Python development services: senior teams that ship to production
Dedicated Python teams that build FastAPI and Django back ends, data pipelines and model-serving services, and keep them typed, tested and fast.
By the Ryz Labs team · Updated October 2026
Ryz Labs is a Python development company that gives you a dedicated team of senior Python engineers to build web back ends, APIs, data pipelines and machine-learning services, in your repos and your cloud, and ship them to production. Every engineer is in the top 1% of the tens of thousands we have interviewed, and the team works on US business hours with real-time overlap with your engineers.
What we build
Python covers more ground than most languages, so we scope by system, not by framework. These are the systems our Python teams most often build:
- FastAPI services: async APIs with Pydantic models, generated OpenAPI docs and typed clients, built for high request volume and quick iteration.
- Django applications: products and internal platforms that benefit from Django's ORM, admin, auth and migrations, with Django REST Framework or Django Ninja for APIs.
- Data pipelines and ETL: ingestion and transformation jobs orchestrated with Airflow, Dagster or Prefect, using pandas or Polars, landing in Snowflake, BigQuery, Databricks or Postgres.
- Machine-learning services: model training code, feature pipelines and inference APIs built with scikit-learn, PyTorch or XGBoost, with versioned models and monitored predictions.
- LLM-backed back ends: Python services that call OpenAI or Anthropic models, run retrieval over your documents and log results for evaluation. Larger efforts run as an AI pod; see AI development services.
- Background processing: Celery, RQ or Dramatiq workers for reports, document processing, scheduled jobs and fan-out work, with retries and visibility into failed tasks.
- Automation and internal tools: replacing spreadsheet-and-script workflows with tested Python services, CLIs and small admin apps your operations team can rely on.
- Upgrades and modernization: moving code off end-of-life Python versions, adding type hints and tests to untyped codebases, and replacing Flask or legacy Django setups where they block progress.
How an engagement works
- Talk. A scoping call about the system, its data, its users and the outcome you need: a launch, a pipeline that runs on time, an API that stays under a latency target.
- Match. We propose a team scoped to your stack, for example Python back-end seniors plus a data engineer or ML engineer. You get a plan, a price and the names of the people.
- Join. The team starts in your repos, CI, cloud accounts and standups, and demos working software every week.
- Grow. Add engineers or disciplines as scope grows, or hand the system to your team with documentation, runbooks and dashboards.
Week 1 is access, a reproducible local environment (lockfile, Docker, seed data), and a review of the existing code, tests and error logs. Month 1 usually means agreed contracts and data models, type checking and linting in CI, and the first slice in production. By month 3, the team ships on your release cadence, pipelines and services have alerts and owners, and test coverage protects the paths that matter.
The stack our teams work in
| Layer | Tools we use | Notes |
|---|
| Web frameworks | FastAPI, Django, Django REST Framework, Flask, Litestar | FastAPI for API-first services; Django when you need admin and auth out of the box |
| Data access | PostgreSQL, SQLAlchemy 2.0, Django ORM, Alembic, Redis | Migrations reviewed like any other code |
| Data and pipelines | pandas, Polars, PySpark, Airflow, Dagster, Prefect, dbt | Polars or Spark when pandas runs out of memory |
| ML and AI | scikit-learn, PyTorch, XGBoost, MLflow, OpenAI and Anthropic SDKs, pgvector | Experiments tracked; models versioned |
| Tooling and quality | uv or Poetry, Ruff, mypy or Pyright, pytest, Hypothesis, pre-commit | Types and lint checks block merges |
| Workers and async | Celery, RQ, asyncio, Kafka, Amazon SQS | Idempotent tasks with dead-letter handling |
| Deployment and monitoring | Docker, Kubernetes, AWS (ECS, Lambda), Azure, Gunicorn, Uvicorn, OpenTelemetry, Sentry | Same container image from CI to production |
How we keep Python systems correct and fast
Python lets a team move quickly, and it lets bugs reach production quietly. Dynamic typing, a forgiving runtime and huge dependency trees are the usual sources of trouble. A senior team handles them like this:
- Type errors found by users. We add type hints at module boundaries first, run mypy or Pyright in CI, and use Pydantic to validate data at the edges, so a missing field fails a test instead of a customer request.
