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✓ On-time delivery
+ Agile delivery
2-wk sprints
Python Development Services

Python Applications Built for Speed, Data & AI Workloads

We build Python-powered applications — Django & FastAPI backends, data pipelines, ML integrations, and automation scripts. Full-stack Python delivery.

Django & FastAPI
Data Pipelines
ML Integration
100% IP Yours
Free first consultationNo commitment needed

Python development

Django, FastAPI, data & ML

100%

code & IP yours from day one

typical MVP timeline
est.

6–10 wks

48h

Avg. Response Time

no surprises, ever

What are Python development services?

Python development services cover the full range of software built with Python: web applications using Django, FastAPI, or Flask; REST and GraphQL APIs for mobile and web clients; data pipelines and ETL processes using pandas, PySpark, and Apache Airflow; machine learning integration using scikit-learn, PyTorch, and LLM APIs; and infrastructure components such as Celery task queues, Redis caching, and containerised microservices. A Python development company provides the engineering team, architecture expertise, and delivery process to take a Python project from requirements to production.

Our Services

Python Development We Deliver

Web, API, AI, and data engineering under one roof.

01

Django Web Application Development

Full-featured web apps with Django ORM, admin panel, authentication, and REST framework for rapid, maintainable delivery.

02

FastAPI and Flask API Development

High-performance REST and GraphQL APIs for web, mobile, and microservices. FastAPI handles async workloads natively.

03

AI and ML Integration

Python-based AI features: LLM integration, computer vision, NLP classification, and recommendation engines added to your product.

04

Data Pipeline and ETL Development

Apache Airflow, pandas, and PySpark data pipelines for analytics and reporting, with monitoring and retry logic built in.

05

Data Science and Analytics Tools

Jupyter-based analytics platforms, interactive dashboards, and reporting automation for business intelligence teams.

06

Python Microservices and DevOps

Containerised Python services, Celery task queues, Redis caching, and Kubernetes deployment for scalable production systems.

Our Stack

The Python Ecosystem From Web to AI

Web Frameworks

  • Django
  • FastAPI
  • Flask
  • Starlette

Data and ML

  • pandas
  • NumPy
  • scikit-learn
  • PyTorch

AI and LLMs

  • OpenAI API
  • LangChain
  • Hugging Face
  • LlamaIndex

Data Engineering

  • Apache Airflow
  • PySpark
  • dbt
  • SQLAlchemy

Infrastructure

  • Celery
  • Redis
  • Docker
  • Kubernetes

Databases

  • PostgreSQL
  • MongoDB
  • Redis
  • Elasticsearch
Why Python

The Right Language for Web, AI, and Data - In One Team

Python lets you build the web backend, data pipeline, and AI features without splitting your codebase or your team across multiple languages and vendors.

Unified team for web backend and AI/ML features
Django admin reduces internal tool build time
FastAPI handles 10,000+ requests per second
Async-first for real-time data and notification systems
Native integration with every major AI and data library
Strong typing with Pydantic and mypy for production codebases
One language from prototype to production ML pipeline
Active library ecosystem maintained by major tech companies
Our Process

How We Deliver Python Projects

A structured process from stack selection to production deployment.

01

Requirements and Stack Selection

Define the application requirements, choose between Django, FastAPI, or Flask, and map the data model before any code is written.

02

Architecture and Data Model Design

Design the database schema, API contract, authentication flow, and any async task queues or data pipeline components.

03

Core Application or API Build

Build the primary application layer with full test coverage, type annotations, and linting from the first commit.

04

AI/Data Layer Integration

Add ML model serving, LLM integration, data pipeline orchestration, or analytics endpoints to the core application.

05

Performance Testing and Optimisation

Load test with Locust or k6, profile slow queries, tune caching strategy, and validate async throughput under realistic concurrency.

06

Deployment and Documentation

Containerise, configure CI/CD, deploy to production, and deliver architecture documentation with local development setup guide.

Indicative Pricing

Python Project Investment Guide

Fixed-scope pricing for common Python project types.

Project TypeScopeInvestment (USD)
REST API or Django web appUp to 10 endpoints or modules$15,000 - $40,000
Full web application + APIAuth, CRUD, integrations, dashboard$45,000 - $100,000
AI or data platformML pipeline, LLM integration, data engineering$60,000 - $150,000+

Pricing depends on feature complexity, number of integrations, and AI/data requirements. Contact us for a scoped estimate.

Why CodeShiper

Why Teams Choose Us for Python Work

Python expertise across web, AI, and data in one engagement.

