Ismael Pedro’s Post

I tried Django, Flask, and finally settled on FastAPI for most of my backend projects. Here’s why 👇 ⚡ FastAPI → Super fast (built on async) → Auto API docs (Swagger / OpenAPI) → Perfect for microservices & AI backends 🧩 Flask → Lightweight & simple → Great for small apps → But needs more setup for production 🏗 Django → Full-featured framework → Good for large monolith apps → But feels heavy for APIs & GenAI use-cases For me, FastAPI fits best when building: 👉 Backend APIs 👉 LLM / GenAI services 👉 Data pipelines & microservices Which one do you prefer for backend — Django, Flask, or FastAPI? 👇 #Python #BackendDevelopment #FastAPI #Django #Flask #APIs

🚀 Backend Is More Than Just CRUD Over the last few years, I’ve worked with: 1.FastAPI 2.Django 3.Django REST Framework 4.Flask 5.Redis & Valkey 6.Celery (Background task processing) And here’s what I’ve learned 👇 Frameworks are tools. Architecture is the real skill. FastAPI taught me async thinking. Django taught me structure and scalability. Flask taught me minimalism and control. Redis/Valkey taught me performance optimization. Celery taught me how to design non-blocking systems. Real backend engineering starts when you ask: 1.What happens under load? 2.What should be async? 3.Where should caching sit? 4.What if a task fails? 5.How do we avoid blocking requests? APIs are easy. Designing reliable systems is the real game. Now focusing more on: 1.Performance tuning 2.Distributed task handling 3.Caching strategies 4.Clean architecture 5.Production readiness Still learning. Still building. 🚀 #Python #BackendDevelopment

We just scaled an analytics dashboard integration for a FinTech client. The core requirement was low-latency data ingestion and rapid API response times under peak load. This immediately brought up the classic Python backend debate: Django vs. FastAPI. Django gives us batteries-included maturity, ORM comfort, and Admin for speed on CRUD-heavy components. It’s our reliable workhorse. But for raw API throughput? FastAPI shines. Its async nature, Pydantic validation, and dependency injection are tailor-made for high-concurrency microservices talking to our Next.js frontends. On this project, we used Django for the core user management/CRM layer and shifted the high-volume data pipelines entirely to FastAPI backed by Supabase for resilience. It's about picking the right tool for the specific job profile, not just philosophical preference. When is Django's structure non-negotiable for your team, even if FastAPI offers better raw speed? I'm curious about your real-world trade-offs. #Python #FastAPI #Django #BackendDevelopment #SoftwareArchitecture

🚀 𝗕𝘂𝗶𝗹𝘁 𝗮 𝗦𝗺𝗮𝗹𝗹 𝗔𝗜 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 (𝗥𝗔𝗚) 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗳𝗼𝗿 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 Recently, I built a 𝗳𝘂𝗹𝗹-𝘀𝘁𝗮𝗰𝗸 𝗔𝗜 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 to explore how 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) systems work in a microservices setup. The goal was purely 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀. The system can 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝘀, 𝗰𝗿𝗲𝗮𝘁𝗲 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀, 𝗮𝗻𝗱 𝗮𝗻𝘀𝘄𝗲𝗿 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝘁𝗵𝗲 𝘂𝗽𝗹𝗼𝗮𝗱𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁. 💻 𝗧𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸: 𝗔𝗜 𝗦𝗲𝗿𝘃𝗶𝗰𝗲: Python, FastAPI, OpenAI, Celery, RabbitMQ, Redis 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲: Go (Gin) for user management & APIs 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱: Next.js, React, TypeScript, Tailwind, Zustand, React Query 𝗜𝗻𝗳𝗿𝗮: PostgreSQL, AWS S3, Docker & Docker Compose This project helped me understand: • RAG pipelines and document processing • Polyglot microservices (𝗚𝗼 + 𝗣𝘆𝘁𝗵𝗼𝗻) • Background workers with message queues • Integrating AI workflows with a modern React frontend 📌 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗥𝗲𝗽𝗼: https://lnkd.in/gJuiy9WJ Feel free to 𝗰𝗹𝗼𝗻𝗲 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼, 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗰𝗼𝗱𝗲, 𝗼𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗶𝘁. Contributions, suggestions, or improvements are always welcome. #AI #RAG #NextJS #Golang #Python #FastAPI #Docker #LearningInPublic #SoftwareEngineering #ReactJs

