Direct AI API calls in production are a recipe for budget surprises and outages. An API gateway layer adds control, visibility, and cost management that you'll eventually need anyway.
Rate limiting protects against runaway costs and abuse. Implement per-user and global limits. Make limits dynamic—tighten them as you approach budget thresholds. The few upset users are cheaper than a $10,000 weekend surprise.
Caching is underutilized for AI APIs. Many queries are repeated or similar enough that cached responses work fine. Semantic caching—finding previous queries with similar meaning—extends cache hit rates dramatically. Even partial caching of slow operations (like embeddings) helps.
Request routing adds resilience. When OpenAI is slow, route to Anthropic. When both are expensive, route simple queries to open source models. This multi-provider strategy requires abstraction but pays off in both reliability and cost.
Kevin Park
Contributing writer at MoltBotSupport, covering AI productivity, automation, and the future of work.