AI Gateway

What is an AI Gateway?

An AI gateway is an infrastructure layer that sits between your applications and AI model providers. It routes requests to the appropriate model and provider, normalizes different API formats into a single interface, and handles operational concerns like failover, monitoring, and cost management.

Think of it as a smart proxy for AI inference: instead of your application talking directly to OpenAI, Anthropic, and Google with three different integrations, it talks to one gateway that handles all the complexity.

Why AI Gateways Exist

The multi-model reality: No single AI provider offers the best model for every task. Claude excels at reasoning, GPT at certain coding tasks, Gemini at multimodal work. Production agents increasingly mix models—using frontier models for planning and cheaper models for execution.

Operational complexity: Each provider has different APIs, rate limits, pricing, and reliability characteristics. As OpenRouter's COO explains, managing this across 70+ providers is a full-time job.

The optionality requirement: The model landscape changes monthly. An AI gateway lets you switch models without rewriting code, test new releases immediately, and avoid vendor lock-in.

Key Capabilities

Unified API

• Single authentication and billing
• Normalized request/response formats
• Consistent tool calling across providers

Intelligent Routing

• Route by capability (best model for task)
• Route by cost (cheapest option meeting requirements)
• Route by latency (fastest provider available)
• Geographic routing for data compliance

Reliability Features

• Automatic failover when providers have outages
• Load balancing across multiple endpoints
• Capacity management for burst workloads

Observability

• Real-time latency and accuracy monitoring
• Cost tracking across models and use cases
• Usage analytics by team, project, or agent

AI Gateway vs Direct API Access

Why Gateways Matter for Agents

Production AI agents have specific needs that gateways address:

Tool calling accuracy: The same model can behave differently across providers. Quality gateways benchmark and route to providers with verified tool-calling reliability.

SLA requirements: When agents run in production, downtime matters. Gateways provide enterprise-grade uptime through multi-provider redundancy.

Cost optimization: Agents make many API calls. Gateways help route routine tool calls to cheaper models while reserving frontier models for judgment calls.

Major AI Gateway Providers

• OpenRouter - Largest independent gateway, 70+ providers
• Portkey - Enterprise-focused with governance features
• LiteLLM - Open-source, self-hostable
• Cloud-native options - AWS Bedrock, Azure AI Gateway

The Gateway Layer in Agent Architecture

┌─────────────────────────────────────────────┐
│            Agent Application                │
│  (reasoning, tool calls, orchestration)     │
└─────────────────────┬───────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────┐
│              AI Gateway                      │
│  (routing, failover, monitoring, billing)   │
└─────────────────────┬───────────────────────┘
                      │
        ┌─────────────┼─────────────┐
        ▼             ▼             ▼
   ┌─────────┐  ┌─────────┐  ┌─────────┐
   │Anthropic│  │ OpenAI  │  │ Google  │
   └─────────┘  └─────────┘  └─────────┘

Related Reading

• Tool Use - The capability that makes agents work
• AI Agents - Systems that benefit most from gateways
• How Companies Put Agents Into Production - OpenRouter's data on agent adoption