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Open Source Cloud Costs Monitoring in Docker

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Open Source Cloud Costs Monitoring in Docker

OpenCost is an open source implementation for Kubernetes cost monitoring and now cloud cost monitoring for AWS, Azure, and GCP. The project makes all of this data accessible via an API and user interface. While discussing the idea of running OpenCost on platforms besides Kubernetes we realized that with this new Cloud Costs feature there are users who want API access to their cloud billing data without needing to run on Kubernetes. I opened the Issue OpenCost without Kubernetes #2268 and as luck would have it, we had our internal Hackathon last week. If you're not familiar with OpenCost, it's the open source CNCF project for monitoring Kubernetes and cloud spending. It's a Golang implementation of the OpenCost Specification for monitoring Kubernetes cloud costs. It has an optional web UI and you can also run it as a Prometheus metrics exporter. The code is all at https://github.com/opencost/opencost and you can learn more about the project at https://opencost.io

Visit productView on Hacker NewsNov 21, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: user, dock, new · Missing: mac, agents, macos
88%88% 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.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
74%74% 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: users · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface, users · Missing: plus, intuitive, reviews
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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