Why Inference Latency and Availability Drift in Production

Inference systems experience gradual performance degradation that escapes detection because failures are not sudden but accumulate over time. The article identifies three primary sources of latency problems: infrastructure-layer variability from resource contention, model-serving configuration issues like batching and KV cache management, and traffic pattern mismatches with autoscaling capabilities. CoreWeave argues that addressing drift requires architectural solutions such as explicit GPU allocation, traffic-aware autoscaling, and predictable networking rather than improved monitoring alone.