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The Hidden Costs of AI: What Your Cloud Bill Doesn’t Tell You About LLM Deployment
When we launched our first internal GPT-based assistant, the excitement was electric. Legal teams could search compliance policies in plain English. Engineers could debug configs just by pasting logs. Executives started asking questions like, “Can we put this in every team’s dashboard?”
We were riding the LLM wave — and it was working. Until the invoice hit.
Our cloud bill spiked 4x in 21 days.
And what caught us off guard wasn’t the number of requests — it was all the invisible weight behind each one. We weren’t just paying for inference. We were paying for the hidden economics of scaling AI in production — latency budgets, memory footprints, cold starts, GPU flakiness, and thousands of subcomponents humming quietly in the background.
Here’s what no one tells you about the real-world cost structure of running LLMs — and how we learned to tame it.
What Your Cloud Dashboard Doesn’t Show
Most teams launch their LLM app and monitor three metrics:
- Requests per minute (RPM)
- Average token count
- Total spend
