How we reduced AI inference costs by 60% without sacrificing accuracy

A team reduced ML inference costs from $12,000/month to $4,500/month for a fintech document classification pipeline by applying two key strategies. First, they distilled BERT-large (340M params) into DistilBERT (66M params), cutting SageMaker costs from $4,200 to $1,400/month with only a 1.4% accuracy drop. Second, they began replacing expensive GPT-4 API calls for entity extraction with targeted smaller models suited to each extraction category.