Languages
Python
The language we reach for when the work is data, models or automation — and we’ll tell you plainly when it isn’t.
Overview
Python is a general-purpose, dynamically typed language whose defining trait is readability: code that looks close to executable pseudocode, with a single obvious way to do most things. That legibility is why it became the default language for anyone who writes software occasionally rather than for a living — researchers, analysts, operations engineers — and why it now sits at the centre of data science, machine learning and automation.
The language itself is unremarkable; the ecosystem is the point. NumPy and pandas turned Python into a serious numerical tool, scikit-learn made classical machine learning routine, and PyTorch and TensorFlow made it the lingua franca of deep learning. On the web, Django gives you a batteries-included framework with an admin and ORM, while FastAPI has become the standard for typed, async, high-throughput APIs. For everything in between — scripting, glue code, one-off data pulls — Python is the tool most teams already have installed.
We build and run Python in production: data pipelines, model-serving APIs, internal automation and the FastAPI or Django services that sit behind them. We are equally direct about its limits. Pure-Python CPU-bound code is slow, the packaging story has sharp edges, and it has no real place on mobile or in the browser. Knowing where those walls are is most of what makes Python worth using.
Best for — Data-heavy back ends, ML and AI systems, and the automation that ties infrastructure together — anywhere the ecosystem does the heavy lifting and raw single-threaded speed is not the constraint.
Why teams choose Python
The ecosystem does the work
From pandas and scikit-learn to PyTorch and the entire modern LLM tooling stack, the libraries you need already exist and are battle-tested. You spend time on your problem, not on rebuilding primitives.
Readable code lasts longer
Python’s syntax makes intent obvious, which matters more than cleverness on systems that live for years. New engineers become productive quickly, and reviews catch real bugs rather than fighting the language.
Fast to a working system
Dynamic typing and a huge standard library mean you can go from idea to running prototype in hours. For data and ML work, where the shape of the problem is uncertain up front, that iteration speed is decisive.
Why businesses choose Python
- Your system is built around data, models or AI, and you want the ecosystem rather than a reimplementation of it.
- You need to move quickly on a problem whose requirements are still shifting, and readable code you can change is worth more than micro-optimised code you cannot.
- Your bottleneck is I/O — network, database, external APIs — where async Python is genuinely fast.
- You want engineers who will offload the hot paths to compiled extensions rather than pretend Python is fast at everything.
What we build with Python
The capabilities this technology is genuinely strong at — and what we most often build with it.
The scientific stack
NumPy for vectorised arrays, pandas for tabular data, and SciPy for numerical methods give Python a numerical core that runs in optimised C and Fortran under a clean Python surface.
Machine learning and deep learning
scikit-learn for classical models, PyTorch and TensorFlow for neural networks. The research world publishes in Python first, so new techniques arrive here before anywhere else.
FastAPI and async
Type-hint-driven request validation, automatic OpenAPI docs, and an async runtime that handles high concurrency for I/O-bound APIs — the modern default for Python services.
Django for full applications
A batteries-included framework with an ORM, migrations, authentication and a generated admin, suited to content-heavy and CRUD-heavy applications that need structure out of the box.
C extension interop
Cython, C extensions and tools like ctypes and cffi let you drop the performance-critical fraction of your code into compiled languages while keeping the rest in Python.
Type hints and static tooling
Optional typing checked by mypy or pyright, plus ruff for fast linting and formatting, turn a dynamic language into one you can refactor with confidence at scale.
Use cases
Data pipelines and ETL
Ingesting, cleaning and transforming data with pandas and orchestration tools, feeding warehouses and downstream analytics reliably and on schedule.
Model training and serving
Training models in PyTorch or scikit-learn and serving them behind a FastAPI endpoint, with the same language spanning research and production.
LLM and AI applications
Retrieval systems, agents and tool-using workflows built on the OpenAI, Claude and LangChain ecosystems, which are Python-first by a wide margin.
Infrastructure automation
Replacing fragile shell scripts with tested Python for deployments, data migrations, reporting and the operational glue that keeps systems running.
