Soncini Sessa · CFOFederico Soncini Sessa · COOGiulio Roggero · CTO
AI & MCP Gateways: three things to take away.
- Where the category shines
Deep, fast control at the call.
On the gateway alone they are often deeper: tool access, routing, limits and spend caps, quick to put in front of existing traffic and loved by developers.
- Where it stops
A gateway sees the call, not the whole picture.
On its own it has no agent registry, no shared context and no golden path to production. It can't say which agent made the call, who owns it or what it may do in production.
Where Mia-Platform fitsThe gateway as one enforcement point of a governed platform.
In agentic engineering the call is one moment among many. With Mia-Platform you decide what each agent knows, what it can access, who owns it, which AI Playbook it follows and how far it may act in each environment. Spend limits and model choice sit next to the same policy.
AI & MCP Gateways, in plain words.
What it is
AI and MCP gateways sit between agents and the models and tools they call. They route and secure traffic and increasingly speak the new standards for agent tool access and agent-to-agent communication.
What it is for
- Controlling which tools and models an agent can reach
- Routing, limiting and caching traffic across providers
- Capping spend per team or application
- Tracing and logging agent calls
Why it matters now
Agents call tools and spend tokens all day, so the call is a natural place to enforce, which is why almost every platform vendor now offers a gateway, its own or a third-party one integrated. Each built-in gateway governs traffic inside its own platform. The question that matters is what the gateway knows about the agent that makes the call.
Where Mia-Platform differs
- Enforced where AI runs. The gateway is one enforcement point of a governed platform, next to context, ownership and playbooks, not a standalone proxy.
- Cost and compliance governed together. Spend limits and model choice sit next to the agent's policy.
- Independent of every vendor. One rule set across agents, models, clouds and toolchains, including where you already run an API gateway.
- Open at the gateway layer. No proprietary proxy: Mia-Platform builds on open-source gateways and keeps the one you already run. What it adds is the governance around the call.
AI & MCP Gateways and Mia-Platform: where each approach is strong.
Read the rows that matter to your context. Where the category is as strong as we are, we say so.
| Criterion |  | Typical AI & MCP gateways |
|---|---|---|
| Agent identity and ownership | Full Every agent has an owner and scoped access. | Partial Sees the call; which agent made it and who owns it usually lives elsewhere. |
| Shared, governed context for agents | Full Dynamic context retrieval from one unified catalog, the same for every agent. | None Moves calls; it doesn't hold or govern the context agents use. |
| Golden path to production | Full AI Playbooks and golden paths to deployment, with the same guardrails for every agent. | None A proxy has no path to production of its own. |
| Autonomy by environment and separation of duties | Full Each environment sets how far an agent may go: more freedom in dev, tighter limits in production. And no agent approves its own code. | Partial Per-call rules; autonomy by environment and separation of duties sit outside the gateway. |
| Spend limits and model choice | Full Budgets per team and use case and the model set per agent sit next to the policy: cost and compliance are governed together. | Full Many cap spend and route between models, without knowing which agent spent it or who owns it. |
| Depth of the gateway itself | Partial Builds on open-source gateways instead of competing with them; the depth it adds is the governance around the call. | Full Often deeper on the gateway alone. |
| Quick to adopt on existing traffic | Full Added around the gateway you already run. | Full Quick to adopt, often open source and developer-loved. |
Based on publicly available documentation and marketing materials. It describes what the category typically offers: individual products vary, so check each one against your own requirements.
The products behind this comparison.
Every Full row in the table comes from one of these products. Govern agents before and after the gateway.
AI Foundry
Design, manage, and govern AI Playbooks to bring generative AI applications and agents to production.
IN THIS TABLEExplore AI Foundry
Context Catalog
Connect, configure, and serve context to AI agents and applications through a governed catalog of enterprise knowledge.
Explore Context Catalog
AI & MCP Gateways: questions worth asking.
Three questions that cut through the positioning, and how we answer each one. They work for any vendor, including us.
Ask any vendor
In Mia-Platform, every agent has an owner and scoped access, recorded in the agent registry. The gateway sees the call; Mia-Platform adds who the agent is, who owns it and what it may do in production, which turns a decision about traffic into a decision about governance. See this row in the table
About Mia-Platform
Other categories you may be weighing.
Already running a gateway? No problem. Where you already run an API or AI gateway, the conversation is familiar: Mia-Platform adds the context, ownership and environment rules around it, so the gateway becomes one enforcement point of a governed platform.
Hyperscaler Agent Platforms
Strong enforcement inside their own cloud. The catch: your AI rules end up living in that cloud too.
AI Cost & FinOps Tools
Cost reports after the bill. AI spend managed before the first token.
AI Governance Platforms
They hold the policy and the inventory. We apply the policy where agents act, and produce the evidence they consume.