🚀 Exploring Flask — A Lightweight Yet Powerful Python Web Framework Recently, I started diving deeper into Flask, and I’m impressed by how simple yet flexible it is for building web applications and APIs. 🔹 What is Flask? Flask is a micro web framework in Python that allows developers to create web apps quickly without unnecessary complexity. Its minimalistic design gives full control over architecture and extensions. 💡 Why developers love Flask: ✅ Lightweight and easy to learn ✅ Perfect for REST API development ✅ Flexible project structure ✅ Huge ecosystem of extensions ✅ Great for AI/ML and automation integrations 👨💻 As someone working in automation and software development, I see Flask as a powerful bridge between backend logic, AI models, and real-world applications. Whether you’re building dashboards, APIs, or microservices — Flask makes development fast and efficient. 📌 Currently exploring: • Building REST APIs with Flask • Integrating Python automation workflows • Connecting AI models with web interfaces #Python #AIModels#Flask #WebDevelopment #BackendDevelopment #API #SoftwareEngineering #LearningJourney

I picked up Django out of pure curiosity. No course. No roadmap. Just me, yes Claude(best tool for learning) and a weekend. One week later, I had a working Fraud Detection API. As someone who'd been writing Spring Boot for months... that felt wrong. So I went down the rabbit hole. I did used Spring Boot at Peak Apps to work on an enterprise ERM platform, JWT auth, Redis caching, the works. Solid. Structured. But a lot of time and work to get there. Django just... moved. Fast. Same concepts. Half the code. And because it's Python, my ML libraries were right there. I remember working on YouBid (my final year project). A full Ai powered auction platform. We needed real-time WebSocket bidding AND OpenAI-powered product descriptions. So we used both. FastAPI (Python) for the AI microservice. Spring Boot for the core auction logic. And that's when it clicked. The Django vs Spring Boot debate isn't a debate. It's the wrong question. The right one: what does THIS project actually need? Small team + AI/ML + fast delivery → Django/FastAPI Enterprise scale + large team + type safety → Spring Boot Complex system with multiple concerns → use both I made a full system design guide breaking down exactly when to pick which swipe through What's your go-to stack? Drop it below 👇 #Python #Django #SpringBoot #Java #BackendDevelopment #SoftwareEngineering #SystemDesign #Spring #Springboot #Performance #Optimisations #TechnologyStack

Why People Choose FastAPI Over Django in the Modern Era of APIs Over the years, Django has been the go-to framework for many. It’s powerful, mature, and battle-tested. But when it comes to modern API-first systems, people choose FastAPI, and here’s why 🔹 Performance by Design - FastAPI is built on ASGI, Starlette, and Pydantic, which means: - Non-blocking I/O - True async request handling - Much higher throughput under load For real-time systems, microservices, and high-concurrency APIs — this matters. 🔹 Type Safety = Fewer Bugs - FastAPI fully embraces Python type hints: - Request/response validation is automatic - Errors are caught early - Code is easier to read and maintain API becomes self-documenting and safer by default. 🔹 Automatic API Documentation - With FastAPI, you get built-in Swagger UI, no overhead of documentation. - OpenAPI specs are out of the box, without extra configuration. This dramatically improves collaboration between backend, frontend, and mobile teams. 🔹AI & ML Workflows Feel Natural - Async endpoints for model inference - Easy integration with PyTorch / TensorFlow pipelines - Ideal for LLM, RAG, and real-time inference APIs 🔹Why AI Ecosystems Prefer FastAPI - Async → better GPU/CPU utilization - Lightweight → faster cold starts - Clear schemas → perfect for AI agents & tools - OpenAPI → seamless tool calling for LLMs 🔹 FastAPI vs Django FastAPI - Very fast (async-first) - Type-safe and explicit - Clean API-focused architecture - Excellent developer experience - Perfect for microservices & AI APIs Django (for APIs) - Sync-first by default - Heavier for API-only services - Async support still feels secondary 🔹 When I Choose FastAPI - When I need High-performance APIs - AI / ML inference services - Microservices architecture - Clear contracts & scalability from day one - Django is still great — but FastAPI is built for how we build APIs today. Tools evolve. Choosing the right one is about context, not loyalty. What’s your go-to API framework for 2026? #Python #BackendDevelopment #WebDevelopment #Flask #Django #FastAPI #SoftwareEngineering #APIs #Programming #AI #ML #swagger #team #win #microservices

