Himanshu `’s Post

FastAPI vs Flask (Practical Comparison) FastAPI vs Flask — When Should You Choose Which? After working on production APIs using both, here’s my practical take: 🔹 Flask Minimal, flexible micro-framework Great for small to medium apps Large ecosystem More manual setup for validation & docs 🔹 FastAPI Built-in request validation (Pydantic) Automatic OpenAPI/Swagger docs Async support out of the box Higher performance (ASGI-based) In my experience: ✔ Flask works well for lightweight internal tools ✔ FastAPI is better for high-performance, scalable REST APIs ✔ FastAPI reduces boilerplate significantly For backend systems handling high traffic + strict schema validation, FastAPI has been my go-to. What’s your preference for production APIs? #Python #Backend #FastAPI #Flask #RESTAPI #SoftwareEngineering

Excited to share a short demo of a Todo Management REST API I built using FastAPI 🚀 This project helped me apply what I’ve been learning about backend development and API design. 🔹 Features implemented: • RESTful APIs with FastAPI • User authentication and authorization using JWT • Secure password hashing • CRUD operations for todos • Database integration using SQLAlchemy and SQLite Building this project gave me hands-on experience with structuring APIs, handling authentication flows, and connecting backend logic to a database. Looking forward to improving it further and building more backend projects 💪 #FastAPI #Python #BackendDevelopment #RESTAPI #SQLAlchemy #JWT #LearningByDoing

I just migrated backend of a major project I'm working on from Flask to FastAPI. Same endpoint. Same logic. Completely different developer experience. Flask version: Manual input validation Manual error handling Manual JSON serialization Manual Swagger setup try/except blocks standing guard like overworked security FastAPI version: 4 lines. Type hints. Return statement. That’s it. What’s actually happening: - user_id: int validates automatically - Invalid input → 422 response (automatic) - Response models serialize clean JSON (automatic) - OpenAPI docs generate themselves (automatic) - Async support without duct tape - No magic. Just smart design around Python’s type system. This isn’t just “less code.” It’s fewer edge cases, fewer hidden bugs, better performance, and APIs that document themselves. In simple terms, Flask feels like: “Here are the tools. Build carefully.” FastAPI feels like: “I already installed the guardrails. You’re welcome.” This is what “work smarter, not harder” looks like in API architecture. If you’re building APIs in Python, this difference is huge. #Python #FastAPI #BackendDevelopment #SoftwareEngineering #APIDevelopment #backendMigration #FlaskVSFastAPI #Flask

Been deep in a FastAPI backend project and something bugged me — the code I was writing looked different from the official docs. So I dug into it. Turns out FastAPI pushed some massive updates recently; 1. Python 3.9 support? Gone. Pydantic v1? Gone. The entire documentation was rewritten for Python 3.10+ syntax. What used to be: from typing import Optional name: Optional[str] = None Is now just: name: str | None = None Cleaner. No imports needed. 2. .dict() became .model_dump(). 3.  .from_orm() became .model_validate(). 4. JSON responses got 2x faster automatically through Pydantic's Rust serializer. 5. They added native streaming with yield — perfect for AI applications. Spent tonight updating 12 files across my project. Every old pattern eliminated. Lesson: documentation changes. Check the release notes regularly. What you learned 6 months ago might have a newer, better way now. #FastAPI #Python #BackendDevelopment #WebDev

After learning Flask, I’m now diving into FastAPI — and the difference is exciting. 🚀 FastAPI gives me built-in data validation, automatic API docs, async performance, and cleaner, more scalable architecture out of the box. Even better, by using Jinja2 templates with FastAPI, I can still build full web applications — not just APIs — combining modern backend performance with traditional server-rendered pages. Best of both worlds. The animated gif shows a blog web application I am building as I follow the excellent Python FastAPI Tutorial  series on YouTube created by Corey Schafer. Source code: https://lnkd.in/eaNkVYHb #FastAPI #Flask #Python #WebDevelopment #APIDevelopment #BackendEngineering #SoftwareEngineering #LearningJourney

