Beyond the technological promises, AI carries a real economic cost. Here's what production bills actually look like.
The prevailing narrative around generative AI often glosses over a harsh financial reality. According to Andreessen Horowitz (a16z), the industry is deeply constrained by infrastructure costs.
Share of capital raised by AI startups allocated to compute resources.
Where the bill actually comes from
A call to an LLM is billed by the token, for what you send in (the context: instructions, documents, conversation history) and for what the model sends back. The bigger the context you send, the more expensive the call, even on a simple question. Many teams discover their real bill only after the first month in production, once traffic outgrows what development testing ever covered.
A Direct Impact on Teams
In practice, this weighs on teams. An IT department deploying a RAG system quickly realizes that 70% of the bill comes from the context sent to the AI.
Cutting the bill without cutting quality
- Summarize the history: send only the context still relevant to the current question on each call.
- Route the work: a fast, cheap model triages simple requests, and a more expensive model only handles the ones that need it.
- Cache repeat queries: a recurring question (FAQ, status check) reuses the answer already computed.
"Inference is often the 'silent killer' of profitability."
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