Some workloads — such as text classification, keyword extraction, intent detection, routing, or lightweight summarization — do not require the capabilities of the most advanced model families. When high-cost models are used for these simple tasks, organizations pay elevated token rates for work that could be handled effectively by more efficient, lower-cost models. This mismatch typically arises from defaulting to a single model for all tasks or not periodically reviewing model usage patterns across applications.
On-demand Azure OpenAI deployments are billed per input and output token. Larger, more capable models (e.g., GPT-4 class) have significantly higher cost per token. Choosing a model that exceeds workload requirements increases spend without improving output quality.
