Isn’t Python slow for production?

Slower than compiled languages at raw computation, yes. In practice most applications spend their time waiting on databases and networks, where the language barely matters. When something genuinely is computation-bound, we move that piece rather than argue.

Can Python work alongside our Node backend?

Yes, and it is a common setup. Node handles the user-facing API while Python takes the data or model work behind it, communicating over HTTP or a message queue. Each language does what it is good at instead of one being stretched to cover both.

Do we need Python for AI features?

For anything involving your own models or data, effectively yes. If you only need to call an existing service like a commercial language model API, any language can do that, and we will tell you when that is the cheaper and more sensible route.

Can we hire dedicated Python developers?

Yes. Alongside full project delivery, we place dedicated Python developers who work inside your team, follow your process, and report to your leads. It suits companies with an existing roadmap that need capacity rather than direction.

Python
Development Services

Stateside Digitals provides Python development services for web applications, data work, and AI features, covering both the everyday backend and the parts of your product that need something more specialized.

Python is a general-purpose language best known for being readable, which sounds minor until you are the one paying someone to understand code they did not write. It reads close to plain English, and that keeps the cost of changing an application years later much closer to the cost of building it than most languages manage.

It is also the language the data and machine learning world settled on. Nearly every serious library for analysis, forecasting, computer vision, and language models is written for Python first, and often only for Python. If your product will ever touch those areas, Python ends up in your stack whether you plan for it now or discover it later on.

First Choice For Data

Analytics And AI Included

The libraries behind forecasting, recommendations, and language models are all Python native, so nothing has to be rebuilt to use them.

Quick To Change Course

Built For Shifting Requirements

Less ceremony around each change means a new requirement mid-project costs days rather than the rewrite a stricter language demands.

One Language, Many Jobs

Web, Data, And Automation

The same team can build your API, the reporting behind it, and the scripts holding your operations together, without switching tools between them.

AI And ML Features

Recommendations, document processing, forecasting, and chat interfaces, built into a product rather than bolted on beside it.

Data Pipelines And Reporting

Moving information between the systems your business runs on, cleaning it along the way, and turning it into reporting your team trusts enough to make decisions from.

Internal Automation

The repetitive work someone currently does by hand every week, replaced by something scheduled that does not forget or make mistakes.

Nearly every meaningful machine learning library is Python-first: PyTorch, scikit-learn, TensorFlow, and most of the tooling around large language models. If your product needs recommendations, document processing, forecasting, image recognition, or anything built on an LLM, that piece is written in Python regardless of what the rest of your stack runs.

That does not mean the whole product should be. A common and sensible arrangement is a Node.js or Java application serving your users, with a Python service handling model work and returning results. We build both sides of that line, and we are just as willing to tell you a feature needs a well-chosen API rather than a model of your own to train and maintain.

We build on current Python with type hints throughout, using Django where a project needs an admin and conventions out of the box, and FastAPI where it needs a lean, well-documented API. PostgreSQL handles data, Celery and Redis handle background work, and pandas, scikit-learn, or PyTorch come in when the problem calls for them. Everything ships in Docker to AWS or Google Cloud.

Python
Django
FastAPI
PostgreSQL
Celery
Redis
pandas
scikit-learn
PyTorch
Docker
AWS
Google Cloud

Typed Python, Not Loose Scripts

Reproducible Environments

Data Handled Responsibly

Models Kept Versioned

Type hints and tests are there from the start, so the code that ends up load-bearing was written to be load-bearing.

Dependencies are pinned and the environment is defined in the repository, so what runs in production is what was tested, not an approximation of it.

Access, retention and anonymisation are decided while the pipeline is designed, rather than after somebody asks where the data went.

Model versions, training data and results are tracked, so a change in behaviour can be traced to a cause instead of guessed at.

Typed Python, Not Loose Scripts

Reproducible Environments

Data Handled Responsibly

Models Kept Versioned

Django suits products needing an admin interface, user management, and structure decided for you. FastAPI suits APIs where speed and clean documentation matter more than built-in features. Many projects use Django for the app and FastAPI for a service beside it.

Python
Development Services