A large portion of real-world AI workloads involve repetitive or deterministic inference patterns—such as classification labels, routing logic, metadata extraction, FAQ responses, keyword detection, or summarization of static content. Vertex AI does **not** provide native inference caching, so applications that repeatedly send identical prompts to the model incur avoidable cost. When no caching mechanism is implemented, workloads repeatedly invoke the model and consume tokens even though the output is predictable. Over time, especially at scale, these repetitive token charges accumulate into significant waste. This inefficiency is common in early-stage deployments where teams optimize for correctness rather than cost.
Generative AI workloads are billed per input and output token. Without a caching layer, repeated requests for deterministic or low-variability tasks incur full token charges for every call, increasing cost and latency unnecessarily.
