Python Development Services
Python gets a lot of things right: it reads like pseudocode, it ships fast, and its ecosystem covers everything from web backends to machine learning to automating the tedious stuff your team wastes hours on. Done properly — with the right structure, typing, and testing — it's a serious tool for production software, not just a scripting language for quick hacks.
What we use Python for
Web backends. FastAPI and Django power APIs that serve mobile apps, React frontends, and third-party integrations. FastAPI gives you async performance and automatic OpenAPI docs out of the box; Django gives you a full-featured framework with an admin panel, ORM, and authentication system that's been battle-tested for nearly two decades. We choose based on the project, not the hype cycle.
Data pipelines and ETL. Moving data between systems, transforming it, cleaning it, and making it usable. Python's data libraries — pandas, Polars, SQLAlchemy — are the standard for good reason. We build pipelines that are tested, logged, and monitored, not scripts that silently corrupt data and are discovered three weeks later.
Prototypes that become products. Python is unmatched for getting from idea to working software in days, not weeks. The crucial part — and the part that separates a throwaway prototype from a foundation you can build on — is writing it properly from the start: type hints, tests, clean architecture. We do that so your prototype doesn't need to be rewritten when it turns out to be a real product.
Done properly, not just quickly
The main criticism of Python — that large codebases become hard to maintain — is really a criticism of how Python is often written, not of the language itself. We use mypy for static type checking, pytest for comprehensive testing, Ruff for linting and formatting, and dependency injection patterns that keep modules decoupled and testable. The result is Python code that doesn't make the next developer curse your name.
We structure projects for longevity: clear module boundaries, configuration that doesn't live in the codebase, database migrations that are version-controlled and repeatable, and CI/CD pipelines that run the full test suite and type checker before anything reaches production. Fast to write doesn't have to mean fragile.
Automation that pays for itself
Every business has tasks that someone does manually: copying data from one system to another, generating reports by exporting CSVs and fiddling with Excel, sending templated emails triggered by calendar events, reconciling records between tools that don't talk to each other. These tasks are boring, error-prone, and surprisingly expensive when you add up the hours.
Python is the ideal tool for automating them. A script that takes an hour to write and saves a team thirty minutes a day pays for itself within two weeks — and keeps paying for years. We've automated everything from invoice processing pipelines to multi-step deployment workflows to Slack bots that answer the questions your operations team gets asked five times a day. The hallmark of good automation is that after the first month, people forget it's there — because the problem it solved has simply stopped existing.
FAQ
Isn't Python too slow for production applications?
For most business applications, Python's speed is not the bottleneck — database queries, network calls, and architecture decisions are. A well-structured Python backend can comfortably handle thousands of requests per second. When raw CPU performance matters, Python integrates cleanly with compiled extensions (Cython, Rust via PyO3) or can delegate heavy computation to task queues (Celery, ARQ). For the vast majority of web applications, APIs, and automation work, Python is fast enough — and the speed of development often outweighs the marginal runtime performance gained by switching to a compiled language. If your application genuinely requires sub-millisecond latency at scale, we'll tell you and recommend a stack that fits.
Should I use Django or FastAPI?
It depends on what you're building. Django is a full-stack framework: it gives you an ORM, admin panel, authentication, form handling, and a templating engine out of the box. Choose it when you need a complete application with user management, admin tools, and server-rendered pages — or when you want to move fast without assembling a toolkit from scratch. FastAPI is lighter, async-native, and designed specifically for building APIs. It auto-generates OpenAPI documentation and is excellent for microservices, mobile backends, and systems where you want fine-grained control over components. We often use FastAPI for API-only services and Django for applications that need an admin interface and built-in auth. Both are solid choices; the right one depends on the shape of your project, and we'll help you decide during discovery.
Can you do machine learning and AI work?
We build the engineering infrastructure around ML models rather than training custom models from scratch. That means data pipelines to feed training jobs, APIs that serve model predictions, integration with existing ML platforms (Hugging Face, OpenAI, Azure AI), and the deployment and monitoring that turns a notebook experiment into a reliable production service. If you already have a model or a data science team, we handle the software engineering side so they can focus on research. If you need a custom model trained from scratch, we can assess feasibility and connect you with specialist partners where needed.
What's the ROI of workflow automation?
It depends on what you're automating, but the math is usually straightforward. If a task takes a team member 30 minutes a day and you pay a loaded hourly rate of €35, that task costs about €350 per month in labour alone — not counting errors, context-switching cost, and the morale drain of repetitive work. An automation that eliminates that task typically costs €1 500–€4 000 to build and pays for itself in 4–12 months. After that, it's pure savings. For teams of five or more people, automating even two or three recurring manual processes can recover the equivalent of a part-time hire's worth of productive time — invested once, returned indefinitely. The most overlooked benefit is accuracy: automated processes don't make copy-paste errors, forget steps, or have off days.
Get in touch
Whether you need a backend built, a pipeline untangled, or a manual process turned into a button, the best next step is a conversation. Tell us what you're trying to solve and we'll tell you honestly whether Python is the right tool — and what it would take to build.