When Python is the right choice
- Right when the work is data, statistics, machine learning or AI — the scientific and ML stack has no equal in any other language.
- Right for automation, scripting and glue: stitching together services, cleaning data, and replacing brittle shell scripts with something readable and testable.
- Right for APIs and back ends where throughput is I/O-bound — FastAPI and async Python handle thousands of concurrent requests comfortably.
- Wrong for CPU-bound hot loops in pure Python — numerical crunching, tight algorithms or real-time systems belong in C, Rust or Go, or must offload to compiled extensions.
- Wrong for mobile apps and browser front ends — Python has no credible native mobile or client-side web story, and you should not force one.
Python: pros and cons
Strengths
- Unmatched libraries for data, machine learning and scientific computing — no other language comes close.
- Highly readable, which lowers maintenance cost and onboarding time on long-lived systems.
- Excellent as glue: it talks to C libraries, databases, cloud APIs and every message queue you are likely to use.
- Modern tooling — type hints, mypy, ruff and pyright — brings real static checking to a dynamic language when you want it.
Trade-offs
- CPython is slow for CPU-bound pure-Python code; anything numerically intensive must lean on C-backed libraries or a compiled language.
- The Global Interpreter Lock prevents true multi-core parallelism within a single process, so CPU-bound threading buys you nothing.
- Dynamic typing lets whole classes of type errors reach runtime unless you enforce hints and static checks with discipline.
- Packaging and virtual environments remain a genuine friction point — dependency resolution and reproducible builds still take care to get right.
How we structure Python systems
We separate the parts of a Python system that must be fast from the parts that must be flexible. Business logic, orchestration and I/O stay in idiomatic Python; the numerically heavy inner loops run in NumPy, in a compiled extension, or in a dedicated service. Pretending the whole thing can be pure Python is how projects end up slow and hard to fix.
For web services we lean on FastAPI when the workload is I/O-bound and API-shaped, and Django when the application needs an ORM, admin and conventional structure. Either way we keep a clear boundary between the framework, the domain logic and the data layer, so the parts that change often are not tangled with the parts that rarely should.
Concurrency choices follow the GIL, not against it. I/O-bound work uses async or threads; CPU-bound work uses multiple processes or is pushed down into libraries that release the lock. Getting this decision right at design time is far cheaper than discovering it under load.
Being honest about speed
CPython executes pure-Python bytecode slowly — often one to two orders of magnitude behind C for tight numerical loops. This is a real constraint, and anyone who tells you otherwise is selling something. The way you live with it is to spend almost no time in pure Python for hot paths.
In practice that means vectorising with NumPy, using pandas operations that run in C, and moving genuine bottlenecks into Cython or a compiled extension. Profiling comes first: we measure where time actually goes before optimising, because in most data systems the cost is in I/O and serialisation, not in the Python interpreter at all.
For workloads that are irreducibly CPU-bound and latency-critical — real-time signal processing, high-frequency algorithms — we will recommend a different language for that component rather than contort Python into a role it is bad at.
Security in Python systems
Most Python security risk lives in dependencies, not the language. The ecosystem’s strength is also its exposure: a typical project pulls in a large transitive dependency tree, so we pin versions, scan with tools like pip-audit, and keep a bill of materials for what is actually installed.
At the application layer we rely on frameworks that do the right thing by default — parameterised queries through the ORM to prevent injection, framework-level CSRF and input validation, and secrets kept out of code and environment dumps. Pydantic and FastAPI’s typed validation give a strong, explicit boundary at the edge of the system.
We are careful with the language’s sharp edges: never using eval or pickle on untrusted input, and treating deserialisation as a trust boundary rather than a convenience.
Scaling Python
Python scales horizontally well and vertically poorly. Because the GIL caps what one process does on multiple cores for CPU-bound work, the standard pattern is to run many stateless worker processes behind a load balancer and scale by adding them, which containers and Kubernetes make straightforward.
For background and CPU-heavy work we use process-based workers and task queues rather than threads, so the GIL is never the ceiling. I/O-bound services scale differently again: a single async process can hold thousands of concurrent connections, so the constraint becomes the database and downstream services, not Python.
