Cost Optimization in Production
Reduce cloud ML inference costs without sacrificing performance or reliability
Where the Money Goes
Cloud ML costs break down into four buckets. Most teams only optimize compute; the bigger wins are often elsewhere.
Compute (GPU/CPU instances)
Typically 60-70% of cost. Optimize with: right-sizing instances, spot/preemptible instances for training, mixed-instance types, auto-scaling to match traffic patterns.
Data storage and transfer
Often underestimated. Training data stored at full precision when compressed formats would work. S3 egress costs for model serving. Optimize with: Parquet/Delta Lake for feature storage, keeping compute and storage in the same region.
Serving idle capacity
Keeping 10 replicas running at 5% utilization is 95% waste. Right-size with HPA. For non-latency-critical workloads, batch inference costs 10-50x less than real-time serving.
Retraining frequency
Retraining daily when weekly would suffice multiplies training costs 7x. Use drift monitoring to retrain only when needed.