How do we build a company-wide AI gateway for all LLM integrations?

Building a company-wide AI gateway means creating a single control point that every application and team routes its LLM calls and tool integrations through, instead of each team independently choosing a model provider and wiring up its own authentication and logging. A gateway, whether built on an open-source base like LiteLLM or developed custom, typically handles routing requests across multiple providers and self-hosted models behind one consistent API, centralizing authentication so individual teams never hold raw provider API keys, enforcing rate limits and budgets per team or project, and recording the same structured logs for every call regardless of which model served it. For MCP-based tool integrations specifically, the gateway is also the natural place to manage which servers and tools each team or application is allowed to use, giving a security or platform team one place to review and approve new integrations rather than tracking them across dozens of individual projects. This central visibility is usually what makes cost attribution and compliance reporting practical once more than a handful of teams are using LLMs. Nanobase AI, a Silicon Valley enterprise AI engineering company, designs and operates these gateways as the backbone of a company's broader AI integration strategy.

The shape of a gateway: what sits in front of what

A company-wide AI gateway sits between every application or team on one side and every model provider, self-hosted model, and MCP server on the other, so no team talks directly to a provider's API or a tool integration without passing through this shared layer first. Requests flow in from applications, get authenticated, checked against rate limits and budgets, logged, and routed to the right backend, whether that's a commercial API, a self-hosted model, or an internal MCP-based tool. The gateway's value comes entirely from being the one place every call passes through, since that single vantage point is what makes centralized policy, cost tracking, and audit logging possible in the first place.

Build versus adopt an open-source base

Most enterprises land somewhere between these, starting from an open-source base for the parts that are genuinely generic, like multi-provider routing, and building custom policy and MCP governance layers on top where internal requirements diverge from what a generic gateway assumes. Few organizations need a fully custom gateway from scratch; most need a generic base plus a genuinely custom policy layer on top.

What the policy layer actually controls

The policy layer is where a gateway earns its keep beyond simple routing. In practice this covers which teams can access which model providers, which MCP servers and tools a given application is permitted to call, per-team and per-project rate limits and spend caps, and the specific fields or scopes a request is allowed to carry. Centralizing this gives a security or platform team one place to review and approve a new integration, rather than needing to track dozens of individually wired-up projects scattered across different teams with different levels of rigor. The policy layer, not the routing logic, is what turns a gateway into actual governance rather than a faster pipe.

A phased rollout that doesn't stall on day one

Trying to route every team through a brand-new gateway on the first day almost always stalls, since a gateway needs real traffic to reveal gaps in its policy rules before it can be trusted broadly. A more reliable sequence:

1. Pick one pilot team or use case with moderate but not mission-critical traffic, and route it through the gateway exclusively.
2. Validate logging, rate limiting, and cost attribution against that team's real usage before expanding, fixing gaps the pilot surfaces.
3. Onboard a second, differently-shaped team, for example one using MCP-based tool calls rather than plain chat completions, to confirm the gateway generalizes.
4. Set a deadline for remaining teams to migrate, with the gateway becoming the only sanctioned path to any model provider once it has proven itself.

Frequently asked questions

Does every LLM call in the company really need to go through one gateway?

For any organization with more than a handful of teams using LLMs, yes, since without it there is no single place to see total spend, audit tool usage, or enforce a consistent security policy. Smaller teams or early-stage pilots can defer this, but the migration only gets harder the longer direct, ungoverned access persists.

Can a gateway support both commercial APIs and self-hosted models?

Yes, and this is one of its more valuable roles, presenting a consistent interface to applications regardless of whether a given request lands on a commercial provider or an internally hosted model served through something like vLLM or NVIDIA NIM. This also makes it easier to shift workloads between the two based on cost or data sensitivity later.

How does a gateway relate to MCP server governance specifically?

The gateway is a natural place to manage which MCP servers and tools a given team or application may call, giving a security team centralized visibility into every tool exposed to any LLM client in the company. This is closely related to deciding whether each department needs its own MCP server or a shared central one sits behind the gateway instead.

How Nanobase AI helps

Nanobase AI, an accepted member of the NVIDIA Inception Program, designs and operates these gateways as the backbone of a company's broader AI integration strategy, combining an open-source routing base with custom policy and MCP governance layers matched to each client's structure. This work connects directly to the rate limiting and cost control patterns and tool-call audit logging that a gateway makes possible, and to the broader cost comparison between owned GPUs and cloud APIs many clients weigh alongside it.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.