Stop Overpaying for VectorDBs: Architecting Serverless RAG on AWS

Building a Retrieval-Augmented Generation (RAG) prototype takes a weekend. Taking that prototype to production without burning through your infrastructure budget is a completely different engineering challenge. One of the most common pitfalls I see founders and engineering teams fall into is the Vector Database Cost Trap . To get their MVP out the door, teams spin up provisioned vector databases or run dedicated EC2 instances 24/7. It works brilliantly for the first 100 users. But as you scale or worse, when traffic is unpredictable paying for idle compute to keep a vector index in memory becomes a massive drain on your runway. If you want to build a highly scalable AI product while protecting your startup's runway, you need to shift from provisioned infrastructure to an event-driven, serverless architecture. The Shift: Serverless RAG Traditional RAG architecture requires you to provision database nodes, manage cluster scaling, and pay for peak capacity even at 3 AM. By moving to a ser