FastAPI Surpasses 1M+ Requests Per Second in Benchmarks

Ever wondered if Python could finally ditch its GIL shackles and go toe-to-toe with Go for screaming-fast backends? Spoiler: In 2026, it did. 🚀 Let's break it down with the latest from the trenches. First off, Python 3.14 made no-GIL mode production-ready, unlocking true multicore parallelism in FastAPI apps. We're talking 2-5x speedups for CPU-bound tasks like data crunching in microservices. The catch? You'll need to refactor for race conditions, and memory might spike, but it means architects can stick with Python's rapid dev cycle without jumping ship to Go for scalability. On the FastAPI side, version 1.0 dropped with native async support for Python 3.12, slashing context-switching overhead and delivering 20-30% lower latency in I/O-heavy APIs. It's a game-changer for high-throughput systems, making it competitive with Go's goroutines. Trade-off: Migrating sync code gets messier, with more debugging time upfront. Go isn't slacking either. Go 1.22 brought built-in WebAssembly support, letting you compile backends to run at near-native speeds in edge or serverless setups. It crushes FastAPI in cold starts by up to 50%, thanks to static binaries ditching interpreter baggage. Downside? Steeper curve for Wasm tweaks, but it's gold for hybrid cloud-edge architectures. And if you're picking sides, Uber's 2026 benchmark update shows Go edging out in raw throughput (15% better RPS in high-concurrency spots), but FastAPI wins big on dev velocity—30% faster feature rolls with its ecosystem. Go shines for ops efficiency, Python for quick innovations. ⚡ What's your take? Building high-performance backends—do you lean FastAPI for speed-to-market or Go for raw power? Drop your stack stories below. 👇 #FastAPI #Golang #PythonBackend #Concurrency #Microservices

𝗦𝘁𝗼𝗽 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆 𝗯𝗮𝗰𝗸𝗲𝗻𝗱 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘄𝗮𝘆. FastAPI isn't just "another Python framework." It's a deliberate choice — and knowing when to reach for it matters more than knowing how to use it. 𝗣𝗶𝗰𝗸 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 𝘄𝗵𝗲𝗻:  • You're building ML/AI-powered APIs and your team already lives in Python  • You need async performance without the boilerplate of Go or Java  • Auto-generated docs (Swagger/OpenAPI) aren't a nice-to-have — they're a requirement  • You want type safety that actually catches bugs before production 𝗦𝘁𝗶𝗰𝗸 𝘄𝗶𝘁𝗵 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗯𝗮𝗰𝗸𝗲𝗻𝗱𝘀 (𝗦𝗽𝗿𝗶𝗻𝗴, 𝗗𝗷𝗮𝗻𝗴𝗼, 𝗘𝘅𝗽𝗿𝗲𝘀𝘀, .𝗡𝗘𝗧) 𝘄𝗵𝗲𝗻:  • Your org already has deep expertise and infra around them  • You need battle-tested ORM support and a massive plugin ecosystem  • You're building monoliths where convention-over-configuration saves months 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗮𝗻𝘀𝘄𝗲𝗿? 𝗜𝘁'𝘀 𝗻𝗲𝘃𝗲𝗿 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸. 𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. FastAPI shines where speed-to-deploy, async I/O, and Python-native ML pipelines intersect. Forcing it into a legacy enterprise CRUD app is like using a scalpel to chop wood. Choose your tools like an engineer, not a fan. Thoughts? When did FastAPI click (or not click) for you? #FastAPI #Python #BackendDevelopment #SoftwareEngineering #WebDevelopment #APIDevelopment #TechCommunity #Programming #MLOps #SystemDesign

🚀 Built & Deployed a FastAPI REST API Excited to share that I’ve been working on building high-performance REST APIs using FastAPI! 🔹 Designed scalable API endpoints 🔹 Implemented CRUD operations 🔹 Integrated request validation using Pydantic 🔹 Ensured high performance with async support 🔹 Tested endpoints using Postman FastAPI makes backend development faster, cleaner, and more efficient compared to traditional frameworks. Currently exploring deployment strategies and integrating APIs with AI/LLM-based applications 🤖 #FastAPI #RESTAPI #BackendDevelopment #Python #APIDevelopment #AI #MachineLearning

