Building a Production-Grade AI Gateway on AWS: Token-Aware Rate Limiting, Semantic Caching, Model Routing & Cost Observability
AI gateways need to manage more than API requests. This article builds a production-oriented architecture for token-aware rate limiting, semantic caching, model routing, streaming cost tracking, and per-application observability using FastAPI, Redis, and Amazon Bedrock.
Building a Production-Grade AI Gateway on AWS: Token-Aware Rate Limiting, Semantic Caching, Model Routing & Cost Observability
Modern AI applications rarely depend on a single LLM request.
As traffic grows, teams have to answer harder questions:
- Who is consuming the most tokens?
- What happens when token quotas are exhausted?
- Can repeated questions avoid another model call?
- Should every request use the most expensive model?
- What happens when a user cancels a streaming response?
- Can we trace one request across the gateway, cache, model and logs?
An API gateway that only forwards requests is not enough.
A production AI gateway needs to become an intelligent control plane between applications and foundation models.
In this article, I’ll extend a basic FastAPI + Redis LLM gateway into a production-oriented architecture with:
- Token-aware rate limiting
- Redis-backed reservation and settlement
- Semantic response caching
- Model routing and fallback
- Streaming-aware cost tracking
- Per-user and per-application cost attribution
- Request correlation and observability
- Amazon Bedrock integration
1. The Problem With Request-Based Rate Limiting
A common implementation looks like:
Client → API Gateway → Redis → LLM
and limits requests such as:
1
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