Inference pricing for large language models has fallen sharply over the past two years, but that doesn't mean LLM apps are cheap to run. Input and output tokens are priced differently, rates vary between providers, and usage patterns shift constantly as prompts get longer or users ask more complex questions.
Unlike a traditional API with a predictable response size, an LLM call might return 50 tokens or 5,000 tokens depending on the query, and infrastructure can't know which until generation finishes. That unpredictability makes capacity planning and cost forecasting genuinely harder than for conventional services.
Production-grade LLM infrastructure needs real-time token tracking, per-request cost attribution, and caching layers that catch repeat or near-duplicate queries before they hit the model. Without that visibility, teams routinely discover cost overruns only after the invoice arrives.
Cost visibility also enables better product decisions, not just engineering ones. When a team can see exactly which features or user segments drive token spend, they can make informed calls about rate limits, tiered pricing, or where to route traffic to cheaper models without guessing at the trade-offs.
Read More: A breakdown of LLM infrastructure costs
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