Your AI Agent's Cloud Bill Is an Attack Surface
LLM inference costs money. In the cloud, every token has a price tag. The industry built cost controls around a simple model — one request, one inference, one bill. That model is dead. Agentic AI, MCP tool chains, multi-modal inputs, and scheduled agents have made the actual unit of cost an execution tree, not a request. This post breaks down what fundamentally drives LLM cost, tests six assumptions we've been building on, and maps the gaps that emerge when those assumptions fail.
What Actually Costs Money?
First-principles thinking means stripping away conventions and asking: what is irreducibly true? Not "what attacks have people seen before" — that's reasoning by analogy. Instead: what are the raw materials of LLM cost?
An LLM inference has exactly four cost components:
- Compute — GPU cycles to process input tokens and generate output tokens
- Memory — VRAM to hold model weights and the KV cache...
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