In today’s edition, I will walk you through how production-grade container workflows are designed end-to-end.

Here is what you will learn.

We will also look at,

Sad Servers gives you live DevOps scenarios where you debug real problems like high CPU usage, failing services, and networking issues.

No tutorials. Just real troubleshooting.

👉 Try the challenges

One question I get asked constantly is How should a container-based workflow be designed end-to-end for production?

So if you are looking for a guide that teaches you production level docker image build workflow for applications, this guide is for you.

In this crash course, you will learn the following.

👉 Read the Detailed Guide

In this hands-on guide, we will walk you through a complete, production-grade Docker image build and promotion pipeline using GitHub Actions just like how it is done in real enterprise environments.

Here is what is covered in this guide.

👉 Checkout the Detailed Hands-on Guide

Argo CD has become a widely used GitOps tool for Kubernetes. But here is the thing. Knowing how to use it and knowing how it works are two very different things.

By the end of this guide, you will have the clear mental model of the following.

👉 Checkout the Detailed Architecture

AWS now has an EBS Volume Clone feature that lets you copy an EBS volume instantly with a single API call, and no snapshot is required.

By the end of this guide, you will understand,

👉 Checkout the Hands on Guide

A Neo Cloud is a new type of cloud provider built mainly for AI workloads, especially those that need GPUs. You can call it as GPU-as-a-service.

Unlike traditional cloud providers such as AWS, Azure, and Google Cloud, neoclouds concentrate on providing infrastructure specifically for AI, machine learning, and analytics.

Neoclouds currently account for about 17% of AI infrastructure investment, a figure expected to grow to over 30% in the next ten years

Here are the main ones you should know

Here is the reason behind the origins of Neo Cloud.

AI boom created massive demand for GPUs. AI models like LLMs require significant compute power, often running on large GPU clusters. As more companies started building AI products, GPU demand increased rapidly. However, supply could not keep up. This gap is what truly accelerated the growth of neoclouds.

SDD is a workflow where you write a detailed specification first before generating any code. Instead of prompting AI randomly and iterating blindly, you clearly define what needs to be built and use that as the single source of truth.

Here is the core idea.

AI is very good at execution, but not at deciding what to build. SDD separates these concerns. Humans define the “what”, and AI handles the “how”.

Here is an example workflow.

IBM has a project called IaC Spec Kit. It applies SDD to infrastructure (Terraform, cloud infra)

👉 Read this Guide to Know more about SDD

One of the key issues with LLM applications is the repeated use of large prompts. The same instructions and context are often sent again and again, which increases token usage, cost, and response time.

rkt addresses this by acting as a high-performance CLI proxy between your application and the LLM. It optimizes requests, removes redundant data, and caches responses, reducing token consumption by up to 60–90%.

👉 Checkout the Git Repo