API gateway pattern for AI applications

Your API gateway works great for REST APIs. Connect it to an LLM and watch it become expensive, slow, and blind.

The ai api gateway pattern solves this. But most teams use traditional gateways and wonder why their AI costs spiral out of control while their monitoring shows nothing useful.

Why traditional gateways break with AI

Traditional API gateways count requests. They rate limit by calls per minute. They track response times and error rates. This made sense when each request cost roughly the same and took similar time to process.

AI breaks all these assumptions.

A single LLM request can consume 10 tokens or 10,000 tokens. That first request might cost a fraction of a cent. The second one? Several dollars. HAProxy’s research shows token consumption varies by over 1000x between requests to the same endpoint.

Response times are just as unpredictable. Generate a short answer and you wait 200 milliseconds. Ask for detailed analysis and you are looking at 30 seconds or more. Traditional timeout settings either fail fast or wait forever.

Your authentication works, sure. Your rate limiting? Useless. Someone makes 10 requests and hits your limit, but they only used 100 tokens total. Another user makes 3 requests and burns through your entire daily budget with massive context windows.

The cost tracking nightmare

Running AI without token tracking is like paying for electricity without a meter. You find out the damage when the bill arrives.

I came across this breakdown from TrueFoundry that captures the problem. They track organizations trying to manage LLM costs and finding that traditional monitoring tells them nothing useful about who is spending what or why.

The ai api gateway pattern needs to count tokens, not requests. It needs to track costs per user, per feature, per team. This means intercepting every request, parsing the prompt to count input tokens, reading the response to count output tokens, and multiplying by the current rate for that specific model.

Different models charge different rates. GPT-4 costs more than GPT-3.5. Claude Opus costs more than Claude Haiku. Your gateway needs to know which model handled each request and apply the right pricing.

Langfuse’s token tracking shows what production-ready cost management looks like. They track tokens at the request level, aggregate by user and feature, and provide daily metrics for showback and chargeback. Without this level of detail, you cannot answer basic questions like which product feature is burning through your AI budget.

Multi-model orchestration reality

Here’s what production AI looks like. You call OpenAI’s API. It returns a rate limit error. Your application shows an error to the user.

Or you implement proper multi-model orchestration. Same scenario, but your gateway automatically retries with Anthropic. User sees no error. You stay online.

Portkey’s fallback patterns explain the implementation. Define your primary model, list your fallback options in order, set retry logic and circuit breakers, and let the gateway handle failures automatically.

This gets more interesting when you optimize for cost and performance simultaneously. Route simple queries to faster, cheaper models. Send complex requests to more capable models. If the expensive model is unavailable, fall back to the cheaper one instead of failing.

The Apache APISIX team documented how they handle multi-provider routing. They proxy requests to OpenAI, Anthropic, Mistral, and self-hosted models through a single endpoint, enforce consistent authentication and rate limiting across all providers, and provide unified observability regardless of which model actually processed the request.

Load balancing helps when you are hitting rate limits. Split traffic across multiple API keys for the same provider. Distribute requests across different models with similar capabilities. Route to different regions based on latency.

Security done right

API key management for AI gets complicated fast. Each developer needs keys for testing. Each environment needs different keys. Each customer might need isolated keys for compliance.

Storing these keys in your application code is obviously wrong. Putting them in environment variables is barely better. API Gateway security patterns show that proper key management means storing credentials in a secure vault, rotating them regularly, using the gateway to inject keys at request time, and never exposing raw keys to client applications.

Data privacy matters more with AI than traditional APIs. Every prompt you send potentially contains sensitive information. Every response might include data you should not cache or log.

The gateway needs to sanitize logs, removing personally identifiable information before storing request details. It needs to enforce data residency rules, routing requests to models in specific geographic regions. It needs to support compliance requirements like GDPR and HIPAA without making developers implement these controls in every application.

Audit logging becomes critical. Who made which request? What data did they send? Which model processed it? How long was the response cached? These questions come up during security reviews and compliance audits. Your gateway should answer them without you digging through application logs.

What works in production

Real implementations from Apache APISIX users show the same pattern. Companies like Zoom, Lenovo, and Amber Group use API gateways to manage AI traffic alongside traditional APIs, but they configure them differently for AI workloads.

Kong Gateway offers token-based rate limiting that counts tokens instead of requests. Their implementation pulls token data directly from LLM provider responses, supports limits by hour, day, week, or month, and handles different limits for different models automatically.

Azure API Management shows what enterprise-grade AI gateway looks like. They manage multiple AI backends from a single gateway, implement semantic caching to reduce duplicate requests, provide built-in token metrics and cost tracking, and integrate with existing API management workflows.

The self-hosted versus managed decision depends on your priorities. Self-hosted gives you complete control over data routing and security policies. Managed solutions reduce operational overhead but lock you into their ecosystem.

Start with observability. If you cannot see token usage, model performance, and cost attribution, you cannot optimize anything. Observability for AI gateways needs to track token consumption per request, cost per user and feature, cache hit rates for savings, model latency and error rates, and fallback trigger frequency.

The ai api gateway pattern becomes mandatory when you run AI in production. The question is not whether you need it, but whether you build it properly or learn these lessons expensively.