Reliability & Scaling
AI systems in production face reliability challenges that standard software does not — probabilistic outputs, external API rate limits, latency variance, and quality degradation that is hard to detect automatically. This section covers the operational patterns that make AI systems resilient at scale: caching, rate limit handling, intelligent routing, monitoring, and defining SLOs that actually reflect AI system health.
In This Section
Caching Strategies
Prompt caching, semantic caching, and response caching — how each works, what it costs to set up, and when each pays off.
Rate Limit Handling
Designing systems that handle provider rate limits gracefully — exponential backoff, request queuing, and capacity planning.
Multi-Model Routing
Routing requests to different models based on complexity, cost, and latency — patterns, tradeoffs, and fallback strategies.
Monitoring & Alerting
The metrics that matter for AI systems — latency, quality signals, cost, and how to alert on degradation that traditional uptime monitoring misses.
SLOs for AI Systems
Defining service level objectives for AI — why traditional uptime SLOs are insufficient and how to define quality and latency SLOs for AI workloads.