Leverage prompt caching to reduce AI agent token costs
Reuse previously processed prompt prefixes to avoid reprocessing and save on token costs for agents.
Prompt caching lets a previously processed prefix be reused rather than reprocessed. Cache reads are billed at a discount to normal input pricing on standard deployments, and can be discounted up to 100% on provisioned deployments.
Reduces token costs by avoiding redundant processing of system instructions, tool schemas, and policy text that are re-sent on every turn of an agent.
Implement a caching mechanism that stores and retrieves previously computed prompt prefixes, significantly lowering costs for multi-turn agent interactions.
Implement prompt caching for AI agents to reduce costs. By reusing previously processed prompt prefixes, you avoid reprocessing common elements like system instructions and tool schemas, leading to significant savings on token usage.
Agents often repeat the same initial instructions or context across multiple turns. Caching these repeated elements means they only need to be processed once, saving computational resources and associated token costs on subsequent turns.
When agent interactions are consistently short or do not involve repetitive prefixes.
- The Economics of Agent Optimization: Four ways to lower the cost ↗Microsoft Azure Blog · Microsoft Azure Blog · Aug 26, 2026 · quoted as shared
First seen on Subvolts Aug 26, 2026. Updated Sep 11, 2026. Spotted a problem? Tell us.
- The Economics of Agent Optimization: Four ways to lower the costMicrosoft · Microsoft Azure Blog · Aug 26, 2026
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- WorkflowBuild an AI agent evaluation pipeline in one hourCompress weeks of AI agent testing into one hour using open-source tools and automated pipelines. · 60 min
- Anti-patternUsing prototype defaults for production AI agent architectureDon't let prototype settings for AI models and prompts become production defaults; they are not optimized for scale or cost. · 60 min
- ChecklistChecklist for production-ready AI agent refactoringSteps to ensure an AI agent is production-ready after refactoring. · 20 min
- Anti-patternManual code tweaking is not an AI agent testing strategyStop manual code tweaking and use automated pipelines to evaluate AI agents for production. · 60 min
- ChecklistAI agent evaluation pipeline checklistChecklist for building and running an end-to-end AI agent evaluation pipeline. · 60 min
- Skill or agent configInitialize Restless for API agent readinessRun this command to initialize Restless and make your API agent-ready. · 5 min
Questions people ask
- Which AI tools does this work with?
- Any current model. It was seen with in the sources.
- Where did this come from?
- Microsoft Azure Blog (Microsoft Azure Blog). Subvolts collected, normalized and credited it; the link above opens the original.