Why Ignoring Token Costs Can Kill Your AI Product (and How to Fix It)

When building applications powered by LLMs from providers like OpenAI, Google, or Mistral AI, there’s a detail that often gets overlooked: token cost. At small scale, it’s barely noticeable. But once your application starts getting real usage, token consumption grows quickly—and if you’re not measuring it, you can easily end up with a feature that costs more than the value it delivers. The real problem with token usage Every interaction with an LLM typically involves: input tokens (your prompt) output tokens (the model’s response) sometimes cache tokens, depending on the provider Individually, these costs are small. But combined with: longer prompts verbose outputs high request volume they scale faster than most people expect. And there’s an important nuance here: Not all models cost the same, and not all tasks require the same type of model. Model selection is a cost decision It’s common to default to the most capable model available, but that’s rarely the most efficient choice. For e