Python on Gaurav.dev

How to Choose a Modern Web Stack Without Regretting It

Thu, 24 Sep 2026 00:00:00 +0000

The best technology stack is rarely the one with the longest feature list. It is the one your team can operate confidently while the product changes around it.

Start with constraints

Before comparing frameworks, write down the facts: expected traffic, search requirements, real-time behaviour, integrations, compliance, team experience, delivery date, and operating budget. These constraints eliminate more options than a benchmark ever will.

Next.js is a strong default for content-rich products that also need application behaviour. It offers flexible rendering, a broad React ecosystem, and a straightforward path to full-stack TypeScript. Its flexibility also means the team must be disciplined about caching and server/client boundaries.

Django or FastAPI? Choosing the Right Python API Foundation

Sun, 06 Sep 2026 00:00:00 +0000

Django and FastAPI can both power serious APIs. The more useful question is not which framework is faster; it is which one removes the most risk from your particular product.

Choose Django for an integrated product platform

Django brings an ORM, migrations, authentication, administration, security defaults, and a mature ecosystem. It shines when the API sits inside a business application with users, permissions, editorial workflows, relational data, and operational staff.

Flask, FastAPI, or Bottle: Choosing a Python Microframework

Fri, 24 Jul 2026 00:00:00 +0000

Python gives API teams an unusual amount of choice. Flask, FastAPI, and Bottle can all expose an HTTP endpoint in a few lines, but they optimise for different kinds of simplicity. Choosing well means looking beyond the first route and asking how the service will be validated, tested, deployed, and changed.

Flask: flexible and proven

Flask is the dependable general-purpose option. Its request model is easy to understand, its extension ecosystem is mature, and experienced Python developers can recognise a Flask application quickly. It works especially well for internal tools, integration services, conventional REST APIs, and products that need freedom without inventing the entire HTTP layer.

Building a Python Full Stack for Generative AI Products

Thu, 18 Jun 2026 00:00:00 +0000

An AI product is still a product. It needs authentication, permissions, billing, data ownership, reliable jobs, observable failures, and an interface that helps users judge uncertain results. A practical Python full stack handles those concerns without forcing model code into every layer.

Choose the application centre

Use Django when the product needs an integrated business platform: relational workflows, administration, users, permissions, and substantial back-office operations. Django’s conventions reduce the number of infrastructure decisions and give operations teams useful interfaces early.