Its been 4 days since I started exploring FastAPI, and it’s been a really interesting learning curve 🚀 Coming from a Django background, the biggest shift was the mindset — FastAPI is more explicit, async-first, and pushes you to think clearly about architecture and request flow. What I’ve covered so far: FastAPI basics & request lifecycle Path, query, and body handling using type hints Pydantic models, validation & response models Dependency Injection (Depends) Injecting DB, config, and auth logic Protected endpoints Production-style JWT authentication Challenges faced: Properly understanding async vs sync Grasping dependency execution order JWT auth felt more manual than Django at first but now the clarity and control make sense FastAPI is clearly growing fast, and I can see why — it’s clean, scalable, and fits modern backend and AI/LLM use cases really well. Looking forward to going deeper and getting fully ready with FastAPI 💪 #FastAPI #Python #BackendDevelopment #APIs #LearningJourney #SoftwareEngineering

● Why Async Matters in FastAPI (And When To Use It) While building APIs, I realized something important: Not all endpoints should be written the same way. FastAPI supports both synchronous and asynchronous functions — but using async correctly can significantly improve performance. 🔹 What’s Happening Here? •The request waits for an external API •Instead of blocking the thread, async allows other requests to be processed. •This improves concurrency under high traffic. 🔹 When Should You Use Async? Use async when: •Calling external APIs •Querying databases (with async drivers) •Performing I/O operations 💡 WHY THIS MATTER Efficient backend systems are not just about writing endpoints. They’re about handling multiple users without slowing down. Understanding async is crucial for building scalable APIs. Backend performance is architecture + execution. #BackendDevelopment #FastAPI #Python #APIDesign #ScalableSystems

Combine Django with Rust-powered engine makes the server faster, So we are obviously obligated to escape to Fast-API yet 😁 . #Django #Python #backEndDevelopment #WebDevelopment

FastAPI 0.131.0 shipped a silent performance monster. I ran the numbers. For those unfamiliar — FastAPI is one of the most popular Python web frameworks for building APIs. It's fast, async-native, and built on top of Pydantic for data validation. It's become the go-to choice for ML/AI backends, microservices, and production APIs across the industry. Now it just got a whole lot faster. JSON serialization now runs on Pydantic's Rust engine. The change? Almost embarrassingly simple. Declare your return type. That's it. FastAPI hands serialization to Pydantic's Rust core instead of Python's json library. ✅ Do this: @app.post("/search") async def search(query: str) -> SearchResponse: return await run_search(query) ❌ Stop doing this: return JSONResponse(content=response.model_dump()) You're converting Pydantic → dict (Python) → JSON bytes (Python). Two slow steps. Let FastAPI + Pydantic handle it in Rust. One step. The benchmarks: 📊 100 search hits, 35KB JSON, 5,000 iterations → Pydantic Rust: 14,372 ops/s → json.dumps (pre-converted): 7,466 ops/s → model_dump + json.dumps (typical): 4,987 ops/s → ~3x faster out of the box 📊 Real-world RAG response — 50 hits w/ 256-dim embeddings + 20 chat messages, 188KB JSON → Pydantic Rust: 1,690 ops/s → Typical pattern: 419 ops/s → 4x faster. 1.8ms saved per request. Bigger payload = bigger gap. #FastAPI #Python #Pydantic #Performance #Backend #WebDev #RustLang

Whether you need to render HTML templates, serve JSON/YAML APIs, or just return plain text, Dyne makes it effortless. 🎨 Dyne features a built-in Jinja2 wrapper for clean template rendering with `resp.html` and an intelligent `resp.media` property that handles content negotiation (JSON/YAML) automatically based on the client's request. Plus, dynamic routing uses familiar f-string syntax to pass arguments directly to your handlers! Build flexible web services: https://lnkd.in/eqEc4Tuw #Python #Jinja2 #WebDev #API #Backend #DyneFramework