Python development services
Python is where our backend, data and AI work meets. It is the shortest path from a working idea to a service in production, and the ecosystem behind it is the reason most machine-learning work starts here.
Where we use Python
Python is part of the stack on these 5 services. Each page covers how we work, what you get and what it costs to start.
Python in practice
Python’s advantage is not the language itself — it is that almost every problem outside pure web serving has a mature Python library. Data processing, scientific computing, imaging, scraping, and every major AI SDK are first-class here, which is why work that touches those domains tends to land in Python regardless of what the rest of the stack is.
For web work it holds up well: Django and FastAPI are both excellent for different shapes of project. The known caveat is the global interpreter lock, which means CPU-bound parallelism needs processes rather than threads. In practice web workloads are bound by the database and external calls long before the interpreter becomes the constraint.
What we build with Python
Django and FastAPI services behind web and mobile products, including the admin tooling the client team actually uses day to day.
Scheduled jobs that move, clean and reconcile data between systems that were never designed to talk to each other.
Model integration, retrieval pipelines and inference services — the layer between a model and a product feature people can use.
Is Python right for you?
Ask usA good fit when
- Backends that also do data or AI work
- Automation, integration and scheduled processing
- Products where a scientific or imaging library decides the language
- Teams that value readable code a newcomer can review
Probably not when
- CPU-bound parallel workloads without redesigning around processes
- Hard real-time or latency-critical systems
- Teams with a strong JVM or .NET estate and no Python operations experience
What we run alongside Python
The rest of the setup, and why each piece is there. We keep this list short on purpose — every dependency is something someone has to maintain.
- FastAPI or Django
- FastAPI for lean typed APIs, Django when you want the full framework and an admin.
- Pydantic
- Validation and settings with types that hold at runtime, not just in the editor.
- uv or Poetry
- Dependency and environment management that is reproducible across machines and CI.
- Celery or RQ
- Background jobs, so slow work never sits in a request.
- pytest
- The testing standard, and pleasant enough that coverage actually gets written.
- Ruff
- Linting and formatting fast enough to run on every save.
Why Python
Let’s talkUnmatched library coverage
Data, scientific computing, scraping, imaging and every major AI SDK are first-class here, so less has to be built from scratch.
Readable by people who are not its authors
Python code reviews well, which matters when a project changes hands or a client team takes it over.
Fast from idea to running service
A prototype can become a deployable service without the language getting in the way of the change.
What we get called in to fix
Get a second opinionDependency environments nobody can reproduce
Projects that build on one machine and fail on another. A lockfile and a container end the conversation.
Blocking calls in async code
A synchronous library inside an async endpoint, stalling the whole event loop.
Untyped code at scale
Large codebases with no type hints, where every refactor is archaeology. Adding hints incrementally pays quickly.
Notebook code in production
Analysis code promoted straight to a service. It usually needs restructuring, tests and error handling before it can be trusted.
Python or the alternative
The comparisons we are actually asked to make, answered the way we would answer them on a call.
Python when data, AI or scientific libraries are involved. Node when the frontend is JavaScript and sharing code and people matters more.
Django for the full framework and an admin. FastAPI for typed, documented APIs where the rest would be weight.
Go for throughput and single-binary deployment. Python when developer velocity and library access dominate.
Python works well with:
Node.js
React
Vue
Angular
AWS
Azure
Go
Google Cloud
Java
JavaScript
LaravelGot an idea? Let’s make it real.
Tell us the short version
This could be the first step towards a new and successful collaboration. A one-line idea and a finished spec are both fine — tell us the problem, the deadline you’re working to and what’s in your way.
Keep looking
Frequently asked questions
Django when you want batteries, an admin and a mature ORM. FastAPI when the job is a lean, typed, well-documented API surface.
For the vast majority of web workloads, yes — the bottleneck is nearly always the database or an external call, not the interpreter.
Yes. Our AI integrations, retrieval pipelines and computer-vision work are predominantly Python.
A currently supported release, and we say so explicitly in the project setup. Running an end-of-life Python is a security position, not just a version number.
Almost certainly. Scaling problems in Python web apps are nearly always database access patterns, not the interpreter.