🚀 𝗙𝗹𝗮𝘀𝗸 𝘃𝘀 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 If you have worked with Python for backend development, you have probably come across Flask and FastAPI. Both are powerful, but they serve slightly different purposes depending on your use case. 🔹 Flask is a lightweight and flexible micro-framework. It’s been around for years and has a huge community. You get full control over how you structure your application. However, that flexibility comes at a cost — you often need to write more boilerplate code and manage things like validation and async handling manually. 🔹 FastAPI, on the other hand, is relatively newer but built for modern APIs. It leverages async programming and type hints, making it incredibly fast and developer-friendly. ⚡ Why is FastAPI faster? FastAPI is built on Starlette (for async support) and Pydantic (for data validation). It uses asynchronous request handling, which allows it to process multiple requests efficiently without blocking the server. 🐢 Why is Flask slower? Flask is primarily synchronous. While you can use async with Flask, it’s not its core strength. For high-concurrency applications, this can become a bottleneck. 🧠 When to use Flask? 1. Small to medium projects 2. Simple APIs or web apps 3. When you need flexibility and full control ⚡ When to use FastAPI? 1. High-performance APIs 2. Microservices architecture 3. Real-time or async-heavy applications 4. When you want automatic validation and documentation 𝗦𝘂𝗺𝗺𝗮𝗿𝘆 - Flask is like a blank canvas — simple and flexible. FastAPI is like a smart toolkit — optimized and ready for scale. Both are great — the choice depends on your project needs, not just speed. #Python #FastAPI #Flask #BackendDevelopment #WebDevelopment #APIDesign #SoftwareEngineering #Programming #Developers #TechCommunity #CodingLife #LearnToCode #AsyncProgramming

If you're building backend systems in Python—especially APIs for AI applications—you already know FastAPI is an absolute game-changer. But beyond the raw speed, the engineering concepts behind its design are what make it my go-to framework for modern backends: 1️⃣ Strong Typing & Validation: Thanks to Pydantic, data validation goes from being an imperative headache to a clean, declarative process. You catch errors right at the entry point. 2️⃣ Native Async Support: Handling I/O bound tasks, database queries, or external calls to LLMs becomes incredibly efficient with native async and await. 3️⃣ Dependency Injection: Honestly, one of my favorite features. It makes sharing database connections, enforcing security rules, and writing isolated unit tests incredibly straightforward. 4️⃣ Automatic Documentation: Getting OpenAPI (Swagger) and ReDoc generated automatically drastically reduces the friction between backend and frontend teams. The image below shows how it compares with other popular frameworks. It forces you into good development habits by design. For the Python devs out there, what is your favorite feature of FastAPI? #FastAPI #Python #Backend

We added a Go runtime to kagent and the numbers are wild 🚀 When we built kagent — our Kubernetes-native framework for AI agents — we noticed something: every declarative agent was spinning up a full Python runtime just to glue together an LLM and some MCP tools. So we built a Go runtime. Same agent definitions, same tools, same A2A protocol — just a different engine. The results: 📦 Image Size: 29.7 MB vs 335 MB (11x smaller) ⚡ Startup Time: 2.7s vs 18.2s (6.7x faster) 💾 Memory (idle): 7 Mi vs 253 Mi (36x less) That memory number is the one that matters at scale. 20 agents on Python = ~5 GB in runtime overhead. The same 20 agents on Go? About 140 Mi. The best part? Switching is a single line in your Agent CRD: Python isn't going anywhere — it's still the right choice for custom agent logic, code execution, and framework integrations. But for declarative agents defined through CRDs? Go gives you the same behavior at a fraction of the cost. Full write-up with all the details 👇 https://lnkd.in/d8AH4hwG #Kubernetes #AI #AIAgents #CloudNative #Golang #Python #DevOps #OpenSource #CNCF #kagent

AI can write code — but it can’t design your system Recently, I worked on a small project — a housing price prediction platform — to explore how different parts of a system come together. The setup was simple: 💠 A Python service running an ML model 💠 A Java backend handling APIs and market data 💠 A Next.js frontend combining everything into one portal While building this, I also experimented with using AI tools to generate parts of the code. But one thing became very clear: 👉 AI helps you build faster 👉 But it doesn’t replace thinking The real work was in: 💠 Deciding how services should communicate 💠 Separating responsibilities between layers 💠 Designing clean data flow across frontend and APIs For example, instead of calling the ML service directly from the frontend, I routed everything through the backend layer. That small decision made the system cleaner and easier to manage. So even though AI helped with implementation, the important part was still: understanding the system and making the right design decisions. I wrote a detailed breakdown of this project and what I learned here: 🔗 👉 https://lnkd.in/giAsVGt2 GitHub repo: https://lnkd.in/gMYbdCba Curious how others are using AI in development — especially when balancing code generation vs system design. #SoftwareArchitecture #AI #WebDevelopment #FullStack #DeveloperExperience

