AI Gateway

ELI5 (Explain Like I'm 5)

Think of an AI Gateway like a corporate travel desk. Employees don’t book flights directly with every airline. Instead, they go through a single travel system that:

• Chooses between airlines
• Applies company policies
• Tracks spending
• Handles approvals
• Provides visibility to finance and security teams

The employees just say where they want to go and their tentative travel dates.

An AI Gateway works the same way for AI models. Applications don’t talk directly to OpenAI, Anthropic, Gemini, or other AI providers. They send requests to the gateway, which:

• Routes the request to the appropriate model
• Applies security rules
• Tracks usage and cost
• Ensures consistent behavior across applications

Why AI gateways exist

Managing multi‑model complexity

Each LLM provider uses different APIs, authentication methods, and response formats. AI gateways normalize these differences, often on a ChatGPT-like interface for all models.

Security and compliance

Gateways enforce rate limits, redact sensitive data, manage API keys, and log prompts and responses for auditing, which is critical for enterprise AI deployments.

Cost and usage control

AI Gateways provide comprehensive monitoring of AI interactions. This includes tracking API performance, latency, error rates, and token consumption.

Popular Tools and Providers

Cloudflare AI Gateway: Great for speed, leveraging a global network to cache responses and cut down on latency.

LiteLLM: An open-source tool for developers who need to swap between 100+ different models with a single line of code.

Kong: Enterprise option that now includes specialized plugins for AI governance and PII masking.

Apache APISIX: A great option for large-scale teams that need to include AI security in their existing API infrastructure.

TrueFoundry: A platform-level solution that integrates the AI gateway into a full MLOps ecosystem for model deployment.

Solo.io Gloo: Designed for complex Kubernetes environments, managing AI traffic alongside your standard cloud-native services.

AI gateways are quickly becoming the standard infrastructure for any company moving beyond basic experimentation with AI into full-scale production. As open standards like MCP (Model Context Protocol) mature, these gateways will act as the bridge that allows AI to safely access private company data without compromising security.