Flask vs Django vs FastAPI: Choosing the Right Python Web Framework

One of the things I really like about Django is how cleanly it handles CRUD, from database models to forms, views, and templates, everything fits together in a very natural way. It’s a great framework for building real, production level backends without overcomplicating things. Fast to develop, structured by default, and powerful when you need to scale. . . . #Django #Python #WebDevelopment #BackendDevelopment #SoftwareEngineering #BuildInPublic

The evolution of Python backends. 🚀 For the longest time, the choice was binary: Do you want the simplicity of Flask or the heavy-lifting power of Django? But FastAPI has changed the conversation entirely. The big advantage FastAPI brings isn't just that it is faster (though it is). It’s that it brought Type Safety and asynchronous programming to the forefront of Python web dev. - Flask is great for flexibility and learning. - Django is unbeatable for rapid enterprise development. - FastAPI is the bridge to modern, high-concurrency needs (like AI models). It feels like we finally have a "Big Three" that covers every possible use case perfectly. #SoftwareEngineering #Python #Coding #TechTrends #BackendDeveloper

🚀 Python Web Dev 2026: Best Frameworks, Use Cases & Tips Python continues to dominate modern web development with its speed, scalability, and powerful frameworks. From startups to enterprise platforms, Python enables secure, high-performance, and future-ready web applications. This guide covers: ✅ Best Python web frameworks in 2026 (Django, FastAPI, Flask) ✅ Django vs FastAPI vs Flask comparison ✅ Real-world Python web development use cases ✅ Best practices for scalable & secure apps Build faster, smarter, and more scalable web platforms with Python. 🔗 Read the full blog: https://lnkd.in/d9-ETinu #Python #PythonWebDevelopment #Django #FastAPI #Flask #BackendDevelopment #WebDevelopment #ScalableApps #Tech2026 #Codism

My Python RAG pipeline choked at 50 concurrent users. So I ripped out the orchestration layer and rebuilt it in Node.js. Unpopular opinion: Python is the king of training. But for serving? It’s too heavy. When you move from a Jupyter notebook to real-world WebSockets, things break. I didn't just need inference. I needed: • To handle 1,000+ concurrent embeddings. • Non-blocking streams. • Zero serialization headaches. Python’s GIL (Global Interpreter Lock) fought me every step. Node’s event loop ate the load for breakfast. The new stack: 1. Training: Python (obviously). 2. API/Orchestration: Node.js + TypeScript. 3. Vector DB: Pinecone. The result? 40% lower latency and no thread-blocking nightmares. Use the right tool for the layer, not just the language you learned first. What is the biggest bottleneck in your current stack? #VectorDatabase #RAG #Javascript

I recently revised Django from basics to advanced and converted my handwritten notes into a clean PDF , Sharing this with the community in case it helps anyone who’s learning Django or revising concepts for interviews. Topics covered include Django architecture, models, views, templates, ORM, authentication, DRF, and deployment basics. Feel free to download, share, and give feedback 🚀 #Django #Python #WebDevelopment #LearningInPublic #BackendDevelopment

🚀 New Post in my #TechnicalScripter2026 Series! In this write-up, I compare Flask vs Django, two powerful Python frameworks, and discuss how developers can choose the right one based on project requirements and scalability needs. Choosing the right framework plays a key role in building efficient backend applications. 🔗 Read the post here: https://lnkd.in/g38YMU8j Looking forward to your thoughts and feedback! #Python #Django #Flask #BackendDevelopment #WebDevelopment #TechnicalScripter2026

How to make your Python Docker container slimmer 🐳👇🏼 If you’ve ever deployed Python containers only to watch them balloon to several GBs, you’re not alone — I once hit a 5 GB Python image (thank you, PyTorch dependencies 😅). But with a few simple optimizations, you can dramatically reduce image sizes, speed up deployments, and lower cloud costs. Here are practical tips that have worked well: 1️⃣ Choose a Lean Base Image Start smaller: ✔️ python:3.x-slim instead of the full Python image ✔️ python:3.x-alpine if compatible with your dependencies (and you are a brave developer 😆) Smaller base = smaller final image and faster pulls. 2️⃣ Use Multi-Stage Builds (Big Win!) Multi-stage builds let you install tools and build dependencies in one stage, then only copy what’s needed into a clean final image. This can reduce the size by 60-90% compared to single-stage builds. 3️⃣ Minimize Layers Every RUN / COPY / ADD adds a layer. ➡️ Combine related commands with && to reduce layers and eliminate intermediate files (although you won't gain much size reduction). Example: RUN apt-get update && \ apt-get install -y build-deps && \ pip install -r requirements.txt && \ apt-get purge -y build-deps && \ rm -rf /var/lib/apt/lists/* Fewer layers = smaller image. 4️⃣ Use .dockerignore to Cut Build Context Exclude unnecessary files (.git, logs, IDE folders, virtual environments) so Docker doesn’t send them into the build context — this keeps images lean and speeds up builds. 5️⃣ Install Only What You Need Separate dev dependencies from production deps. Use flags like --no-dev or careful requirements files so you install only what your app actually needs at runtime. 💡 Why it matters: smaller images → faster CI/CD → less storage & network cost → reduced attack surface → happier developers. If you want to learn more, please check my tutorial about multi-stage build: https://lnkd.in/gHTpdt2S #mlops #docker #devops #python

💡Django tip Subqueries: #python #subqueries #orm When you need to perform more complex filtering or calculations, Django’s ORM supports subqueries and conditional expressions, allowing you to take your queries to the next level. A subquery allows you to embed one query inside another. This is useful when you need to fetch related data conditionally. This query fetches each customer and adds a latest_order_date field that contains the date of their most recent order. #tip #tips #tipoftheday #djv_mo #100daysofcode

Code the Web. Power It with Python. HTML gives structure. CSS gives style. JavaScript gives interaction. But Python? Python gives logic, automation, data, and intelligence. This is where the web stops being static and starts becoming smart: • APIs that actually scale • AI features that solve real problems • Backends that connect everything together I stopped asking “Which language should I learn next?” and started asking “What can I build with what I know?” That shift changed everything. If you’re learning web development: 👉 Don’t chase tools 👉 Build projects 👉 Let Python power the web you create What’s one thing you’ve built (or want to build) with Python + Web? Drop it in the comments 👇

JavaScript devs call them Promises. Python devs call them Futures. When people mention async/await, most of us immediately think of JavaScript. But Python has had the same idea for a long time just with a different name and ecosystem. In Python, asynchronous work is built around Futures and the 𝙖𝙨𝙮𝙣𝙘𝙞𝙤 event loop. A Future represents a value that will be available later very similar to how a Promise works in JavaScript. What really clicked for me: • async functions don’t execute immediately • Calling it returns a coroutine object (a task waiting to be executed) • That coroutine is scheduled and wrapped as a Future • await simply pauses execution until that future is resolved • The event loop decides when and what runs next Same concept. Different language. Different syntax. Once you understand Futures, async code in Python stops feeling “magical” and starts feeling predictable. This understanding came up when I started learning WebSockets and Django Channels, and I’ll be sharing more about that in the next post. Learning async isn’t about syntax it’s about understanding how time and execution are managed. #Python #AsyncProgramming #Futures #BackendDevelopment #SoftwareEngineering #EventLoop #Promise #asyncio