🚀 𝗙𝗹𝗮𝘀𝗸 𝘃𝘀. 𝗙𝗮𝘀𝘁𝗔𝗣𝗜: 𝗪𝗵𝗶𝗰𝗵 𝗣𝘆𝘁𝗵𝗼𝗻 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗣𝗼𝘄𝗲𝗿𝘀 𝗬𝗼𝘂𝗿 𝗕𝗮𝗰𝗸𝗲𝗻𝗱? 🚀 Choosing the right web framework for your Python backend can significantly impact development speed, performance, and scalability. Today, we're diving into a popular debate: 𝗙𝗹𝗮𝘀𝗸 vs. 𝗙𝗮𝘀𝘁𝗔𝗣𝗜. Both are excellent choices, but they cater to different needs and project philosophies. Understanding their core differences is key to making an informed decision. 𝗙𝗹𝗮𝘀𝗸: 𝗧𝗵𝗲 𝗠𝗶𝗰𝗿𝗼𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗠𝗮𝗲𝘀𝘁𝗿𝗼 • Simplicity & Flexibility: Minimalist design, giving developers full control over components and libraries. Great for small to medium-sized projects or when you need highly customized solutions. • Maturity & Ecosystem: A long-standing framework with a vast community, extensive documentation, and a rich ecosystem of extensions. • Synchronous by Default: Primarily synchronous, though asynchronous capabilities can be added with extensions. • Ideal for: Rapid prototyping, small APIs, web applications where you want to pick and choose your tools. 𝗙𝗮𝘀𝘁𝗔𝗣𝗜: 𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗿𝗻, 𝗛𝗶𝗴𝗵-𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗖𝗼𝗻𝘁𝗲𝗻𝗱𝗲𝗿 • Performance & Asynchronicity: Built on Starlette and Pydantic, offering blazing-fast performance and native asynchronous support (`async/await`). • Automatic Docs: Generates interactive API documentation (Swagger UI, ReDoc) automatically from your code. • Data Validation & Serialization: Pydantic provides robust data validation and serialization out-of-the-box, reducing boilerplate code and errors. • Type Hinting: Leverages Python type hints for better code completion, error checking, and overall developer experience. • Ideal for: High-performance APIs, microservices, data science APIs, and projects where speed and data integrity are paramount. 𝗧𝗵𝗲 𝗩𝗲𝗿𝗱𝗶𝗰𝘁? If you value extreme flexibility and a lightweight core, Flask might be your go-to. If you prioritize performance, built-in features like async support, automatic documentation, and robust data validation, FastAPI is a strong contender. Which framework do you prefer for your Python backend projects and why? Share your experiences and insights! Comment 𝗣𝗬𝗧𝗛𝗢𝗡𝗙𝗥𝗔𝗠𝗘𝗪𝗢𝗥𝗞 to join the discussion! #Python #Flask #FastAPI #BackendDevelopment #WebDevelopment #API #TechComparison #SoftwareEngineering

For the Python developers who's follwing me, that a good post to know which framework use depending on your project!! 🐍

Picking an ORM shouldn't be hard. But it is, even with AI. For Python I love using Django ORM: expressive syntax, migrations, built-in admin. I skip the rest of Django's stack and pair it with FastAPI. Took me a while to land on this. For Typescript, choosing between Prisma, Drizzle and TypeORM is a mess. And some people use Sequelize or Kysely... I wanted to see how they compare side-by-side: same tasks, different ORMs. Couldn't find a good tool for that. So I built one. Inspired by component-party .dev (frontend framework comparisons), but for ORMs. Would love feedback, especially on what comparisons to add next. Link in the first comment.