AI Infrastructure Scaling Patterns

AI workloads have fundamentally different scaling characteristics than traditional web applications. A web server handles requests in milliseconds with negligible marginal compute cost. A model inference endpoint may take seconds per request, consume gigabytes of GPU memory per replica, and cost orders of magnitude more per request. This means that scaling decisions have a much larger cost impact, over-provisioning is extremely expensive, and under-provisioning causes cascading latency failures rather than graceful degradation.

The other critical difference is heterogeneity. A traditional web application scales by adding identical replicas behind a load balancer. AI infrastructure must support multiple model types (each with different resource requirements), mixed workload priorities (latency-sensitive inference vs. throughput-optimized training), and different scaling behaviors (inference scales with request volume, training scales with data volume and desired iteration speed). A single scaling policy cannot handle this diversity.