The AI world has gone all-in on Python. Every new library, every fancy framework, every overhyped tutorial assumes you’re neck-deep in Jupyter notebooks. If you’re not using Python, people look at you like you just showed up to a Formula 1 pit stop with a screwdriver and duct tape.
And yet, I’m still using Node.js.
Not because I’m a contrarian. Not because I hate Python (though I kind of do). But when you’re building actual usable systems—not just playing around with models—Node is faster, leaner, and frankly, more sane.
Python Owns the Models. I Don’t Care.
Let’s get this out of the way: if you’re training models, Python wins. No contest. PyTorch, TensorFlow, Hugging Face… it’s all Python, and it’s all brilliant at what it does.
But that’s not what I’m doing.
Most of the time, I’m not training models. I’m building the glue: the scaffolding around models. I’m building job queues, microservices, Google Sheets integrations, dashboards, schedulers, and API gateways. I’m moving data, triggering actions, managing retries, feeding and cleaning tokens, pushing predictions into useful places.
That’s where Node.js shines. Python’s great for ML research. Node’s better for real-world deployment, especially when your project isn’t a Kaggle competition but something that actually has to run in production.
Async That Actually Works
Python’s async ecosystem is duct-taped together with asyncio, trio, aiohttp, and a dozen other libraries—most of which break the minute you plug in something not built for it. You’re constantly stuck in coroutine hell.
Node? Async is the default. You don’t have to fight the language to handle concurrent API calls or run non-blocking tasks. It just works.
If you’re trying to hit the OpenAI API with throttling, batch calls to the Amazon SP API, and push everything into Sheets, Node handles it natively and elegantly. No weird async wrappers. No “await this or everything dies” rituals.
NPM Is the Real Powerhouse
Say what you want about the JavaScript ecosystem, but NPM is still king when it comes to gluing stuff together. There’s a package for literally everything. You can go from idea to prototype in an hour and into production in a week, without writing every goddamn utility function from scratch.
Python? Sure, there’s pip, and it mostly works—until some deep dependency breaks on Ubuntu 22.04 because someone’s repo hasn’t been touched since 2018.
JSON All the Way Down
Every API I touch—OpenAI, Amazon, Shopify, Notion, you name it—speaks JSON. Node speaks JSON natively. It’s literally JavaScript. There’s no translation layer. No JSON.parse / dumps dance. No weird type coercion issues. Just clean, fast integration.
Python makes you jump through hoops. I don’t have time for that.
If I Need Python, I’ll Call It
I’m not religious. If I need to run a Python script to use a specific model or parse something weird, I’ll spawn a child process or call it via an API microservice.
But the rest of the stack? Node.
Why? Because:
- It’s fast.
- It’s async-native.
- It handles real-world, production-grade tasks without a fuss.
- And I’ve got a decade of JS under my belt. Why throw that away just to chase trends?
Build With What Actually Works
There’s a weird pressure in the tech scene to do what’s “expected.” Like using Python is somehow more serious, more academic, more real.
I don’t buy that.
You know what’s real? Building stuff that works. Shipping features. Automating workflows. Running jobs reliably without praying to the asyncio gods.
Node.js isn’t trendy in AI circles. It just gets the job done.
And that’s exactly why I’m still using it.
