Backend & APIs

Python for automation, data pipelines, and AI-integrated backends

Python APIs, internal tools, business automation, data workflows, and LLM-powered systems that remove manual work from operations and create new product capabilities.

✦Production-ready

◈Backend & APIs

◉We build with it

Plan this build Get Pricing for My Project

Python Development

How we use it

Python is our primary language for AI integration work, automation pipelines, and data processing. For API services we use FastAPI. For automation and scripting we use plain Python with Celery for scheduling. For AI workflows we use LangChain, LlamaIndex, and the native Anthropic and OpenAI SDKs.

Best fit for

Business automation

AI & data workflows

Internal tools & APIs

Why now

Python has been the most popular programming language for four consecutive years on the TIOBE Index and the most wanted language for the fifth year running in the 2024 Stack Overflow Survey. The reason is no longer just data science - it is AI. Python's ecosystem (LangChain, LlamaIndex, FastAPI, Celery, and the native SDKs from OpenAI, Anthropic, and Google) has become the de facto infrastructure layer for any business integrating LLMs into products or operations. Every serious AI workflow, RAG system, and LLM agent built in 2024–2025 is built in Python. Generative AI spending is forecast to reach $644 billion in 2025 (Gartner) - and Python is the language powering the vast majority of it.

What's included

Capabilities

API architecture & endpoint design

Database schema & integration

Authentication, RBAC & security hardening

Third-party integrations & webhooks

Performance optimisation & caching strategy

Fit analysis

Is this right for you?

Honest breakdown of where Python Development shines — and where it doesn't. Pick the right tool.

When to choose this

Right fit scenarios

You need to automate repetitive business operations - data extraction, report generation, file processing, or API synchronisation - that currently consume hours of manual work weekly

You are building an AI-powered product or integrating LLMs (OpenAI, Anthropic, Gemini) into your business workflow - Python's AI library ecosystem has no peer in any other language

Your business generates data - sales, logistics, operational metrics - that needs to be extracted, cleaned, transformed, and surfaced for decision-making

You need a lightweight, high-performance REST API using FastAPI that connects to a database, processes data, and integrates with third-party services

You are building internal tools - Slack bots, scheduled data jobs, web scrapers, automated reporting dashboards - that need to be maintainable by a small technical team

When to choose this

Right fit scenarios

You need to automate repetitive business operations - data extraction, report generation, file processing, or API synchronisation - that currently consume hours of manual work weekly

You are building an AI-powered product or integrating LLMs (OpenAI, Anthropic, Gemini) into your business workflow - Python's AI library ecosystem has no peer in any other language

Your business generates data - sales, logistics, operational metrics - that needs to be extracted, cleaned, transformed, and surfaced for decision-making

You need a lightweight, high-performance REST API using FastAPI that connects to a database, processes data, and integrates with third-party services

You are building internal tools - Slack bots, scheduled data jobs, web scrapers, automated reporting dashboards - that need to be maintainable by a small technical team

Honest limitations

Not the best fit if…

High-concurrency web applications serving thousands of simultaneous users with real-time requirements - Node.js handles I/O concurrency more efficiently for this pattern

Mobile application backends where your React Native team wants to stay within the JavaScript ecosystem end-to-end

Simple content websites or marketing sites where a CMS or static site generator is faster, cheaper, and requires no backend programming

Teams that need to hire primarily from a PHP or JavaScript developer pool - Python requires finding engineers with Python-specific experience

Services

Built into our workflow

Strategy

AI Automation

Do more with the same team

Learn more

Development

DevOps & Hosting

Infra that never sleeps, never fails

Learn more

Strategy

Maintenance & Support

Peace of mind, every month

Learn more

Common questions

You're probably wondering

How long does a Python project take to build?

A Python automation workflow or internal tool takes 2–4 weeks. A FastAPI or Django REST API with authentication and database integration takes 6–10 weeks. An AI-integrated product with RAG pipelines, LLM integration, and custom data processing takes 12–20 weeks depending on complexity and the maturity of the data sources.

Python vs Node.js - which backend should I choose?

Choose Python if you need AI and LLM integration, data processing, scientific computation, or automation scripts. Choose Node.js if you want JavaScript end-to-end, real-time features, or a shared TypeScript codebase with your React frontend. For pure REST API development, both are competitive - the deciding factor is usually the team's existing expertise and what the adjacent systems are written in.

What Python frameworks do you use?

FastAPI is our default for API-only backends - it is async-native, extremely fast, and auto-generates OpenAPI documentation that clients can use to understand endpoints without a meeting. Django is our choice when applications need a full-featured framework with ORM, admin panel, and built-in authentication. Flask is used for small, focused services where framework overhead is not justified.

How much does Python development cost in India?

Python automation projects start at ₹1.5–3 lakh. Full Python API or backend builds start at ₹5–9 lakh. AI-integrated Python systems with custom LLM workflows, RAG pipelines, and vector database integration range from ₹10–25 lakh. Senior Python developers in India bill at ₹2,500–5,500/hour.

Can you build RAG systems and LLM-powered features in Python?

Yes - this is one of our strongest capabilities. We build RAG (Retrieval-Augmented Generation) systems, document processing pipelines, custom AI workflows, and LLM API integrations using LangChain, LlamaIndex, and the native Anthropic and OpenAI SDKs. We pair these with pgvector in PostgreSQL for vector search so you do not need to maintain a separate vector database.

Can Python handle production-level traffic?

Yes. Instagram runs on Python/Django and handles billions of interactions. Proper production Python deployments use Gunicorn or Uvicorn with multiple workers, Redis for caching, Celery for async tasks, and Nginx as a reverse proxy. The bottleneck is almost never Python itself - it is architecture and database design. FastAPI with Uvicorn benchmarks faster than many Node.js frameworks for JSON API workloads.

Is Python good for scheduled jobs and automation?

Python is the standard for automation. Celery with Redis handles complex background jobs and scheduled tasks. APScheduler handles cron-like periodic workflows. The ecosystem for interfacing with external APIs, Excel and CSV files, email, databases, PDF generation, and browser automation (Playwright, Selenium) is more mature in Python than any other language. Most operational automation we build for clients is in Python.