Honestly, how I Cut Our AI API Bill by 95%: What Actually Worked
When I first looked at our AI infrastructure spend six months ago, I nearly choked on my coffee. We were burning $11,000 a month on LLM calls for a product serving maybe 4,000 active users. The math was brutal — we were subsidizing every interaction, and our unit economics were completely broken.
The worst part? I knew it was bad, but I didn't realise how much was being left on the table. After three months of focused optimization, we're running the same workload for under $400/month. That's not a typo. Here's the playbook, written from the trenches.
If you're a CTO or engineering lead shipping AI features right now, this is for you. No fluff, no hand-waving — just the architecture decisions that moved the needle on our P&L.
The First Mistake: Defaulting to the Most Expensive Model
