Comparison of LLM Prompt Caching: Cloudflare AI Gateway, Portkey, and Amazon Bedrock

 Unlocking faster, smarter, and more cost-effective LLM (Large Language Model) performance has become a priority for businesses building AI-powered applications. In this detailed Comparison of LLM Prompt Caching: Cloudflare AI Gateway, Portkey, and Amazon Bedrock, we explore three cutting-edge solutions designed to optimize API latency, manage token costs, and boost prompt response efficiency.



Each platform offers unique strengths:


Cloudflare AI Gateway provides robust observability and caching closer to the edge, ideal for latency-sensitive apps.


Portkey stands out with its user-friendly interface, granular analytics, and flexible caching layers tailored for different LLMs.


Amazon Bedrock, integrated deeply with the AWS ecosystem, offers native prompt caching that works seamlessly with foundational models like Anthropic Claude and AI21.


This blog compares these services across key dimensions: performance, observability, cost savings, ease of integration, and real-time metrics. Whether you're a startup experimenting with OpenAI or an enterprise deploying multi-modal AI agents, understanding these caching strategies can drastically reduce operational overhead and speed up inference times.


At AntStack, we help teams build production-grade, serverless AI architectures that scale. This analysis will guide your choice of the best caching layer to power intelligent applications more efficiently.

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