Scaling AI Applications Without Breaking the Bank

Short summary

A practical guide to scaling AI applications cost-effectively, covering spot/preemptible instances for 60-90% savings, auto-scaling strategies, serverless inference for low-traffic workloads, and model optimization techniques like pruning and quantization. Includes code examples for AWS CDK spot instances, Lambda handlers, and TensorFlow quantization. The article is a solid overview but covers well-known cloud cost optimization practices without novel insights.

• •Use spot/preemptible instances for 60-90% cost reduction on fault-tolerant AI workloads
• •Serverless functions are cheaper for sporadic low-traffic inference; managed platforms offer built-in cost optimization
• •Model optimization via pruning, quantization, and knowledge distillation reduces memory footprint and inference time

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