Prompt Caching for Anthropic and OpenAI Models: Building Cost-Efficient AI Systems

Prompt Caching for Anthropic and OpenAI Models: Building Cost-Efficient AI Systems What is Prompt Caching? How Prompt Caching Works Advantages of Prompt Caching 1. Major Cost Reduction 2. Reduced Latency 3. Improved Scalability Common Use Cases Where Prompt Caching Helps Retrieval-Augmented Generation (RAG) AI Troubleshooting Systems A Realistic Production Prompt Caching Architecture Cached Prefix Dynamic Portion Production Prompt Structure Example Production AI System Cost Comparison Prompt Caching with Anthropic Models (via DigitalOcean) Mixed Cached and Non-Cached Content Tool Output with Cache Control Anthropic Relay Example Cache Configuration Prompt Caching with OpenAI Models (via DigitalOcean) Chat Completions Example Responses API Example Example Usage Response Production Use of “prompt_cache_key” and “prompt_cache_retention” Cost efficient LLM deployment with DigitalOcean About the author Try DigitalOcean for free Related Articles Scaling Autonomous Site Reliability Engineering: Architecture, Orchestration, and Validation for a 90,000+ Server Fleet How DigitalOcean’s Agentic Inference Cloud powered by NVIDIA GPUs Achieved 67% Lower Inference Costs for Workato DigitalOcean Gradient™ AI GPU Droplets Optimized for Inference: Increasing Throughput at Lower the Cost By Satyam Namdeo Updated: March 17, 2026 9 min read Large Language Models (LLMs) have become a foundational component for modern AI applications, from developer copilots and documentation assistants to advanced troubleshooting tools. As these applications scale, one challenge quickly becomes apparent: token costs can grow rapidly when large prompts are repeatedly sent to the model. A common architecture for production AI systems includes long system instructions, tool schemas, retrieved knowledge base documents, and conversation history. These components can easily add thousands of tokens per request. When applications handle thousands or millions of requests per day, repeatedly processing the same static prompt content becomes expensive. To address this problem, prompt caching has emerged as an essential optimization technique supported by major model providers such as Anthropic and OpenAI. Prompt caching allows repeated prompt segments to be reused across requests, significantly reducing both latency and cost. In this article, we will explore: What prompt caching is and how it works How Anthropic and OpenAI implement caching The billing implications and cost advantages Real-world use cases A realistic production architecture that can reduce token costs by 70–90% We will also show how prompt caching can be implemented when using models via DigitalOcean. Prompt caching is a mechanism where large portions of a prompt that remain identical across requests are stored and reused , instead of being reprocessed every time.