📣 SynapseKit just hit 1.0.0 A few weeks ago this was an idea. Today it's a production-grade Python framework that ships with everything you need to build real LLM applications without the complexity that usually comes with it. Here's what 1.0 looks like: ⚡ Async-native from day one - not retrofitted, not a wrapper. Every API is async/await first. 🌊 Streaming-first - token-level streaming across all 15 providers, identically. 🪶 2 hard dependencies - numpy[NumPy] and rank-bm25. Everything else is opt-in. What's inside: 🔌 15 LLM providers behind one interface : swap models without rewriting a line 🔍 18 retrieval strategies : from basic vector search to Self-RAG, Adaptive RAG, HyDE, FLARE 🤖 3 multi-agent patterns : Supervisor, Handoff Chain, Crew 🛠️ 32 built-in tools : search, code, files, databases, APIs, arXiv, PubMed, GitHub and more 🔗 MCP client and server : native Model Context Protocol support 📊 Built-in RAG evaluation : Faithfulness, Relevancy, Groundedness metrics out of the box 🔍 Full observability : OpenTelemetry tracing, TracingUI dashboard, auto-trace every LLM call 🛡️ Production guardrails : PII detection, content filters, topic restrictors 🤝 A2A protocol : agents that discover and talk to each other across services 🖼️ Multimodal : images and audio, automatic format conversion across providers 1,011 tests. 2 dependencies. Apache 2.0 license[ApacheCon - ASF Events]. Built in the open. No VC. No team. No marketing budget. Just engineers who thought the Python LLM ecosystem deserved something better. Thank you to every contributor, every person who opened an issue, every engineer who cloned it at 11pm to try something. This is yours too. This is 1.0.0 The stable foundation. Everything from here gets built on top of it. ⚡ pip install synapsekit==1.0.0 #Python #AI #LLM #RAG #OpenSource #MachineLearning #Agents #MCP #BuildInPublic #SynapseKi

𝗧𝘄𝗼 𝗪𝗮𝘆𝘀 𝘁𝗼 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗲 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗗𝗷𝗮𝗻𝗴𝗼 — 𝗪𝗵𝗶𝗰𝗵 𝗜𝘀 𝗖𝗹𝗲𝗮𝗻𝗲𝗿? When you're just starting out with Django APIs, manually building your response dict feels natural.   You're in control. You know exactly what's going out. It works.   Then your response grows. Fields are added. Nested relationships appear. Validation logic creeps in. 𝗔𝗻𝗱 𝘀𝘂𝗱𝗱𝗲𝗻𝗹𝘆 𝘁𝗵𝗮𝘁 𝗺𝗮𝗻𝘂𝗮𝗹 𝗱𝗶𝗰𝘁 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗳𝗲𝗲𝗹 𝘀𝗼 𝘀𝗶𝗺𝗽𝗹𝗲 𝗮𝗻𝘆𝗺𝗼𝗿𝗲.   Look at the two approaches in the image. Same data being returned. Two very different amounts of code.   The manual approach gives you full control, useful for simple, one-off responses or when you need something very custom.   The serializer approach handles validation, nested data, and read/write logic out of the box and scales cleanly as your API grows.   What I've learned after building production APIs:   ➝ For simple, internal endpoints manual dicts are fine. Don't over-engineer. ➝ For public APIs or anything with validation, serializers will save you significant time. ➝ The real power of DRF serializers shows up when you need to handle POST and PUT, not just GET.   𝗧𝗵𝗲𝗿𝗲'𝘀 𝗻𝗼 𝘀𝗵𝗮𝗺𝗲 𝗶𝗻 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗺𝗮𝗻𝘂𝗮𝗹 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵. Most of us did. The key is knowing when to make the switch.   𝗪𝗵𝗲𝗻 𝗱𝗶𝗱 𝘆𝗼𝘂 𝗺𝗮𝗸𝗲 𝘁𝗵𝗲 𝗷𝘂𝗺𝗽 𝘁𝗼 𝘀𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗲𝗿𝘀 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗳𝗶𝗻𝗮𝗹𝗹𝘆 𝗽𝘂𝘀𝗵𝗲𝗱 𝘆𝗼𝘂 𝘁𝗼 𝗱𝗼 𝗶𝘁? #Django #DRF #Python #BackendDevelopment #SoftwareEngineering #API

Asynchronous APIs ... Simple. If you've built with Flask, Starlette's mental model is familiar—routes, requests, responses. What changes is the foundation: Starlette is async-first, which means your server handles many requests concurrently without spawning threads. In Day 1, we go from zero to: ✓ A running local server with Uvicorn ✓ Two working API endpoints (GET routes) ✓ A POST handler that reads JSON and creates new tasks Plus: why async matters, why the async/await pattern is everywhere in Starlette, and what the request object actually gives you. No complex setup. Just fresh virtual environment + pip install starlette uvicorn + 30 lines of code that's already production-shaped. → Read the full Day 1 (of 5) Article here: https://lnkd.in/gRNJdhaz #Starlette #Python #ASGI #BackendDevelopment #APIDevelopment #Tutorial #linkedin