01

Python-First Team

Python is not a secondary skill for us. Our engineers use it daily across web, data, and AI workstreams and know the ecosystem in depth.

02

Web, AI, and Data in One Engagement

You get a single team that can handle your Django backend, data pipeline, and LLM integration without context-switching between vendors.

03

Production-Grade Code Standards

Type annotations, mypy, ruff, pytest, and pre-commit hooks are non-negotiable. The codebase you receive is maintainable from day one.

04

FastAPI Performance Expertise

We have built async FastAPI services processing millions of events per day. We know how to structure dependencies, lifespan events, and connection pooling for production.

05

Async and Real-Time Patterns

WebSockets, Server-Sent Events, and background task queues are regular patterns for us. Real-time features are not afterthoughts bolted on later.

06

Documented and Tested Handover

Every engagement ends with architecture documentation, a local development guide, deployment runbooks, and a test suite your team can extend.

100%
IP ownership from day one
2 wks
First working build
48 h
Average response time
98%
Client satisfaction rate
FAQ

Common Questions About Python Development Services

When should I choose Python for my project?
Python is the right choice when your project involves web applications that need to grow into AI or data features, data pipelines and analytics, machine learning integration, or rapid API development. If your primary concern is high-throughput real-time gaming or low-level system programming, other languages may be more suitable. For most business applications, Python delivers faster development, a rich library ecosystem, and a clear path to AI capabilities.
What is the difference between Django, FastAPI, and Flask?
Django is a batteries-included framework best suited for full-featured web applications with complex data models, user authentication, and admin interfaces. FastAPI is the best choice for high-performance REST APIs, asynchronous workloads, and microservices - it handles 10,000+ requests per second and generates OpenAPI documentation automatically. Flask is a lightweight micro-framework suited for simple APIs or when you need maximum control over your stack with minimal opinions imposed.
Can you add AI features to an existing Python application?
Yes. We regularly add LLM integration (OpenAI, Anthropic, open-source models), computer vision, NLP classification, and recommendation engines to existing Python applications. The integration approach depends on your current stack - Django apps typically get AI features as background tasks via Celery, while FastAPI services can expose AI endpoints directly with async support.
How does Django compare to Node.js for web applications?
Django has a mature ORM, built-in admin panel, and an authentication system that significantly reduces development time for CRUD-heavy applications. Node.js is faster for pure I/O throughput and has a larger front-end ecosystem. For teams that need both a web backend and AI/data capabilities in the same language, Django is often the more practical choice since it avoids splitting the codebase across two languages.
Can you build data pipelines with Python?
Yes. We build production data pipelines using Apache Airflow for orchestration, pandas and PySpark for transformation, and SQLAlchemy or dbt for data modelling. Pipelines can run on scheduled intervals or be triggered by events, with monitoring, retry logic, and alerting included as standard.
Does Python perform well at scale?
Python web applications scale well with the right architecture. FastAPI with async handlers can match Node.js throughput for I/O-bound workloads. For CPU-bound tasks, we use Celery workers and process-level concurrency. Caching with Redis, database query optimisation, and horizontal scaling via Kubernetes handle the load increases that come with growth.
How long does it take your team to get up to speed on our Python codebase?
We target a productive contribution within the first week. Our onboarding process involves a codebase walkthrough, running the test suite, and making a small first contribution. We document our understanding of your architecture during onboarding, which is useful for your team as well as ours.
How do you handle deployment for Python applications?
All Python applications are containerised with Docker. We use Kubernetes for production orchestration when scale warrants it, or simpler deployment targets (Railway, Render, AWS ECS) for smaller applications. CI/CD pipelines handle automated testing, image builds, and deployment. Infrastructure as code with Terraform is included for AWS and GCP deployments.
What is your approach to testing Python code?
We write pytest unit tests for business logic, integration tests for API endpoints, and use factory_boy for test data generation. Django applications get full test coverage of models, views, and serializers. FastAPI endpoints are tested with TestClient. Code quality is enforced with mypy type checking, ruff linting, and pre-commit hooks.
Do you provide maintenance after the project launches?
Yes. We offer a 60-day post-launch support period covering bug fixes at no extra cost. Ongoing maintenance retainers cover dependency updates, security patches, performance monitoring, and feature additions. We provide handover documentation including architecture diagrams, deployment runbooks, and a local development setup guide.

Let's Talk

Ready to Build With Python?

Tell us about your project and we will recommend the right Python stack and team structure for your requirements.

Get a Free Python Project EstimateSchedule a Call
NDA available before any technical discussionResponse within 48 hoursNo pressure. No hard sell.