Rompli – Simple Docker Hosting

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Hi everyone! I built a hosting platform called https://www.rompli.com. It can host any docker container that listens on HTTP. The web app is built entirely in meteor and therefore has first class support for meteor applications (sticky sessions, websockets, ssl). How much does it cost? It is currently in beta which means two things. 1. It's free. 2. You may only have up to five (5) containers. What do I get for one container? One container is 512MB memory with 0.5 ECU compute. You also get single click coordinated rolling updates, and single click scaling. How do I deploy my meteor app with Rompli? https://medium.com/@isohaze/how-to-deploy-a-meteor-js-application-on-rompli-com-48c775cae8c0#.izzu3693p How do I use a custom domain name? https://medium.com/@isohaze/how-to-use-your-own-domain-on-rompli-89f8c6c91d96#.kwvbzbodi Is there a FAQ? Yes: https://www.rompli.com/faq What is the tech stack? The webapp is written entirely in meteor@1.3.4. The container orchestration is implemented with [Docker Swarm](https://docs.docker.com/swarm/). Nginx is used for loadbalancing and I have an Elastic Load Balancer in front of the nginx servers. Why is it named Rompli? It got the name because docker containers work really well in groups and a group of otters is called a romp. Also the underlying technology is using [The Raft Consensus Algorithm](https://raft.github.io/) and a large group of otters is also known as a raft. I hope you try it out and give me feedback! Thanks, Kris

Visit product View on Hacker NewsJul 30, 2016

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

77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: dock, single, using · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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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