Originally published on AI Tech Connect.
Why 50–90% of Your Inference Bill Is Avoidable Most teams ship their first LLM feature with a single model wired to every code path. That model is almost always a frontier model, because it was the easiest thing to validate during the prototype. The bill that arrives at month-end is treated as the cost of doing business. It is not. Industry analysis consistently shows that enterprises without a deliberate cost strategy routinely overspend 50–90% on inference — not because the models are expensive, but because the models are used carelessly. The root cause is always some combination of three patterns: routing everything to the most capable and most expensive model regardless of query difficulty; calling the model fresh for queries you have answered before; and running inference at API…
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