Seven Patterns That Decide If Your AI App Survives 10,000 Users
Hello, I'm Maneshwar, and I'm building LiveReview — a blast-radius aware AI code review built for your business-critical systems. Star us to help devs discover the project, give it a try, and share your feedback to help improve the product. Here's a fun fact that nobody puts on the slide at the AI meetup. Your agent can be perfect. The prompt can be tuned within an inch of its life. The eval…
All seven patterns aim to ensure an AI application remains functional and scalable as it attracts more users. The first pattern involves utilizing an API gateway as a reception desk to manage incoming traffic, check the identity of callers, and route requests efficiently. The second pattern introduces rate limiting to control the amount of work that can begin, which is more relevant for AI systems than simple request counting.
The third pattern discusses caching safe-to-reuse data, such as embeddings and retrieval results, in a Redis cache to reduce latency and costs. However, careful consideration must be given to cache expiry, scope, and invalidation to avoid serving incorrect or outdated information. Pattern four focuses on deferring non-critical work to background queues, allowing the system to prioritize immediate requests while handling less urgent tasks at its own pace.
Pattern five emphasizes the importance of preventing a single broken dependency from overwhelming the entire system, ensuring that failures are contained and do not lead to system-wide outages.
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