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GitHub Cost Visibility and Optimization

Hacker News

GitHub Cost Visibility and Optimization

Hi HN, I'm Co-Founder & CEO of Vantage: a cloud cost observability and optimization platform. We just recently launched support for Github Cost Support within Vantage to view costs for Actions, Shared Storage and Copilot. We've heard from people that it's been difficult to view GitHub costs; the billing APIs provided insufficient granularity, and the generated CSVs required manual download and review. Often, people did not track their GitHub usage at all throughout the month, and they looked at their bill only at the end of the month. Lastly some people that used self-managed runners, including those that run on Kubernetes or on virtual machines, found it difficult to associate these compute costs with their overall GitHub spend. Allocating build costs by repository, user, or organization was not possible through the GitHub-provided tooling without time-consuming and manual effort. Users can grant Vantage access to their GitHub billing data by inviting a Vantage-owned email address as a billing administrator to their Enterprise. This billing user allows Vantage to download the detailed usage CSV for maximum billing granularity for GitHub Actions, Storage, and Copilot. For example, customers can see per-user, repository, and workflow-level billing for Actions builds, as well as the detailed usage report for GitHub Enterprise, which enables tracking the license usage of Enterprise members. Here's a video to see this in action and we'd love to hear your feedback: https://www.youtube.com/watch?v=npyZQRlTuGY

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, user, email · Missing: agents, macos, agent
90%90% 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
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
66%66% 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: video, month, users · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
46%46% 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
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.

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