How We Cut AI Infrastructure Costs by 80% for Enterprise Clients

Last year we spent $47,000/month on AI infrastructure for a single enterprise client. Today it's $8,200/month — same quality, same throughput. Here's exactly how we cut 80% without sacrificing performance. The Starting Point: $47K/Month The client had a document processing pipeline handling 500K+ documents monthly. The original architecture: GPT-4 for everything (classification, extraction, summarization, Q&A) Pinecone for vector storage ($500/month for 2M vectors) No caching, no batching, no model routing Every query hit the most expensive model This is what happens when you prototype with one model and never optimize for production. We see this in 80% of enterprise AI projects — the POC cost was fine, the production bill was not. Cut #1: Multi-Model Routing (saved 60%) The single biggest win. We profiled every query type and mapped it to the cheapest model that could handle it: Query Type Before After Cost Change Document classification GPT-4 ($30/1M) GPT-4o-mini ($0.15/1M) -99.5