Welcome back to TechTalks with Manoj.

Over the past two decades, we’ve witnessed some of the biggest shifts in software architecture—from physical servers to virtualization, from cloud computing to containers, Kubernetes, microservices, and serverless computing.

Each wave fundamentally changed how we design, build, and operate enterprise applications.

Today, we’re entering another transformation.

But this time, it isn’t just about infrastructure.

It’s about intelligence.

Many organizations believe building an AI application is as simple as connecting to a Large Language Model through an API.

In reality, that’s only a small piece of the puzzle.

Production-ready AI platforms require entirely new architectural layers—AI gateways, prompt management, model routing, Retrieval-Augmented Generation, vector databases, agent frameworks, guardrails, observability, governance, and cost optimization.

The language model is important, but it is not the architecture.

In this episode, we’ll explore the complete Enterprise AI technology stack through the lens of a Cloud Architect.

We’ll compare traditional cloud applications with modern AI platforms, examine how Azure and AWS services map to each architectural layer, and discuss the design decisions that separate successful enterprise AI implementations from expensive proof-of-concepts.

Whether you’re a software engineer, cloud architect, engineering manager, CTO, or technology leader preparing for the next generation of enterprise systems, this episode will help you build a practical mental model of how production AI platforms are designed.

So, let’s explore The AI Tech Stack Explained for Cloud Architects.

Let’s get started. 🎙️

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