AI API Integration Guide: Patterns That Hold Up in Production
Integrating an AI API is easy until you hit rate limits, timeouts, and inconsistent output. Here are the patterns I use in every integration. Calling an AI API for a prototype takes five minutes. Running the same call in production, reliably, under load, takes more thought. Rate limits, streaming timeouts, inconsistent output formats, and cost overruns all show up once real traffic hits. Here are the integration patterns I use in every AI API integration, refined across a dozen production deployments. Handle Rate Limits With Backoff, Not Retries AI APIs enforce rate limits on requests per minute and tokens per minute. Hitting the limit is not an error, it is a normal operating condition under load. The correct response is exponential backoff with jitter, not an immediate retry. Immediate retries hammer the API and often hit the limit again. I implement backoff starting at one second, doubling up to a cap of thirty seconds, with a random jitter so concurrent retries do not synchronize. I also track the rate limit headers the API returns and throttle proactively before hitting the limit. Proactive throttling keeps the throughput higher than reactive backoff, because requests that would have been rejected stay in the queue instead of failing and retrying. Here is the backoff implementation I use: async function callWithBackoff(apiCall, maxRetries=5): for attempt in range(maxRetries): response = await apiCall() if response.success: return response if response.