Let’s talk about Starlette, the web framework that sounds like a sparkly intergalactic pop star but is, in fact, a blisteringly fast ASGI toolkit for building web services in Python. It’s lightweight. It’s opinionated-but-chill. And it doesn’t bring a truckload of dependencies to your backyard barbecue.
What Is It?
Starlette is a high-performance ASGI (Asynchronous Server Gateway Interface) framework/toolkit designed for building async web apps and services in Python. If Flask is your chill friend who brings snacks, and Django is your overachieving cousin who shows up with a business plan, Starlette is the fast-talking minimalist who rides in on a scooter with a laptop, builds you a microservice in 10 minutes, and vanishes before the pizza arrives.
Relevant? Still?
Oh yes. Starlette is like that one classic meme that refuses to die — not only is it still relevant, but it’s also foundational. It powers FastAPI, which is basically the web framework if you’re working with AI and APIs these days. So yeah, Starlette is the scaffolding behind the scene while FastAPI hogs the spotlight. Think of it as the sound engineer at a Beyoncé concert. Invisible. Essential.
Pros (AKA: Why You Should Swipe Right)
- Blazing fast — Seriously, benchmarks show it can serve thousands of requests per second without breaking a sweat. It’s like the caffeine of web frameworks.
- Modular — Bring your own tools. Starlette doesn’t get in your way or try to define your life choices.
- Async-first — Everything is designed around Python’s
async/await. Say goodbye to thread-induced sadness. - Testable — Comes with a test client that makes mocking endpoints smoother than jazz.
Cons (AKA: The Quirky Baggage It Carries)
- Too minimalist for some — If you’re looking for batteries-included like Django, Starlette says, “Bring your own AAAs.”
- Steeper learning curve for newcomers — Especially if you’re not used to async/await. You might accidentally summon Cthulhu via coroutine.
What’s It Used For?
Starlette is ideal for high-performance APIs, microservices, and async web apps. Basically, anything that needs to be lightweight and wicked fast. Want to build an API for a machine learning model without the framework becoming your co-founder? Starlette.
A Small, Beautiful Example
Here’s the world’s tiniest web app:
from starlette.applications import Starlette
from starlette.responses import JSONResponse
from starlette.routing import Route
async def homepage(request):
return JSONResponse({'hello': 'world'})
app = Starlette(routes=[
Route("/", homepage),
])
That’s it. That’s the tweet.
Who Made It?
Starlette was created by Tom Christie, also known for Django REST Framework. The man clearly has a thing for clean, powerful tools — and, possibly, stress-testing Python.
Who Uses It?
- FastAPI
- Robusta
- Litestar
- Countless custom internal tools at companies that want FastAPI-style speed with lean control
How Popular Is It?
According to GitHub, Starlette has 10.8k+ stars and is actively maintained. It’s not trending on TikTok, but it’s definitely trending in engineering Slack channels. It’s often overshadowed by FastAPI, but make no mistake — engineers who want to optimize performance or customize deeply often go straight to the source.
Up or Down?
Still going up — especially in AI and data-heavy environments where async processing is king. Also, with Python’s ongoing improvements to asyncio, Starlette just keeps aging like a fine artisanal YAML file.
Alternatives?
- FastAPI — Actually built on Starlette, but more batteries included
- Sanic — Also async, and also fast, but with a slightly different vibe
- Flask — Sync-first, but a classic choice
- Django — Bring-a-ladder level framework, great if you want the whole cathedral
Does It Work Well With AI?
Yes. Ridiculously well. Especially if you’re using Hugging Face Transformers or TensorFlow models in production APIs. It’s like Starlette was built for low-latency inference endpoints. Use it to expose your model with speed and swagger.
What Tech Stack Does It Work With?
Starlette plays well with:
- Uvicorn — The go-to ASGI server (https://www.uvicorn.org/)
- SQLAlchemy + Databases — For async DB access
- Jinja2 — For those rare times you want to render HTML like it’s 2009
- Pydantic — Not built-in, but if you’re going FastAPI-style, you’ll want it
Cool Tidbits
- Comes with built-in support for GraphQL via
starlette.graphql(though Graphene has moved slower than my Roomba on a shag carpet) - Has built-in sessions, CORS, GZip middleware, static file support, and background tasks — because who needs external dependencies when you can just be awesome?
- Uses the ASGI spec, so it works great with WebSockets, HTTP/2, and even long-polling (if you’re into that kind of thing, weirdo)
Final Thoughts
Starlette is like the IKEA of web frameworks — clean, modern, occasionally confusing to assemble, but incredibly satisfying once it’s done. Whether you’re building a real-time dashboard for monitoring satellite squirrels or a lightning-fast ML inference API, Starlette won’t let you down.
Follow me for more hot takes and deep dives into tech with the energy of a caffeinated duck on a mission. Got thoughts? Rants? Confessions of Flask-to-Starlette migration trauma? Drop them in the comments — I read everything.
Art Prompt:
Impressionist landscape using short, rapid brush strokes and high-key colors; depict a tranquil river flowing through a meadow under soft golden afternoon light, with distant trees shimmering in pastel tones; the reflection in the water should be gently rippled, capturing fleeting light effects; evoke a dreamy, almost ethereal atmosphere with an emphasis on natural beauty and movement; painted in the expressive, layered style of a 19th-century French master