We

We made cloud security cost less than your team's pizza run ($30/scan)

Hacker News

We made cloud security cost less than your team's pizza run ($30/scan)

Hey HN, My co-founder (Riyaz) and I built a hosted cloud security scanner for solo devs and small teams who can't justify expensive cloud security products. We've noticed teams are often hesitant to use cloud providers security tools due to fear of unexpected billing surprises. After years of securing cloud infrastructure and applications, we noticed a gap: teams either struggle with complex provider tools or rely on open-source scanners running from laptops. While we used to recommend open source solutions, we realized the main challenge wasn't the tools themselves but maintaining consistent security scanning workflows. We built Kloudle to make finding and fixing security issues simple, going beyond basic compliance checklists. Our pay-per-scan model ($30/scan if you have >5 issues, first scan free) eliminates the burden of unused subscriptions. Some technical details: - Built on Google Cloud using serverless and containers for scanning - Custom ELT pipelines for misconfig evaluation - Comprehensive support for AWS, GCP, K8s, and DigitalOcean (383 security issues as of the last count) - Exploring adding support for emerging providers (like Zadara) Interesting insights from our journey: - We discovered DigitalOcean was popular among small teams, so we built comprehensive security checks. Their security team was super friendly and we donated our entire list of checks for to them to kickoff building a foundational CIS Benchmark for DigitalOcean - Our users found creative workarounds for missing features (like using catchall emails for team access) - We handle support between founders across different time zones, covering ~18 hours daily As a small team (<10 people), we've kept our infrastructure lean, passing the cost savings to users. While we're still adding features (team plans are coming!), we're focused on keeping things simple - including passwordless sign-in. I handle the marketing efforts and act as the product manager. Riyaz has his hands full with making sure we keep adding new configuration security checks and evaluate how we can simplify onboarding of existing cloud providers for our users. Our challenges include not enough paying customers giving us feedback or are willing to rate us on review websites. But the situation is slowly improving. While many product folks tell us that if the users love the product they will definitely tell others, it is possible that not everyone is open about the fact that their cloud account had many security issues. Happy to answer questions or hear your feedback! If interested in looking at the product or even running a scan just browse to kloudle(dot)com.

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Actual performance

4points
2comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, user · Missing: mac, agents, macos
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, lua, open source · Missing: https docs, excited, just released
72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: google, users, scanner · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, friendly, users · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
20%20% 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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