Original Summary

For the past few years I built mobile apps the hard way: idea, build, launch, crickets. No market research, no keyword research. The screenshot above is our Play Console: 17 apps, and most of them sit under 100 installs. Only a couple got real traction (5.5k and 657). Looking back, the data would have told us which ideas were worth building before we wrote a single line of code. The tools that have this data cost $70–100/month , which felt steep for an indie dev who mostly needed a handful of features. So I built my own with AI agents (Claude Opus). Within the first week I had data on around 5 million apps across the App Store and Google Play. After that I: Trained a custom machine learning model to estimate app revenue Built an ASO keyword research tool Set up a daily pipeline for scraping, storing, and analyzing the data Built an MCP server so I can query all of it from Claude in plain English Now we’re going back through our apps with real keyword and market data, which is why most of them were updated in the past week. The changes based on the new research have already improved our results, and for the first time we actually know why each app is or isn’t working. Running a pipeline this size for just our team felt wasteful, so two weeks ago we opened it up for free with every feature unlocked. We have 150+ users so far, almost all organic from social media. We also tried a small Meta ads campaign to test interest, but the targeting was rough. Here’s my dilemma: servers and the database cost us $200–300/month, and that grows with users. I’d like to keep it free or very cheap, but I’m not sure that’s realistic for something this data-heavy. For those of you running data-heavy SaaS: Did you go freemium, cheap flat pricing, or usage-based? Has anyone found that very low pricing ($5–10/mo) makes people trust the data less?   submitted by   /u/ImSohelKabir01 [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SaaS
  • 发布时间：2026/10/9 04:20:53