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

DevFeed: [Prompt Caching for Anthropic and OpenAI Models: Building Cost-Efficient AI Systems](<https://devfeed.tech/articles/prompt-caching-for-anthropic-and-openai-models-building-cost-efficient-ai-systems-19934.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/prompt-caching-with-digital-ocean>)

Author: Satyam Namdeo

Published: 2026-03-17T19:25:04Z

Content type: article

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Caching](<https://devfeed.tech/topics/caching.md>), [systems](<https://devfeed.tech/topics/systems.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [anthropic](<https://devfeed.tech/topics/anthropic.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [Digital Ocean](<https://devfeed.tech/topics/digital-ocean.md>)

Tags: [anthropic](<https://devfeed.tech/tags/anthropic.md>), [caching](<https://devfeed.tech/tags/caching.md>), [cost](<https://devfeed.tech/tags/cost.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [latency](<https://devfeed.tech/tags/latency.md>), [models](<https://devfeed.tech/tags/models.md>), [openai](<https://devfeed.tech/tags/openai.md>), [production](<https://devfeed.tech/tags/production.md>), [token](<https://devfeed.tech/tags/token.md>)

## AI overview

This article explains prompt caching for Anthropic and OpenAI models. It describes reusing repeated prompt prefixes, including system instructions, tool schemas, retrieved documents, and conversation history, to reduce repeated processing, latency, and token costs in production AI applications.

## Source excerpt

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. What is Prompt Caching? 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. Since information like System instructions, tool schemas, guardrails, documentations ,etc rarely changes, repeatedly sending it wastes computation and increases token usage costs. Prompt caching solves this by: Storing previously processed prompt segments. Reusing those segments when identical requests appear again. Charging a much lower price for cached tokens. This optimization is especially powerful in production systems where large static prompts are combined with small dynamic