# Governing AI Gateway Sprawl

Teams stand up AI gateways independently and end up with no shared governance. What a gateway does, and how to bring API discipline to it.

## The pattern: gateways stood up in isolation

A pattern enterprise platform teams describe: each AI team stands up its own gateway to solve an immediate routing or cost problem. One picks LiteLLM. Another picks Portkey, now Palo Alto Networks' Prisma AIRS AI Gateway. A third builds its own. Nobody coordinates. Before long, the organization has several gateways, several sets of policies, and no shared view of what's being governed.

It's what happens when AI adoption moves faster than platform governance.

Sprawl is a security problem too. On March 24, 2026, two compromised LiteLLM releases on PyPI shipped a credential stealer ([LiteLLM's incident report](https://docs.litellm.ai/blog/security-update-march-2026)). When nobody owns the gateways, nobody knows which teams need to rotate their keys.

Fragmentation happens by default unless someone deliberately prevents it. Treat it as an [API governance](/glossary/api-governance) problem early.

## What a gateway actually is

Strip away the "AI" branding and a gateway is a familiar thing: a single entry point that sits in front of backend services and handles the cross-cutting concerns so individual services don't each reinvent them: authentication, rate limiting, routing, request and response transformation, caching, observability, and policy enforcement. Front door, bouncer, and concierge, in one layer.

Every gateway is a proxy. Not every proxy is a gateway. A plain reverse proxy forwards traffic. A gateway understands that traffic and enforces policy on it. An AI gateway adds LLM-specific concerns to the same list: routing between models, prompt-level filtering, semantic caching, and token-aware rate limits. LiteLLM, Portkey and Kong now front MCP servers too, deciding which tools each agent can call. All of it sits on the same fundamentals. It doesn't replace them. For cost-specific policies, see [API cost control for AI agents](/guides/api-cost-control-for-ai-agents).

## The bridge: what API platform teams already know

API management already solved most of this. Centralized policy, a management plane separate from the data plane, quota and rate-limit enforcement, audit logging: all standard API gateway practice for years. The technology exists. What's missing is the discipline. Teams building AI infrastructure often came from AI, not API management, and stood up point solutions without it.

The fix is teaching AI teams what API teams already know: one management plane, shared policy, and governance that applies across every gateway instance instead of being reinvented per team. It's the same foundation-first logic as the [Agent Access Stack](/glossary/agent-access-stack): get the API layer wrong and every layer above it inherits the problem.

## Where to start

Before you add another gateway, take inventory of what's already running: how many gateway instances exist, who owns each one, which models and MCP servers sit behind them, and which policies (if any) apply across all of them. Picking a "winning" gateway product rarely fixes sprawl. Putting one management plane and one policy set over the gateways you already have does, the same way a mature API team led by an [API product manager](/glossary/api-product-manager) would.

Want a full benchmark of your APIs and MCP with prioritized fixes? [Get in touch](/contact).

### FAQ

**What is an API gateway?** A service that sits between external traffic and your backend services, handling authentication, rate limiting, request validation, logging, and routing while keeping backend services from being directly exposed.

**What's the difference between the data plane and the management plane?** The data plane is where traffic flows: the requests and responses passing through the gateway. The management plane is where you configure the gateway's policies and settings. Sprawl usually means multiple data planes with no shared management plane.

**What policies should a gateway actually enforce?** IP filtering, authentication and credential verification, rate limiting, quota enforcement, request validation, logging, and usage metering. An AI gateway needs the same list, plus model routing, limits that count tokens as well as requests, and rules for which MCP tools each agent can call.

Source: Level 250 — https://www.level250.com/guides/governing-ai-gateway-sprawl