As AI-powered tools continue to revolutionize industries, there’s a hidden cost that’s often overlooked: input token costs. For developers building GPT wrappers, these costs can add up quickly, eating into profit margins and slowing down inference. I’ve been working on a prompt compression system to help mitigate this issue, and I’ve noticed a surprising trend: many people aren’t too concerned about input token costs. But for input-heavy products like AI study helpers, these costs can be crippling. So, I want to ask: if you’re building something with ChatGPT, Gemini, or Claude APIs, what’s the most pressing issue you’re facing? Is it the cost of input tokens, or something else entirely?
It’s clear that output tokens are more expensive, but I believe that input tokens can have a significant impact on financials, especially for products that rely heavily on user input. By understanding the pain points of developers, we can start to build better solutions that address these costs and improve the overall efficiency of AI-powered tools.