Talk Python in Production

"Have you ever thought about getting your small product into production, but are worried about the cost of the big cloud providers? Or maybe you think your current cloud service is over-architected and costing you too much? Well, in this episode, we interview Michael Kennedy, author of "Talk Python in Production," a new book that guides you through deploying web apps at scale with right-sized engineering."

"Christopher is a well-known figure in the Python community, active on Bluesky at trudeau.dev, and has authored books and video courses himself. He brings a Django-centric perspective to the conversation, offering thoughtful counterpoints to Michael's Flask and FastAPI experiences. His background includes working with various hosting providers and teaching Python to students, making him an ideal interviewer to draw out practical insights about production deployments."

"What to Know If You're New to Python Before diving into this episode's production-focused content, here are some foundational concepts that will help you get the most from this analysis: Virtual environments are isolated Python installations that keep project dependencies separate - this episode discusses how they compare to Docker containers for development Web frameworks like Flask, Django, FastAPI, and Pyramid are tools for building web applications - the episode covers how to choose between them"

Guidance focuses on deploying web applications at scale using right-sized engineering to avoid excessive cloud costs and over-architecting. Practical recommendations cover choosing hosting providers and simplifying infrastructure so small products can reach production affordably. Comparisons explain when to use virtual environments versus Docker containers during development and how Docker packages enable consistent deployment. Evaluation of web frameworks such as Flask, Django, FastAPI, and Pyramid helps match framework choice to project requirements. Advice emphasizes lessons from long-term operational experience, pragmatic trade-offs, and teaching-oriented, hands-on deployment practices.

Read at Talkpython