Reliability means a production GenAI application continues to function as expected and meet business needs, including when failures or changing demand occur. It depends on more than keeping a model available: teams must also test and monitor whether its performance and accuracy remain acceptable. section 11.1 section 11.5.1
Build reliability into deployment and day-to-day operation:
- Plan redundancy, failover, and disaster recovery; identify which responsibilities depend on the cloud provider. section 11.1
- Monitor the application continuously, maintain it, and respond quickly when problems appear. Test and validate model performance and accuracy regularly. section 11.1
- Test under different loads. Use load balancing and auto-scaling to help handle traffic spikes, while watching latency, throughput, and cost. section 11.5.1
Preparing for a traffic spike
Before an LLM-backed application goes live, simulate many concurrent users with Locust or Apache JMeter. Inspect throughput and latency, then assess whether load balancing and auto-scaling keep the application responsive as demand rises. Continue monitoring after deployment; a successful load test does not replace failure planning or ongoing validation. section 11.5.1 section 11.1
Reliability is a continuing operational responsibility, not a one-time deployment check. An application using a hosted model depends partly on that provider’s availability; an edge deployment using a small language model brings different scaling and reliability considerations. section 11.1
