Auto-scaling

Auto-scaling automatically adjusts the compute resources allocated to an AI service based on current demand. When traffic increases, additional model instances are launched to handle the load. When traffic decreases, excess instances are shut down to save costs. This ensures that the service has exactly the right capacity at all times. Auto-scaling can be based on various metrics: CPU or GPU utilisation, request queue depth, response latency, or custom metrics like tokens per second. Scaling policies define the rules — for example, 'add an instance when GPU utilisation exceeds 80% for 5 minutes' or 'remove an instance when the request queue is empty for 10 minutes'. For AI workloads, auto-scaling has unique considerations. GPU instances take longer to start than CPU instances (often 2-5 minutes including model loading). Pre-warming strategies (keeping a minimum number of instances ready) can reduce cold-start latency. Some platforms support scale-to-zero for cost savings, while others maintain a minimum instance count for instant availability.

AI workloads often have highly variable demand patterns. A customer support bot might handle 10x more queries during business hours than at night. A document processing service might see spikes when large batches are submitted. Without auto-scaling, organisations must either over-provision (wasting money on idle resources) or under-provision (risking poor performance during peaks). Auto-scaling directly impacts the economics of AI operations. GPU compute is expensive, and paying for idle GPUs erodes the ROI of AI investments. Auto-scaling ensures that organisations pay for GPU time only when it is actually needed, potentially reducing costs by 40-70% compared to fixed provisioning. The trade-off is complexity and cold-start latency. Auto-scaling configurations must be carefully tuned to respond quickly enough for user experience while avoiding thrashing (rapidly scaling up and down). Model loading times during scale-up events need to be managed to prevent degraded service during traffic ramps.