What ChatGPT doesn’t say about Kubernetes in production

This post was originally published on Info World

Like many technology organizations, when ChatGPT was publicly released, we wanted to compare its answers to those of a regular web search. We experimented by asking technical questions and requesting specific content. Not all answers were efficient or correct, but our team appreciated the ability to provide feedback to improve responses.

We then got more specific and asked ChatGPT for advice using Kubernetes. ChatGPT provided a list of 12 best practices for Kubernetes in production, and most of them were correct and relevant. But when asked to expand that list to 50 best practices, it quickly became clear that the human element remains extremely valuable.

How we use Kubernetes

As background, JFrog has run its entire platform on Kubernetes for more than six years, utilizing managed Kubernetes services from major cloud providers including AWS, Azure, and Google Cloud. We operate in more than 30 regions globally, each with multiple Kubernetes clusters.

In our case, Kubernetes is primarily used to run workloads and runtime tasks rather than storage. The company employs managed databases and object storage services provided by cloud providers. The Kubernetes infrastructure consists of thousands of nodes, and the number dynamically scales up or down based on auto-scaling configurations.

JFrog’s production environment includes hundreds

Read the rest of this post, which was originally published on Info World.

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