<h1>Optimize Costs and Latency with Prompt Caching on Amazon Bedrock</h1>
<p>Amazon Bedrock has announced the general availability of prompt caching, a feature designed to significantly reduce response latency and lower costs. Leveraging prompt caching can lead to up to an 85% reduction in latency and up to a 90% decrease in costs by storing frequently used prompts and reusing them across multiple API calls.</p>
<h2>How It Works</h2>
<p>Prompt caching works by caching frequently used prompts, which helps in optimizing the processing of input tokens. This is particularly beneficial across various use cases, including:</p>
<ul>
<li><strong>Chat with documents</strong></li>
<li><strong>Coding assistants</strong></li>
<li><strong>Few-shot learning</strong></li>
</ul>
<h2>Supported Models</h2>
<p>The prompt caching feature supports multiple models available on Amazon Bedrock, such as Anthropic’s Claude 3.5 Haiku and Claude 3.7 Sonnet. This ensures wide applicability for different types of generative AI applications.</p>
<h2>Monitoring and Performance Tracking</h2>
<p>To manage and optimize the effectiveness of your prompt caching strategies, Amazon Bedrock integrates with monitoring tools like Amazon CloudWatch. These tools help track the performance metrics of cached prompts, assisting you in achieving the best possible outcomes in terms of speed and cost-efficiency.</p>
<h2>Security and Responsible AI</h2>
<p>Amazon Bedrock’s prompt caching feature is built with integrated security, privacy, and responsible AI usage considerations to ensure that your data remains safe and your applications adhere to ethical AI practices.</p>
<p>By utilizing prompt caching on Amazon Bedrock, developers and organizations can significantly enhance the efficiency of their AI applications, making it a crucial capability in the landscape of generative AI.</p>


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Amazon Bedrock has introduced prompt caching, which can reduce latency by up to 85% and costs by up to 90%. This feature caches frequently used prompts, improving efficiency for generative AI applications like coding assistants and few-shot learning. It supports various models and integrates with Amazon CloudWatch for performance monitoring. The feature also focuses on security and ethical AI usage.