In development, you're the only user, the data is small, and the LLM responds in a few seconds. Production at 1 million users is a completely different game.
What Breaks First at Scale
- LLM rate limits: Providers cap requests and tokens per minute. Hit the cap and users see errors. Plan for quota increases, multi-provider fallbacks, and queueing.
- Latency: Retrieval plus reranking plus generation can take many seconds. Stream responses, cache aggressively, and enforce tight timeouts.
- Cost: A few cents per conversation becomes a massive monthly bill. Track cost per request, route simple queries to smaller models, and trim prompts.
- Concurrency: Thousands of long-running HTTP connections hold resources open. Your servers need to handle many slow streaming connections smoothly.
- Vector database limits: Index size, memory usage, and query load grow rapidly. Shard, replicate, and apply metadata pre-filtering.
- Ingestion spikes: Bulk document uploads will starve real-time search queries unless isolated in separate workers and queues.
- Failure cascades: If the LLM provider slows down, requests pile up and crash your web tier. Implement circuit breakers and graceful fallback messaging.
- Abuse & scraping: Bots will target your expensive endpoints. Mandate per-user quotas and bot mitigation.
Pre-Launch Checklist
- Load test with realistic prompt context sizes and conversation lengths.
- Set hard spending limits and real-time alerts.
- Define fallback UX when the model provider suffers an outage.
Scaling an AI app is less about the model and more about everything wrapped around it.
What's your biggest scaling worry?
LinkedIn Post Snippet & Hashtags
Share this key insight directly with your LinkedIn network:
Rate limits, latency, cost, and failure cascades: the things that break AI apps at scale have little to do with the model itself. Here's what to plan for before launch. 👇
#Scalability #AIEngineering #LLM #SystemDesign #SoftwareEngineering
Tags:
#LLM
#AIEngineering
#SystemDesign
#Scalability
#SoftwareEngineering