We built an open-source LaunchDarkly alternative for B2Bs

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Hey HN, Nir, Gal and Tomer here. Last week, we open-sourced Enrolla ( https://github.com/enrolla/enrolla ) - feature management for SaaS companies. It makes it easy for developers to control how their product behaves for customers in different pricing tiers. So things like which features are enabled for whom, rate limits and seat limits, but also your customer secrets (with end-to-end encryption), and other configurations. After 15 years of working together at various companies, where we rebuilt the same SaaS foundation layer again and again - we wanted to create something reliable and feature-rich that will be available for everyone. We now have a backoffice UI, a backend and SDKs for managing customer features and a way to manage pricing tiers on top of it. We plan to add more features around metering and integration with Stripe, so that ideally Enrolla can be used to bootstrap any new SaaS software in minutes. We’ve launched this repo under the MIT license so any developer can use it. The goal is not to charge individual developers. We make money by charging a license fee for enterprise features like Salesforce/Hubspot and SSO integrations. Give it a try ( https://github.com/enrolla/enrolla ), and let us know what you think! Main website: https://www.enrolla.io

Visit product View on Hacker NewsFeb 7, 2023

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

92%92% 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: stripe, new, open · Missing: mac, agents, macos

91%91% 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

81%81% 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: way · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

21%21% 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 · Strong signals: make money · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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