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See the impact on your cloud costs as you code

Hacker News

See the impact on your cloud costs as you code

Hey folks, my name is Owen and I recently started working at a startup ( https://infracost.io/ ) that shows engineers how much their code changes are going to cost on the cloud before being deployed (in CI/CD like GitHub or GitLab). Previously, I was one of the founders of tfsec (it scanned code for security issues). One of the things I learnt was if we catch issues early, i.e. when the engineer was typing their code, we save a bunch of time. I was thinking … okay, why not build cloud costs into the code editor. Show the cloud cost impact of the code as the engineers are writing it. So I spent some weekends and built one right into JetBrains - fully free - keep in mind it is new, might be buggy, so please let me know if you find issues. It is check it out: https://plugins.jetbrains.com/plugin/24761-infracost I recorded a video too, if you just want to see what it does: https://www.youtube.com/watch?v=kgfkdmUNzEo I'd love to get your feedback on this. I want to know if it is helpful, what other cool features we can add to it, and how can we make it better? Final note - the extension calls our Cloud Pricing API, which holds 4 million prices from AWS, Azure and GCP, so no secrets, credentials etc are touched at all. If you want to get the same Infracost goodness in your CI/CD, check out https://www.infracost.io/cicd

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Hacker NewsStrong engagement from HN community · Strong signals: ide, 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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