Cost Reduction Strategies for Azure OpenAI Service

Quick Answer

Azure OpenAI Service billing is tied directly to token count, both the tokens sent in and the tokens generated back, so the two levers that reduce cost are the two things that determine how many tokens get billed: how large a model is doing the generating, and how long its responses are allowed to run. Switching from GPT-4 to a smaller model like GPT-3.5 for a chat application reduces per-token pricing significantly, since larger models cost more per token to reflect their greater capability — a tradeoff worth making when the smaller model's quality is sufficient for the use case. Separately, capping the max_tokens parameter puts a hard ceiling on how many tokens a single completion can generate, which prevents the model from producing unnecessarily long responses that consume tokens (and cost) without adding proportional value to a chat interaction. These two actions attack cost from different angles — one lowers the price per token, the other lowers the number of tokens generated — which is why they're commonly paired as the two-part answer rather than either alone being sufficient. Any scenario asking specifically about reducing Azure OpenAI cost, as opposed to improving output quality or accuracy, should be evaluated against model size and response length as the two primary cost levers.