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Cortisol - Accurately forecast log costs pre-production | Product Hunt

 10 months ago
source link: https://www.producthunt.com/posts/cortisol
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Cortisol

Accurately forecast log costs pre-production

An open-source and easy to use command-line tool that provides cost estimation and forecasting for main observability tools like Datadog, New Relic, Grafana and GCP Cloud Logging, helping users plan and optimize their log costs pre-production. 💰📉.
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Have you ever found yourself in a position that the cost of logs was a double digit of your total cloud costs?
What a great initiative to help users plan and optimize their log costs with your tool. The focus on cost estimation and forecasting is crucial in today's tech landscape. Could you share some insights into how users can seamlessly integrate this command-line tool into their existing workflow with these observability tools?
@rickoliver Hey Rick, thanks a lot for your comment. To answer your question, in 2 ways: 1) during the early development phase, the developer will run this on their terminal to get quickly a log cost estimate. It's going to be used as an early guide. 2) As part of CI/CD phase, where they can set up an upper limit for log cost. If the new code change violates this upper limit, then the code change is not deployed. I hope this answered your question
Man, this will help us alot. We always had cost estimation issues for logs

It appears there might be a mix-up in terminology. "Cortisol" typically refers to a hormone produced by the adrenal glands in the human body. It plays a crucial role in various physiological functions, including regulating metabolism, reducing inflammation, and controlling the body's response to stress.

Cortisol has no direct connection to forecasting log costs in a pre-production context. The term you might be looking for in the context of cost estimation or production forecasting is likely something different, such as "cost forecasting" or "production cost estimation."

If you have a specific question or need information related to forecasting costs or production in a particular industry or context, please provide more details, and I'd be happy to help with that.


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