Open sourcing our GPT-4 caching proxy that reduced our development API costs by 80%
By Holidays in Europe / January 9, 2026 / No Comments / Uncategorized
Introducing Our Open Source GPT-4 Caching Proxy: Cutting Development API Costs by 80%
At the intersection of innovation and efficiency, our team has developed a straightforward yet impactful solution to optimize AI development workflows. Over recent months, my co-founder and I have been experimenting with building AI tools that leverage large language models like OpenAI’s GPT-4 and Anthropic’s API. While these models have revolutionized our capabilities, we encountered an unexpected challenge: mounting API costs during the development process.
The Cost of Development Iterations
Like many developers, we’ve experienced the seemingly endless cycle of testing and debugging prompts. Each time we tweak a prompt, re-run the request, and wait for the response—repeatedly incurring API charges even when the underlying code hasn’t changed. Not only does this inflate our expenses, but it also slows down our development cycle.
The Solution: A Smart Caching Proxy
To address this, we engineered a simple caching proxy that acts as an intermediary between our code and the OpenAI/Anthropic APIs. Its core function is straightforward:
- First Request: When a prompt is submitted, the proxy forwards it to the API and caches the response.
- Subsequent Requests: If the same prompt is sent again—regardless of minor formatting differences—the proxy serves the cached response instantaneously, eliminating redundant API calls and costs.
Enhancing Cache Effectiveness Through Prompt Normalization
A key feature of our proxy is prompt normalization. Developers often copy and paste prompts that may include trailing whitespace or extraneous newlines, which can cause cache misses. By standardizing prompts before caching, we ensure that identical inputs—even if not exactly the same in formatting—hit the cache. This small enhancement resulted in approximately an 11% reduction in token usage, translating into tangible savings on API costs.
Implementation Simplicity
Remarkably, integrating this proxy required only a minor modification to our existing setup:
python
pythonclient = OpenAI(base_url="http://localhost:8000/v1")
This single line reroutes requests through our caching layer without disrupting the standard SDK workflow. The proxy is compatible with existing OpenAI and Anthropic SDKs, making adoption seamless.
Open Sourcing for the Developer Community
After using this tool internally and witnessing significant cost savings and faster response times, we decided to share it with the community. We’ve cleaned up the code and published it on GitHub:
https://github.com/sodiumsun/snackcache
While minimalist—covering caching and prompt normalization—it provides real benefits for AI developers during the iterative development process.
Join the Conversation
We believe this solution can help others reduce friction in AI development, save money, and optimize their workflows. If you’re curious about the implementation details or want to customize it for your projects, we’re happy to answer questions and collaborate.
In summary: a simple, open-source caching proxy has transformed our development experience by drastically lowering costs and accelerating response times. We look forward to seeing how other developers can benefit from this approach.
Note: Always ensure proper handling of sensitive data and API keys when implementing caching solutions.