Shepherd – open-source tools to manage self-hosted software deployments

Hacker News

Hey HN, I wanted to share something I’ve been working on. My startup previously built B2B SaaS, and large enterprises sometimes wanted to self-host it for data residency reasons - but this was a nightmare to build and support. I chatted to other builders/founders and it seems this pain is pretty common. But people tolerate it because the biggest customers often require self-hosting. So we’ve built and open-sourced Shepherd to make it easier to manage these self-hosted deployments. It’s MIT licensed, and provides: 1 - Simplified deployment with a UI to create and manage Helm charts, for easy deployment to customer Kubernetes clusters 2 - Automated deployment upgrades to improve version consistency and provide rollbacks 3 - Deployment health monitoring Try it and please share any feedback: Github repo - https://github.com/contextco/shepherd Demo video - https://www.youtube.com/watch?v=pelVQx_zHJc Website - https://trustshepherd.com/

Visit product View on Hacker NewsJan 21, 2025

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

84%84% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

79%79% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: context, open · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, builder · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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