Python development services
What we reach for when the work is data, automation or a model API.
We build Python backends and services: Django applications with an admin your operations team will genuinely use, FastAPI services for typed JSON APIs and AI integration, and the scheduled pipelines that move data between systems while everyone is asleep. Type hints throughout, and a monitored failure path for every job.
What is Python good for, and when do we choose it?
Python is a general-purpose language with two ecosystems that matter commercially: mature web frameworks, and the best libraries anywhere for data, scraping, scientific work and machine learning. We reach for it when a project leans towards the second list. For a plain web backend, Node or Laravel will usually serve you better, and we will say so rather than use whichever language we opened last.
Django is the productive end of Python on the web. ORM, migrations, authentication, permissions and a generated admin all arrive together, so an internal system with twenty models and five roles becomes usable in weeks. That admin is frequently the entire reason to choose Django: your operations team gets a working back office without anyone designing one screen.
FastAPI is the modern answer for services rather than sites. Python type hints validate incoming data, serialise responses and generate the OpenAPI schema from one set of definitions, so documentation and the typed clients your front-end uses cannot drift away from the code. It is also the natural home for anything calling a model API, because every AI SDK worth having ships a Python client first and the others later.
The limits, stated plainly. Python is slower than compiled languages at raw computation and its web servers hold more memory per request than Node. Neither fact matters for most business software, and where it does, caching and queues fix it long before a rewrite becomes sensible. For the automation end of this work, see workflow automation.
What we build with Python
Django business applications
Internal systems with roles, approvals and audit trails, plus a generated admin your operations team can run without filing a ticket.
FastAPI services
Typed JSON APIs with generated OpenAPI documentation, sitting in front of a database or a handful of third-party systems.
AI and model integrations
Retrieval pipelines, embeddings, document processing and agent workflows against hosted models, with evaluation before release.
Explore →Data pipelines and reporting
Scheduled extraction, cleaning and loading between CRMs, spreadsheets, warehouses and dashboards, where a failure wakes somebody rather than nobody.
Scraping and monitoring
Compliant collection of pricing, catalogue or listing data, with change detection and rate limits that keep you off a block list.
Automation of manual processes
The overnight jobs, reconciliations and report generation currently occupying somebody's first two hours every morning.
Our Python stack, by layer
Python compared with the alternatives
Django vs Laravel
Both are batteries-included frameworks for database-backed applications and both are genuinely good. Laravel has cheaper hosting almost everywhere and a much larger PHP hiring pool. Django's generated admin and Python's data libraries take it when the same project also carries reporting, scraping or model work.
FastAPI vs Node.js for an API
Node handles very high concurrency a little more cheaply and lets you share types with a TypeScript front-end, which is worth real money on a small team. FastAPI wins the moment the service touches data science, document processing or model APIs, because the Python libraries are simply better and better maintained.
Python vs a no-code automation tool
For a straightforward two-system sync, n8n or Make is cheaper and faster to change, and we build those too. Write Python when the logic branches properly, when per-task pricing starts to hurt at your volume, or when the transformation needs to live under version control with tests around it.
Python, answered
Django or FastAPI, which should we use?
Django when there is a database schema, user roles and back-office screens. The generated admin alone saves weeks of work nobody enjoys. FastAPI when you are building a service other applications consume, or anything talking to model APIs. On larger systems we run both: Django for the application, FastAPI for the machine-facing endpoints.
Can Python handle our traffic?
For ordinary business volumes, comfortably. It is slower per request than Go or .NET, so the design carries the load: caching, the right indexes, queues for slow tasks, more instances behind a balancer. If your workload turns out to be compute-bound at scale, we will tell you and use a different tool for that piece.
Do you use Python for AI features?
Yes, it is our default for anything involving retrieval, embeddings or document processing, because the client libraries and tooling are strongest there by some distance. The user-facing part is usually a Next.js interface talking to a Python service over a typed API.
Can you take over an existing Django project?
Yes. The first pass covers Django and dependency versions, migration state, test coverage, and any custom code sitting around authentication and payments, which is where the expensive surprises live. You get a prioritised written plan before we touch a line.
How do you handle scheduled jobs?
Celery with Redis for anything that retries or fans out, plain scheduled tasks with a lock for simple nightly work. Either way it is monitored. A job failing silently for three weeks is worse than never having written it.
Can we hire a Python developer from you?
Yes. Python engineers start at $25/hour with a 15-day trial and profiles within 48 hours. Tell us whether the work is Django, FastAPI or data-heavy, because those are three different people.
A Python service or an automation in mind?
Describe the data and the systems it has to move between. You get an approach and a range back, not a request for a discovery call.