Provisioned Throughput vs Pay-As-You-Go

Pay-as-you-go generally charges for usage without a long-term capacity commitment, while a provisioned-throughput option reserves a stated amount of capacity under a provider’s terms and commitment period. Providers implement these offers differently. Google Vertex AI describes shared pay-as-you-go quota and Provisioned Throughput based on Generative AI Scale Units; Amazon Bedrock offers Provisioned Throughput with model units and commitment terms. Their product conditions are not interchangeable. Shared capacity can be flexible but may face temporary contention or quota errors. Google documents Dynamic Shared Quota behavior and resource-exhausted errors when capacity is unavailable. Reserved capacity may provide more predictable access or throughput within its purchased amount, but it incurs a fixed commitment and does not mean every individual response has guaranteed latency. Read the relevant product terms, capacity estimator, supported models, and overage behavior. Compare the options using measured demand, token mix, peak patterns, utilization, retry behavior, and required service objectives. A reservation can be underused during quiet periods; pay-as-you-go can become costly or insufficient at peaks. Forecasting error, minimum commitments, regional availability, and provider changes affect the outcome. Capacity estimators provide planning inputs, not proof that future requests will meet a given response-time target. Run a pilot with production-like traffic and calculate total cost per successfully served request, including idle reservation cost, overages, and operational controls. Revisit the choice as traffic changes. Capacity planning complements application optimization and does not replace latency monitoring or fallback design.