Python Backends
Built for Production
Not Just Prototypes

Python powers web applications and APIs through three main frameworks: Django, FastAPI, and Flask. Django is the full-stack choice — it ships with an ORM, authentication, an admin interface, and a template engine, making it ideal for content-heavy applications and complex data models. FastAPI is the high-performance API framework — async by default, type-safe with Pydantic, and it auto-generates OpenAPI documentation, which makes it the natural backend for React, Next.js, and Flutter frontends. Flask remains a lightweight option for small services and prototypes. Beyond the web, Python is also the language most teams reach for when a project needs AI integrations, data pipelines, or automation alongside the web layer.

Use Django when you need a full-stack application: a built-in admin interface, an ORM, authentication, form handling, and conventions the framework enforces for you. It is the right choice for content management layers, complex data models, and teams that want batteries-included productivity. Use FastAPI when you need a high-performance REST API — async request handling, Pydantic type validation, and auto-generated OpenAPI docs — serving a separate React, Next.js, Flutter, or React Native frontend, or powering an AI pipeline with GPT integrations. In short: Django for full-stack apps with an admin layer, FastAPI for typed, high-concurrency APIs and AI backends. We recommend based on your requirement during the discovery call.

Python development at CV Infotech costs $30 per hour — compared to $100-200 per hour at US-based Python agencies for the same discipline. Simple automation scripts and small utilities range from $2,000 to $8,000. Full Django web applications with an admin layer, authentication, and a complex data model typically cost $10,000 to $40,000. FastAPI REST backends serving a frontend or AI pipeline run $8,000 to $30,000 depending on the number of endpoints and integrations. Every project starts with a written scope and a fixed-price quote within 48 hours — no surprise invoices.

Yes — Python is the dominant language for AI and machine learning, and it is not close. The core libraries are all Python-native: TensorFlow and PyTorch for model training and inference, scikit-learn for classical ML, and LangChain, LlamaIndex, the OpenAI SDK, and Anthropic's SDK for LLM integrations. When an AI feature requires more than a simple API call — RAG pipelines, custom embeddings, or model chaining — Python is where that work is done. We build these as FastAPI services or Celery workers. For the full AI cost and capability breakdown, see our AI development service.

Both Python and Node.js are valid, production-grade backend choices, and the right answer depends on your project. Python wins when the backend involves AI or machine learning, data pipelines, scientific computing, or when your team already has Python expertise — the AI/ML ecosystem simply lives in Python. Node.js wins for real-time features like WebSockets and live updates, and when a JavaScript frontend team wants to share code and tooling across the stack. CV Infotech builds in both at $30 per hour, so we recommend based on your actual requirement rather than a house preference — we will tell you honestly in the discovery call which fits better.

Our primary Python frameworks are Django and FastAPI. Django covers full-stack applications, admin interfaces, and Django REST Framework APIs. FastAPI covers high-performance async REST backends with Pydantic validation and auto-generated OpenAPI documentation. We also work with Flask for lightweight services, Celery for asynchronous task queues and scheduled jobs, and SQLAlchemy plus Alembic for database access and migrations on FastAPI projects. For AI work we add LangChain, the OpenAI SDK, and the Anthropic SDK on top of that foundation. Every framework choice is made against your requirement, not a default.

A simple Python web application — a Django site with an admin layer or a FastAPI backend with a handful of endpoints — typically takes 6 to 12 weeks from brief to production. A complex application with multi-tenancy, AI integrations, background job processing, and a rich data model takes 16 to 28 weeks. Timelines depend on the number of endpoints, integrations, and the async architecture involved. We work in biweekly sprints, deploy to staging at every sprint close, and update the OpenAPI documentation as we go — so you see working software throughout, never a single final reveal.

Yes — automation and scripting are a core part of what Python does best. We build scheduled data ingestion, transformation, and loading (ETL) pipelines using Python with Celery and Redis. We build web scraping and data collection tools with Playwright or BeautifulSoup, with Pydantic validating the extracted data. We also build task automation — report generation, file processing, API aggregation, and integration glue between systems. These run as standalone scripts, cron jobs, or Celery workers depending on how they need to be scheduled and monitored. Simple automation projects typically fall in the $2,000 to $8,000 range at $30 per hour.

Yes, we migrate PHP projects to Python, but every migration starts with a codebase audit before we quote. We review the existing PHP application to map its data model, business logic, and integrations, then recommend the right Python target — Django when the app needs a full-stack framework and admin layer, or FastAPI when it is primarily an API. Where appropriate we migrate incrementally, running the new Python service alongside the legacy PHP until the cutover is safe. Test coverage is added as part of the migration so behaviour is verified rather than assumed. If a migration does not make commercial sense, we will tell you honestly in the discovery call.

Python works with all the major databases, and we choose based on your data model. PostgreSQL is our default relational database — including pgvector for storing AI embeddings — and MySQL is fully supported. For document data we use MongoDB, and for caching, session storage, and Celery task queues we use Redis. SQLite is used for lightweight and local scenarios. On FastAPI projects we use SQLAlchemy for database access with Alembic handling migrations; on Django projects we use Django's built-in ORM and migration system. All of this is decided during the architecture and schema design step, before development begins.

Yes — testing is part of the delivery, not an afterthought. We write unit tests for business logic and integration tests for API endpoints using pytest, with fixtures to manage database state. On FastAPI projects, Pydantic's runtime type validation catches an entire class of bugs that crash untyped Python in production before they ever reach deployment. During the testing and security phase we also run OWASP checks and load-test the key endpoints. You receive a test coverage report as part of the handover, so quality is verifiable rather than promised.

Yes. CV Infotech has built production software since 2012, our Python rate is $30 per hour, and our delivery is backed by 512 verified 5.0 reviews on Freelancer.com. We build Django full-stack applications, FastAPI REST backends, AI and ML integrations, data pipelines, and automation — all with a 100% in-house team, so your API keys, credentials, and business logic never leave us. The discovery call is free and covers framework choice, API design, and scope, followed by a written fixed-price quote within 48 hours. Whether you are in the USA, UK, or Australia, we work in your timezone and communicate in plain English throughout.