How we reduced AI inference costs by 60% without sacrificing accuracy
Running ML models in production is expensive. When we deployed a document classification pipeline for a fintech client last year, our inference costs hit $12,000/month within the first quarter. The...
Source: dev.to
Running ML models in production is expensive. When we deployed a document classification pipeline for a fintech client last year, our inference costs hit $12,000/month within the first quarter. The models were accurate, but the economics did not scale. Over 4 months, we brought that number down to $4,500/month while keeping accuracy above 95%. Here is exactly how we did it. The starting point The client needed to classify and extract data from financial documents: invoices, bank statements, tax forms, and contracts. We built a pipeline using a fine-tuned BERT model for classification and a GPT-based model for entity extraction. The stack: Classification: Fine-tuned BERT-large (340M params) on AWS SageMaker Extraction: GPT-4 API calls for structured data extraction Volume: ~50,000 documents/month Infra: SageMaker real-time endpoints, always-on It worked well functionally. But the cost breakdown was brutal: SageMaker endpoints (24/7): $4,200/month GPT-4 API calls: $6,800/month S3 + data
