Backends, data pipelines
& automation in Python.
Python is our choice for backend-heavy applications, data processing, and automation systems. Django for full-featured SaaS, FastAPI for high-performance APIs, Celery for task queues — we've shipped Python in production for 8+ years.
Python for the backend problems that need real engineering
Python's versatility and library ecosystem make it ideal for backend-heavy applications — especially those with data processing, automation, or AI integration requirements.
Django & FastAPI Applications
Django for full-featured web applications — auth, admin, ORM, forms. FastAPI for high-performance API services where speed and automatic OpenAPI docs matter.
Data Processing & ETL
Pipelines that extract, transform, and load data from multiple sources. Scheduled batch jobs, event-driven processing, and reporting infrastructure at scale.
Automation & Task Queues
Process automation with Celery and Redis — document processing, API integrations, report generation, and complex workflow orchestration that runs reliably in the background.
AI Integration Backends
Python is the native language of the AI ecosystem. We build backends that integrate with OpenAI, Anthropic, and open-source LLMs — wrapping AI capabilities into production-ready APIs.
The right tool for data-heavy and integration-first projects
Python isn't always the fastest runtime — but for backend applications where developer velocity, data handling, and AI integration matter, it's hard to beat.
Readable, maintainable code
Python enforces readability. Code one developer writes, another can understand without significant ramp-up. For applications with long maintenance tails, that pays off continuously.
Library ecosystem built for data
NumPy, Pandas, Celery, SQLAlchemy, Pydantic, Boto3 — Python has mature libraries for almost every problem category. We pick the right ones and don't build what already exists.
Native AI/ML ecosystem
OpenAI, LangChain, Hugging Face, LlamaIndex — all Python-first. For projects with AI integration requirements, Python removes a significant translation layer.
Strong for integration work
Most enterprise systems have Python SDKs. For projects primarily connecting existing systems, Python is often faster to deliver than alternatives.
What we pair with Python
Python integrates cleanly with the rest of a modern stack. Here's what we typically combine it with.
Common questions about Python development
Django or FastAPI — which do you use?
Depends on what you're building. Django for full-featured applications where you want batteries included — auth, admin panel, ORM, forms. FastAPI for high-performance API services or microservices where speed and automatic OpenAPI documentation matter. We'll recommend the right one.
Can you build a SaaS product with Python?
Yes. Django is a mature, proven framework for SaaS products. We've built multi-tenant Django applications with Stripe billing, team management, and role-based access. It's a solid choice, especially for founders and teams with Python expertise.
Do you build data pipelines and ETL systems?
Yes — Pandas for transformation, Celery for task queuing, PostgreSQL or cloud data warehouses for storage. We build data processing infrastructure, not ML model development.
How does Python performance compare to Node.js?
For I/O-bound work (handling many concurrent HTTP requests), Node.js has an edge. For CPU-bound work (data processing, complex calculations), Python performs well with proper architecture. For most SaaS and API applications, the performance difference is not the deciding factor — project fit and team expertise matter more.
Need a Python development team?
8 years. Django, FastAPI, data pipelines.
Tell us what you're building. We'll give you a straight answer on whether Python is the right tool and what it'll take to ship it.
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