Python development company
From Django and FastAPI backends to data pipelines, LLM-powered services and internal automation, Krapton writes Python with type hints, tests and reproducible packaging, so your team can keep shipping after handover.
- Senior engineers · IST + EST overlap
- NDA on day 1
- 24-hour reply
- Projects shipped
- 50+
- Code ownership
- 100%
- Client rating
- 4.8/5
- First reply
- 24h
Python backends built for the long run
Krapton works as a Python development company across three kinds of systems: web backends, data and ML pipelines, and services that connect large language models to your own data. The engineering habits are the same across all three. Code is typed with modern annotations and checked by mypy or Pyright, linted and formatted with Ruff, and tested with pytest including fixtures for databases and external APIs. Dependencies are pinned with uv or Poetry lock files so builds are reproducible. For web work the choice between Django and FastAPI is made on fit: Django when an admin, ORM migrations and authentication save real build time, FastAPI when the service is an API with heavy I/O and Pydantic validation at the boundary. Python has real limits too. CPU-bound hot paths may need NumPy vectorization, a Rust extension through PyO3, or a different language, and we flag that before it becomes a production incident.
Python development services
Django application development
Django 5.2 LTS projects with the ORM, admin, migrations and Django REST Framework, plus Celery for background jobs and careful query work with select_related and prefetch_related.
FastAPI and async services
Async APIs on FastAPI with Pydantic v2 models, SQLAlchemy 2.0 async sessions, OpenAPI schemas generated from code, and care to keep blocking calls off the event loop.
Data pipelines and ETL
Batch and streaming jobs with pandas, Polars, PySpark or dbt Python models, orchestrated by Airflow, Dagster or Prefect, with data validation checks before anything lands in the warehouse.
LLM and ML services
Retrieval pipelines, model-serving APIs and agent tooling in Python, using official provider SDKs, vector stores such as pgvector, and evaluation suites that catch regressions when prompts or models change.
Legacy Python modernization
Upgrades from end-of-life interpreters and old Django or Flask versions, adding type hints incrementally, replacing setup.py with pyproject.toml, and raising test coverage before risky refactors begin.
Automation and integrations
Scripts and services that connect CRMs, ERPs, payment providers and internal tools, with retries, idempotency keys, structured logging and alerting so failures surface instead of silently dropping records.
Why teams choose Krapton for Python
Senior engineers, fixed scope, and code you own — the things agencies promise and rarely deliver.
Typing as a safety net
Dynamic typing stops being a liability when annotations are checked in CI and pytest covers business rules; reviewers reject untyped public functions in new code.
Framework choice on merit
Django, FastAPI, Flask or Litestar are chosen for the problem at hand, and we document why, so future engineers understand the tradeoff instead of inheriting an unexplained preference.
Performance measured, not guessed
py-spy and cProfile locate hot paths, database queries are inspected before code is blamed, and CPU-heavy work moves to vectorized libraries, worker processes or compiled extensions only when profiles justify it.
Reproducible builds and deploys
Lock files, slim Docker images, pinned interpreter versions and dependency scanning with pip-audit keep environments identical from laptop to production and expose vulnerable packages early.
How a Python project runs with Krapton
01
Scope and architecture
We pin down data flows, integrations and load expectations, then choose framework, database and task queue, recording each decision with its tradeoffs.
02
Iterative build
Features ship in short iterations behind tests, with OpenAPI docs or notebooks demonstrating progress and preview environments for stakeholders to try.
03
Harden for production
Load tests with Locust, error tracking in Sentry, OpenTelemetry traces and security checks with Bandit and pip-audit run before launch, not after the first incident.
04
Maintain and upgrade
Krapton keeps interpreter and dependency upgrades on a schedule, or hands over a documented codebase, CI pipeline and runbook your engineers can own.
The Python stack we build with
Python development: questions answered
How do I choose a Python development company for a new product?
Pick one that can show how it structures, tests and deploys Python, not only which frameworks it lists. Ask to see a typical pyproject.toml, CI pipeline and test layout, how type checking is enforced, and how they decide between Django and FastAPI. A credible Python development company also explains where Python is a poor fit for your workload and what it would do about it.
Django or FastAPI: which should we build on?
Choose Django when you need an admin, user management, ORM migrations and a mature plugin ecosystem from day one; choose FastAPI for API-only services with heavy concurrent I/O and strict request validation. Django also supports async views, but its ORM is still largely synchronous underneath, so FastAPI with async SQLAlchemy is usually the better match for high-concurrency APIs. Many products run both: Django for the back office, FastAPI for public endpoints.
Can you upgrade an old Python 2 or early Python 3 codebase?
Yes, and the safe route is incremental rather than a big-bang rewrite. We first add characterization tests around critical paths, then move to a supported interpreter, since Python 3.9 and older no longer receive security fixes, using tools such as pyupgrade and Ruff’s upgrade rules. Dependencies without maintained releases are replaced one by one, and the service is deployed side by side with the old version until outputs match.
Is Python fast enough for high-traffic systems?
Usually yes, because most web and data workloads are bound by I/O and databases rather than interpreter speed. Async frameworks, connection pooling, caching and horizontal scaling with Gunicorn or Uvicorn workers handle large request volumes. Where CPU becomes the limit, the options are vectorized NumPy or Polars code, multiprocessing, compiled extensions in Rust or Cython, or the free-threaded CPython build introduced in 3.13, once your dependencies support it.
What determines the cost of Python development services?
Scope and risk set the cost, so there is no single figure. The biggest drivers are the number of integrations and data sources, whether legacy code must be migrated, data volumes and latency targets, compliance needs such as HIPAA or GDPR, ML or LLM components that need evaluation work, and the seniority mix required. A short discovery phase converts those into a scoped plan you can compare.
Ready to build? Tell us what you have in mind.
Send a short brief and a senior engineer will reply within one business day with next steps — no sales fluff.