Why Your Inference Stack Is Bleeding Money — And How to Fix It

There's a moment every engineering team hits when they move from prototyping with a hosted LLM API to running models in production. The works beautifully. The CEO is thrilled. Then the invoice arrives. I've spent the last two years integrating generative AI into production systems - first building AI/ML tooling at Apple, then shipping LLM-powered features at SyenApp that serve over 100,000 users daily. Along the way, I've watched teams make the same expensive mistake: they treat inference as a commodity and ignore the architecture beneath it.