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Python
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PyTorchWhy Python
Python is the language of data and AI, and also a productive choice for web applications and automation. Frameworks such as Django and FastAPI let small teams build secure, well-structured back ends quickly.
When Python is the right choice
| Use cases | |
|---|---|
| Great fit | AI and LLM applications, data pipelines, analytics, automation, web apps with Django, high-performance APIs with FastAPI |
| Less ideal | Mobile apps or highly interactive front ends, which need other technologies alongside Python |
What we build with Python
Web apps with Django
Admin, auth and ORM out of the box for business apps and portals.
APIs with FastAPI
Fast, typed APIs with automatic documentation.
AI & machine learning
LLM apps, RAG search, model training and inference services.
Data & automation
ETL pipelines, scrapers, reporting and scheduled jobs.
Our Python stack
Python for automation and internal tools
Beyond web applications and AI, Python excels at the unglamorous automation that saves teams hours every week: pulling data from several systems into one report, reconciling spreadsheets against a database, generating documents, cleaning imported data or monitoring prices and stock levels. These scripts often start on someone’s laptop and become critical to the business without anyone noticing.
We turn them into reliable services: version-controlled code with tests, scheduled jobs running in the cloud rather than on a personal computer, logging and alerts when something fails, and simple web interfaces where non-technical staff need to trigger or review a job. The automation keeps working when the person who wrote it goes on holiday.
Type hints and code quality in Python
Python’s flexibility can make large codebases hard to change safely. We use type hints throughout, checked with tools such as mypy or Pyright, alongside formatting and linting with Ruff. Combined with pytest suites and dependency management through modern tools such as uv or Poetry, this gives Python projects the reliability of more strictly typed languages while keeping the speed of development that makes Python attractive.
Deploying Python applications
Python applications are typically packaged in Docker containers and deployed to managed platforms such as AWS ECS, Google Cloud Run or Azure Container Apps, or to serverless functions for lighter workloads. Web applications run behind production servers such as Gunicorn or Uvicorn, with background workers for queues and scheduled jobs. We set up CI/CD pipelines that run tests, type checks and security scans before each deployment, and monitoring that tracks errors and performance in production.
Choosing a Python web framework
| Framework | Strengths | Best suited to |
|---|---|---|
| Django | Admin panel, ORM, auth and security features built in | Data-rich business apps, portals, content platforms |
| FastAPI | Async, type-driven, automatic OpenAPI documentation | APIs, AI/ML model serving, microservices |
| Flask | Minimal and flexible | Small services and prototypes |
| Celery / Dramatiq | Background task processing | Queues alongside any of the above |
A common pattern is Django for the main application and admin, with FastAPI services for model inference or high-throughput endpoints. Learn more about Django development.
Python for data and AI products
Python is the default language for data engineering and machine learning, which makes it a strong choice when a product combines a normal web application with data pipelines or AI features. Keeping both in one language means the same team can work across the stack, and models can be served without translating them into another runtime.
Data pipelines
Ingest, clean and transform data with pandas, Polars or dbt, scheduled by Airflow or Dagster.
Model serving
Expose trained models through FastAPI with batching, caching and monitoring.
LLM features
Retrieval, prompts, evaluations and guardrails using provider SDKs and orchestration libraries.
Analytics back ends
APIs that power dashboards and reports from your warehouse.
See data engineering and machine learning development.
Performance and scaling
Python is slower than compiled languages for raw computation, but most web workloads spend their time waiting on databases and networks. Where speed matters, the usual techniques are:
- Async frameworks and drivers for I/O-heavy endpoints
- Pushing heavy computation into NumPy, Polars or compiled extensions
- Background workers for anything that does not need to finish within the request
- Caching with Redis and HTTP caching at the CDN
- Horizontal scaling with multiple worker processes behind a load balancer
- Profiling with tools such as py-spy before optimising anything
Dependency and environment management
Python projects are notorious for “works on my machine” problems. We avoid them with pinned dependencies, reproducible environments and containers:
- Locked dependencies with uv, Poetry or pip-tools
- One supported Python version declared and tested in CI
- Docker images built the same way locally and in production
- Automated security scanning of dependencies
- Clear upgrade plans as Python versions reach end of life
What a typical Python project looks like
-
Discovery1–2 weeks
Clarify requirements, data sources and the right framework mix.
-
Foundations1–2 weeks
Repository, CI, environments, authentication and data model.
-
Feature sprintsongoing
Two-week sprints delivering working, tested features.
-
Launch & handover1–2 weeks
Monitoring, documentation and knowledge transfer.
Timelines are typical and depend on scope.
Security practices for Python applications
- Framework protections switched on: Django CSRF, ORM parameterised queries and template auto-escaping
- Input validation with Pydantic models at every API boundary
- Secrets in environment variables or a secret manager, never committed to Git
- Static security checks with Bandit and dependency audits with pip-audit
- Authentication through proven libraries or providers rather than custom code
- Rate limiting and logging on public endpoints
These checks run automatically in the build pipeline, so problems are caught when code is written rather than after release.
Common Python project pitfalls
- Mixing notebook prototypes directly into production code without tests
- Unpinned dependencies that change silently between deployments
- Running CPU-heavy work inside web requests instead of background workers
- No type hints, making large refactors risky
- Letting Python versions fall out of support before upgrading
Measuring success after launch
We agree a small set of measures at the start so the team knows whether the application is doing its job: response times for key endpoints, error rates, background job success and queue length, and the business outcome the software was built for, such as hours saved on a manual process or orders processed per day. These appear on a dashboard from the first release, which makes it easy to spot regressions and decide what to improve next.
Related: API development and DevOps consulting.
Python also suits teams that want one language across web, scripting and data work, which simplifies hiring and lets engineers move between projects without retraining.
Throughout the project you get access to the repository, staging environment and sprint board from day one.
How we work
- Discovery and architecture — a short design phase that agrees structure, data model and hosting.
- Two-week sprints — working software on a staging environment, with code reviews on every change.
- Automated tests and CI/CD — tests and quality checks run before anything reaches production.
- Documentation and handover — setup guides, architecture notes and API documentation.
Building AI features? See AI & data engineering. Hiring? See hire Python developers. For complete products, see custom software development.
This page is part of Backend Development Services — see the full overview for related services, approach and pricing.