---
title: "How should I measure OpenAI prompt caching savings in a multi-tenant app?"  
description: "How should I measure OpenAI prompt caching savings in a multi-tenant app?"  
author: "Ruslan Galiev"  
published: 2026-10-18  
updated: 2026-10-18  
canonical: https://www.mindstick.com/forum/162586/how-should-i-measure-openai-prompt-caching-savings-in-a-multi-tenant-app  
category: "AI API Cost Management"  
tags: ["OpenAI API", "Prompt Caching", "Cost Monitoring"]  
reading_time: 1 minute  

---

# How should I measure OpenAI prompt caching savings in a multi-tenant app?

Our app sends OpenAI requests with a long, shared instruction block followed by tenant-specific settings and the user's message. I'm trying to estimate whether prompt caching will make a meaningful difference to our bill, rather than just improve a few carefully arranged test requests.

The awkward part is traffic: some tenants send requests every minute, while others might send two in an afternoon. Their settings also change occasionally.

### What would make the measurement realistic?

- Should I compare cached input tokens per request, or calculate a cost-weighted figure across a full day?
- How should I account for changes to the shared instructions and tenant-specific settings?
- Which request details should I record to distinguish cache behavior from ordinary variations in response length?

I'd appreciate advice on **measuring prompt caching savings** using API usage data. I don't want to assume that identical text is always eligible for a cache hit, or that one busy tenant's results represent everyone else.


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Original Source: https://www.mindstick.com/forum/162586/how-should-i-measure-openai-prompt-caching-savings-in-a-multi-tenant-app

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