GenAI FinOps on Databricks: How to Scale Intelligence Without Breaking the Bank
There’s a predictable financial cycle in almost every corporate Generative AI project. Month 1 (The Honeymoon) : The team ships a Proof of Concept. It uses the smartest, most expensive model available. It runs brilliantly on a developer’s laptop. The cloud bill is negligible—maybe $5 a day. Everyone celebrates. Month 3 (The Shock) : The application goes live. Real users arrive. Retrieval volume spikes. Context windows grow. Then the invoice shows up. It isn’t $5 anymore—it’s a meaningful line item, and it’s climbing. Finance asks, “Is this sustainable?” Engineering shrugs: “That’s just what AI costs.” Usually, that’s not true. Runaway costs are rarely a symptom of “expensive AI.” They’re a symptom of unoptimized architecture . Just as you wouldn’t run a simple web server on a supercomputer, you shouldn’t run routine tasks on frontier models with inefficient retrieval and no cost controls. Here’s the financial reality check: how to implement GenAI FinOps on Databricks and cut costs by 4...