- Dependency drift. "Works on my machine" usually means unpinned packages. We lock dependencies with uv or Poetry, build one container image per commit, and update packages in small, tested batches.
- Blocking calls in async code. A synchronous database driver or requests call inside a FastAPI async endpoint blocks the event loop and caps throughput. We use async drivers or run blocking work in a thread pool, and load-test before launch.
- The GIL and CPU-bound work. Threads don't speed up CPU-heavy Python. We push heavy work into vectorized libraries (NumPy, Polars), separate processes or worker queues, and measure with py-spy before rewriting anything.
- ORM performance traps. N+1 queries in Django and SQLAlchemy are the most common cause of slow pages. We use select_related, prefetch_related or eager loading, watch query counts in tests, and review slow-query logs.
- Pipelines that fail silently. A job that "succeeds" with zero rows is worse than one that crashes. Pipelines get data-quality checks (row counts, schema checks, freshness), alerts on failure, and backfills that are safe to rerun.
- Models that drift. ML services log inputs and predictions, compare live distributions with training data and have a tested rollback to the previous model version.
- Secrets and supply chain. Credentials live in a secrets manager, not in notebooks or .env files in Git, and pip-audit or Snyk runs on every build.
Team shapes and cost
Typical Ryz pricing is $7,000 to $15,000 per engineer per month. Mid-level engineers (comparable to Amazon L5) cost $7,000 to $10,000; seniors (comparable to Amazon L6) cost $10,000 to $15,000; leads start at $15,000 and are quoted per team. Monthly cost is headcount times rate; project cost is team size times duration times monthly rate.
- Back-end team (a FastAPI or Django service): 2 senior Python engineers. 2 × $10,000–$15,000 = $20,000–$30,000 per month.
- Data and API team: 1 tech lead, 2 senior Python engineers and 1 senior data engineer. $15,000+ for the lead plus 3 × $10,000–$15,000 = from about $45,000–$60,000 per month.
- ML product team: 1 lead, 2 senior Python engineers, 1 ML engineer, 1 MLOps engineer and 1 front-end engineer, quoted per team after scoping.
Quotes are scoped per team, and you get a plan, a price and the names of the people before you start.
Dedicated team or staff augmentation?
Pick a dedicated development team when you want a system delivered: a new service, a data platform, a modernization with a clear finish. The team owns the plan and the delivery and works alongside your engineers and data owners. If the system is an AI build, an AI pod team is the better shape.
Pick staff augmentation when your lead already owns the roadmap and needs senior capacity. Then you hire Python developers who work on your team, take tickets from your backlog and follow your conventions.
When Ryz isn't the right fit
If you need a one-off script or a few hours of help, a self-serve freelance marketplace is the faster route. If you need engineers covering European or Asian time zones, or follow-the-sun coverage for a global data platform, a global network fits better. And if you want a strategy engagement that ends in a recommendation deck rather than working software, a management consultancy is the better choice.
Related
FAQ
How much do Python development services cost?
Ryz engineers typically cost $7,000 to $15,000 per engineer per month: $7,000 to $10,000 for mid-level, $10,000 to $15,000 for senior, and $15,000 and up for leads, quoted per team. A two-engineer senior Python team is $20,000 to $30,000 per month. You get a scoped plan, a price and the names before work starts.
How soon can a Python team start?
After the scoping call we propose a team with named engineers. Most of the timeline depends on scope and your onboarding, such as access to repos, data and cloud accounts, and any security review. We don't promise a start date before we have seen those.
FastAPI or Django?
FastAPI suits API-first services, async workloads and model-serving endpoints. Django suits products that need an admin, user management and a mature ORM with migrations from day one. Many systems use both: Django for the core product and FastAPI for high-throughput or ML services next to it.
Can your Python team also build AI features?
Yes. Retrieval, LLM calls with evaluation and model-serving APIs are common Python work. When the AI system is the main deliverable, we staff it as an AI pod with an ML engineer and a tech lead; see LLM development services.
Can you modernize an old, untested Python codebase?
Yes. We start with characterization tests around the riskiest paths, add type hints at the boundaries, pin dependencies and upgrade the Python version in steps, so the system keeps running while it improves.
Questions we didn't answer? Email info@ryzlabs.com.