Original Summary

Hi Guys, I’m building an AI B2B SaaS application and planning to launch a public beta. It’s somewhat of an AI wrapper, think along the lines of Cursor, where the value comes from automating manual coding. We use LLMs under the hood. My goals with the beta are to validate the product, get real user feedback, and start building a community and traction for future fund raising. The part I’m struggling with is the pricing/usage structure for the launch. I want developers/users to be able to try the product without having to pay upfront, but I also need to control the AI token costs(using open router). We’re bootstrapping, so free usage can become a real expense pretty quickly. I’m considering a couple of approaches: Option 1:Paid/prepaid beta Give users a detailed demo and then require a prepaid subscription that covers the expected AI usage. The problem is that the product is still in beta and currently only produces ~60% of the value I ultimately expect it to. It doesn’t feel right to ask people to pay before the product is mature enough. Option 2: Very limited free usage Give users a small amount of free usage so they can experience the product, then require a prepaid subscription. The concern here is scale. For example, if each developer/user costs me around $5 in AI usage and 1,000 people try the product across the US/EU, that can add up very quickly. Options 3: BYOK We use multiple LLMs for the best outcome. A single BYOK LLM might not produce an impressive output, which will impact community interest. What pricing/usage model have you tried that actually worked? Are there other approaches I’m missing for balancing free experimentation, user acquisition, validation, and AI/API costs during a beta?   submitted by   /u/AggravatingCoyote186 [link]   [comments]

  • 情报分类：开源项目与落地
  • 分类依据：内容涉及项目实践、创业、副业或变现
  • 信息来源：Reddit · SaaS
  • 发布时间：2026/9/28 03:09:40