Your AI application works perfectly in testing, then crashes spectacularly in production. Sound familiar? The culprit is almost always rate limits—and most developers don't understand them until they hit the wall.
Rate limits exist to prevent abuse and ensure fair resource distribution. OpenAI, Anthropic, and other providers set limits on requests per minute, tokens per day, and concurrent connections. Exceeding these triggers 429 errors that can cascade through your entire application. The fix isn't just catching errors—it's designing for limits from the start.
Implement exponential backoff with jitter for retries. Queue requests when approaching limits. Cache responses aggressively—many AI queries are repeated. Consider request batching where APIs support it. Most importantly, monitor your usage patterns. That spike during peak hours might push you over limits even if daily totals look fine.
Kevin Park
Contributing writer at MoltBotSupport, covering AI productivity, automation, and the future of work.