Four Amazon Bedrock settings that swing inference costs

The guidance arrives as enterprises move generative AI workloads from pilots into production, where request-level settings accumulate into recurring cloud bills. AWS frames the four choices as knobs inside the inference request itself, meaning they can be tuned without retraining or replacing a model—an attractive proposition for teams already managing prompt and application logic.

The tests expose how uneven the tradeoffs are. On cost, a 1-hour cache write costs 2 times the input rate versus 1.25 times for a 5-minute write, so the longer duration only pays off when the same prefix recurs with gaps beyond 5 minutes. On quality, reasoning effort showed a stark split: high effort used +169% median output tokens on a tool-calling agent task, yet on a code-generation sample the gap between low and high was within run-to-run variation and all graded programs passed. That suggests the right effort level depends on how close a task sits to the model's capability boundary.

Operationally, AWS points to request metadata, CloudWatch metrics such as OutputTokenCount and ResolvedServiceTier, invocation logs, and task pass rates as the feedback loop. It also warns that maxTokens is an output ceiling rather than a safety control, and recommends Guardrails for content filtering and prompt-attack detection in production. The recurring theme is per-model verification: cache durations, Flex support, and reasoning defaults differ by model version and Region, so a capability record may be needed wherever routing spans multiple models.