Managing Amazon Bedrock costs end to end: A playbook for FinOps and platform teams | Amazon Web Services
Cost management for generative AI is a shared responsibility between the teams that build applications and the teams accountable for cloud spend. Many teams start by comparing per-model token rates and asking application teams to shorten prompts. Those steps help, but they don’t answer the questions that finance and engineering leaders ask as usage scales: which team spent this, on which workload, and was that spend inside an approved budget?
Agentic workloads make those questions more pressing. A conversational assistant typically issues one model invocation per user turn. An agentic workflow, such as an AI coding assistant that plans a change, reads files, calls tools, and iterates, issues many invocations per task and carries a larger context on each one. Token consumption therefore scales with the degree of autonomy you grant the workflow and the size of the context it carries, rather than with the number of user interactions. Because...
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