A large share of production AI workloads include repetitive or static requests—such as classification labels, routing decisions, FAQ responses, metadata extraction, or deterministic prompt templates. Without a caching layer, every repeated request is sent to the model, incurring full token charges and increasing latency. Azure OpenAI does not provide native caching, so teams must implement caching at the application or API gateway layer. When caching is absent, workloads repeatedly spend tokens for identical outputs, creating avoidable cost. This inefficiency often arises when teams optimize only for correctness—not cost—and default to calling the model for every invocation regardless of whether the response is predictable.
Azure OpenAI on-demand usage is billed per input and output token. Re-running identical prompts consumes tokens unnecessarily when responses could be served from a cache. For workloads with repetitive queries, caching can reduce both cost and latency significantly.
