Your Amazon Bedrock inference bill can keep climbing after you tune prompts and application logic. Request-level choices can still change how many tokens the service processes and the rate applied to them. Cache duration, service tier, reasoning effort, and maxTokens can affect cost without changing your model or prompt.

Each choice depends on the model and workload. This post shows how we tested each choice and how to verify its effect from response metadata, logs, and billing data.

To try these settings yourself, start with the Amazon Bedrock Samples notebooks Mixed TTL Caching and Tenant Isolation with Prompt Caching. For maxTokens and reasoning effort, see the 01-low-effort folder of the sample-code-for-genai-cost-management-workshop GitHub repo. Each sample sets its own model ID, so change it, and the inference parameters, to match the model you call. For model availability by AWS Region, refer to Models at a glance. For broader decisions such as model selection, batch inference, routing, and distillation, see Effective cost optimization strategies for Amazon Bedrock.

Every Amazon Bedrock response carries usage metadata. Input and output tokens bill at the rate for the model and tier that served the request. Cache reads and writes use separate token counters, and reasoning tokens count as output tokens.

Repeated system instructions, tool definitions, or reference context can be processed again when the cache expires before the next request. The Prompt caching for faster model inference guide lists 5-minute and 1-hour time-to-live (TTL) values for supported Anthropic models. 6 models. Support and API fields depend on the model, so check the current support matrix before selecting a duration.

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