Hi everyone! 👋 We built AI Supercharger to help AI agents use fewer tokens without changing your existing workflow. It works as a gateway between your app and the LLM, reducing token usage by 65–95% with minimal impact on response quality. We're launching today and looking for feedback from developers building AI agents and LLM applications. We'd love to hear your ideas, feature requests, or honest criticism. Thanks for checking it out! 🚀

👋 Hi Product Hunt! Thanks for checking out AI Supercharger. We started building it after noticing that many AI agents and LLM applications spend far more tokens than necessary. Existing solutions often require rewriting prompts, changing application logic, or switching models, which isn't always practical. Our goal was simple: build a gateway that developers can place between their application and the LLM to automatically optimize token usage while preserving response quality. In our internal testing, AI Supercharger reduced token usage by 65–95%, depending on the use case, with little to no noticeable impact on output quality. This is only the beginning. We're launching today to learn from real developers and real use cases. If you build AI agents, chatbots, or LLM-powered products, we'd love to know: Does this solve a real problem for you? What features would make it more useful? What concerns would stop you from using it? Every suggestion, question, and piece of criticism helps us improve. Thank you for your support—we're excited to hear what you think! 🚀

The gateway approach is smart, dropping token usage without touching existing code makes adoption painless. Curious how you handle edge cases where the compressed context drifts from the original intent.

Curious how you handle cases where the LLM output depends on those exact tokens to stay coherent, do you run benchmarks against specific tasks or is the 65 to 95 percent savings pretty consistent across use cases?

AI Supercharger

Cut LLM token costs by up to 95% without sacrificing quality

Cut LLM token costs by up to 95% without sacrificing quality