The honest summary is that Python scales fine as long as you architect around its concurrency model from the start. Retrofitting parallelism onto a design that assumed free threads is painful, so we make that call early.
Python integrations & ecosystem
The technologies we most often pair with it — each links to how we work with it.
How we work
We start by deciding what genuinely needs to be in Python and what does not, then build the smallest thing that proves the approach — a working pipeline or a serving endpoint — before adding surface area. For data and ML work especially, where requirements shift as you learn about the data, that early feedback saves a great deal of wasted effort.
From day one we treat a Python project as production software: type hints and mypy, ruff for linting, tests, and reproducible environments with locked dependencies. The dynamic nature of the language means this discipline is not optional if the code is meant to last, and we apply it as standard rather than as an upgrade.
The service behind it
Delivered throughCustom Software DevelopmentWhat we build with Python
The disciplines this technology most often shows up in — from a first build to taking over and stabilising an existing one.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves — including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Industries we use Python in
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
Also in Languages
Why teams choose us for Python
We operate what we build
The people who write your Python also run it in production, so the design accounts for deployment, monitoring and the 3am failure — not just the happy path in a notebook.
Senior engineers only
You get engineers who know when to reach for a C extension, how the GIL will bite, and when to tell you Python is the wrong choice — judgement that comes from having shipped this before.
Honest about trade-offs
We will tell you plainly when a component should be written in Go or Rust, or when your performance problem is architectural rather than something a faster language would fix.
Typical timeline
- 01
Discovery
Understand the data, the constraints and where Python is and is not the right tool. One to two weeks, ending in a concrete plan.
- 02
Prototype
A working slice — pipeline, model or endpoint — that proves the approach against real data before we commit to breadth.
- 03
Build
The full system with tests, typing, observability and reproducible deployment, hardened for production rather than the demo.
- 04
Operate
We run what we built: monitoring, dependency updates, and tuning as data volumes and usage grow.
How pricing works
- Time-and-materials for open-ended data, ML or platform work where the shape of the problem is still being discovered.
- Fixed-scope pricing for well-defined builds — a specific API, pipeline or automation with clear acceptance criteria.
- Retained senior capacity for teams that need ongoing Python engineering without carrying a permanent hire.
Hire Python engineers
Need Python capacity on your own team? We embed named senior engineers into your existing team — reporting to your leads, working in your rituals — so you add capacity without a hiring cycle.
Hire Python engineersCommon questions
Is Python too slow for our system?
It depends entirely on where the time goes. If your work is I/O-bound or backed by NumPy and pandas, Python is fast enough and the interpreter is rarely the bottleneck. If you have tight CPU-bound loops in pure Python, yes — those need vectorising, a compiled extension, or a different language for that piece. We profile before answering rather than guessing.
What is the GIL and does it matter for us?
The Global Interpreter Lock means one Python process runs Python bytecode on a single core at a time, so CPU-bound threads do not run in parallel. It matters if you planned to scale CPU work with threads — you cannot. The answer is multiple processes or pushing work into libraries that release the lock, which we design for from the start. For I/O-bound work the GIL is largely irrelevant.
Should we use Django or FastAPI?
Django when you want a full framework with an ORM, migrations and a ready-made admin for a conventional application. FastAPI when you are building typed, high-concurrency APIs and want async, automatic validation and generated docs without the rest of a framework. They solve different problems, and we pick based on the shape of your application, not fashion.
Can we build our mobile app or website front end in Python?
No, and we would advise against trying. Python has no credible native mobile story and no place running in the browser. Use it for the back end, the data layer and the APIs; use a proper native or web front-end technology for the client. Forcing Python into that role produces something worse than the honest alternative.
How do you keep a dynamically typed codebase maintainable?
With type hints checked by mypy or pyright, ruff for linting and formatting, real test coverage, and locked reproducible environments. Dynamic typing is a convenience during exploration and a liability in a large untyped codebase, so on anything meant to last we treat static checking as standard practice rather than an afterthought.
Building on Python?
A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.