AW

AWS Cloud Template for SaaS Products (payments, auth, RAG, etc.)

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AWS Cloud Template for SaaS Products (payments, auth, RAG, etc.)

I've been coding a small initial platform on AWS in my spare time. I've set up IaC, CI/CD dev/prod, management (sso, billing alerts), backend, frontend, api, database, auth etc. I've also added a simple RAG bot and payments with two popular payment providers. For each SaaS setup, I pay $2-4 per month in AWS cloud costs, and it is mostly CI/CD and an entry to AWS Secret Manager. Architecture: https://saasconstruct.com/blog/the-tech-stack-of-a-simple-sa... Docs: https://saasconstruct.com/documentation/get-started There are few people who bought it to save time on development costs, so I thought I'd ask some opinions here on what to add or fix if that's ok. Questions: Do you think it is a viable thing? Or maybe some features would be nice? I was thinking about expanding the RAG there, like adding memories of previous conversations.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: coding · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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